# Strise (full content) > Strise is the Autonomous FinCrime Department. It automates the full compliance lifecycle for financial institutions: onboarding, screening, monitoring, remediation, and risk scoring. Compliance teams act only when something changes. Everything else runs itself. --- ## Site ### Strise — Manual FinCrime work. Ends now. URL: https://strise.ai Stop repeating the same compliance checks on a schedule. Strise automates KYB, KYC, screening, and monitoring. Your analysts handle the judgement work. [PwC Norway selects Strise](https://strise.ai/blog/pwc-strise-aml-automation) #### Manual FinCrime work. Ends now. Strise automates the routine in onboarding, monitoring, ownership mapping, and alert triage — so analysts spend their time on the cases that actually matter. Play the film The team behind the podcast The Laundry - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - Leading payments company 11.5M entities screened. 80% of routine cases automated. Big Four firm 50% less time on complex cases. Top 5 advisory firm €530k saved in year one with 69% fewer analyst hours. ##### Every source. One living record. Strise cross-references registries, ownership, and AI-analysed documents into one living entity record, traceable to source and always current. The cross-source verification AMLR requires — already built in. ##### Route the routine. Surface the risk. Strise turns your risk model into a real-time scoring engine. Routine cases clear automatically with full audit trails, the evidence-based, risk-based scoring now required. Analysts are freed for the cases that need judgement. ##### Complex cases. Zero wasted time. Forms go to clients when data is needed. AI processes what comes back, and every decision your analyst makes feeds into the evolving entity record, so each review starts further ahead than the last. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## FINCRIME, AUTOMATED. JUDGEMENT, YOURS. ###### UBO discovery. All the way to the top of the chain. Strise maps the full ownership structure for every entity — directors, shareholders, and the beneficial owners behind them. However deep the chain goes, and across every source rather than one register, you get the complete picture without the manual research. [How UBO discovery works →](https://strise.ai/product/ubo-discovery) Ownership diagram showing Bribery Barnes owns 6% (politically exposed person), Corruption Carlson as ultimate beneficial owner with 64%, affecting ownership in Mammal Money Laundering Ltd., Octopus Offshore Holdings, Llama Tax Havens Inc., and Shady Sharks Ltd. ###### KYB onboarding with the case file already built. Every case arrives prepared. By the time an analyst opens it, Strise has already pulled company data, mapped ownership, run screening, and scored risk against your risk model. New businesses onboard in hours, not days. [How KYB onboarding works →](https://strise.ai/product/kyb-onboarding) Risk summary for Shady Sharks Ltd. showing high calculated risk and risk class, with next scheduled review on 17 Dec 2026; network risk includes 15 UBOs, 1 sanction, 2 PEPs, and 8 RCAs. ###### Screening where real hits reach your team. False positives don’t. Strise screens against PEP lists, sanctions, and adverse media, and filters false positives before they reach your team. Every alert that lands has a reason to be there. [How screening works →](https://strise.ai/product/screening-and-alert-triage) AI screening results for four individuals with categories: one likely true match, two likely false matches, one needing analyst review, and options to confirm match, review evidence, or defer review. ###### Ongoing Due Diligence triggered by change, not the calendar. Strise continuously monitors your portfolio and the whole network behind every customer — owners, directors, and connected entities. When something changes, a case opens. When nothing does, it closes itself. [How Ongoing Due Diligence works →](https://strise.ai/product/ongoing-due-diligence) List of four high risk companies with warnings, including Murky Tide Investments, Crocodile Capital Pty Ltd., Bear Market Manipulation Inc., and one unnamed entry. ##### Explore other products. - [KYC onboarding](https://strise.ai/product/kyc-onboarding) - [Identity verification](https://strise.ai/product/id-verification) - [Remediation](https://strise.ai/product/remediation) - [Risk scoring](https://strise.ai/product/risk-engine) - [Document analysis](https://strise.ai/product/document-analysis) ##### Secure, auditable, and explainable. Strise is SOC 2 certified and GDPR compliant, with auditable AI models and a full audit trail on every decision. Visit our security and trust centre - Prescient Security’s ISO/IEC 27001:2022 certification badge - AICPA SOC 2 compliance badge - GDPR compliance badge ##### Things we get asked. Answered. There are a lot of AI compliance tools right now. Why Strise? The honest answer is four things. ###### Intelligence that compounds Every decision your team makes feeds back into the living business record. Cleared false positives, validated ownership structures, and confirmed risk signals become context for the next case. The next analyst starts further ahead than the last one did. Your resolved cases sharpen your own intelligence layer, not a shared model. Your data stays yours. ###### A knowledge graph, not a database Most platforms check records. Strise maps relationships. Entities, directors, ownership structures, sanctions exposure, and adverse media exist in one continuously updated graph. Hidden connections surface automatically, because the system understands relationships, not just fields. ###### Built for European regulation from the start AMLR and the EU AI Act define how Strise works, not features added on top. Every automated decision is explainable, every AI outcome maps to a regulatory obligation, and human review is embedded in the architecture. When a regulator asks, the answer is already in the audit trail. ###### A decade inside the problem Strise was not built by observing compliance from the outside. A decade working alongside Tier-1 European financial institutions shaped everything about how the platform works. The Laundry, our podcast on financial crime compliance, reflects the same commitment, years of conversations with the practitioners, investigators, and regulators who face these problems every day. Does Strise replace our existing tools or work alongside them? Strise works alongside what you already have. It connects to your case management system, CRM, and data providers, pulling everything into one place rather than replacing it. What data sources does Strise pull from? Strise aggregates and resolves data from company registries, sanctions lists, PEP databases, adverse media sources, beneficial ownership records, and more. Data is continuously updated, so every case reflects the latest available information. Does Strise cover both business and individual customers? Yes. Strise handles the full compliance picture: KYB, KYC, UBO discovery, screening, ID verification, and ongoing monitoring, all in one solution. Most customers run all of it together. How is Strise priced? Pricing is based on usage, not headcount. We don’t charge per-seat fees, it scales with your portfolio volume. Contact us for a proposal tailored to your setup. Is Strise GDPR compliant? Yes. Strise is built to operate within GDPR and European data protection requirements. We can provide data processing agreements and full security documentation at any point during your evaluation. What happens when our policies or regulations change? Strise is configurable. When your risk appetite or regulatory requirements shift, your workflows update with them. Every decision is logged, so you always have a complete audit trail. How quickly can we go live? Most teams are 100% up and running within a few weeks. We start with a scoping session to understand your workflows and risk appetite, configure Strise to your policies, and run a pilot before full rollout. Our customer success team stays close throughout the process. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Cookie Policy –– Strise URL: https://strise.ai/cookie-policy Learn how Strise uses cookies to enhance your experience. Manage your preferences and understand our tracking technologies for analytics and marketing. #### Cookie Policy Last updated: June 2026 ##### What are Cookies? Cookies are small text files stored on your browser when you visit a site. Web beacons and pixel tags are tiny invisible images embedded in pages or emails that signal when content has been loaded or an action taken. The Strise website (the "Site") uses these technologies to improve your experience and target marketing effectively. ##### Strise Cookies Policy Please read this policy carefully to understand our practices regarding the use of cookies on the Site. Further details are available in our [Privacy Policy](https://strise.ai/privacy-policy). ##### Tracking Technologies Used: - Google Analytics: Analyzes data from Site visits to measure visitor behaviour, session data, and campaign performance. Learn more. - Google Tag Manager: Manages and deploys tracking scripts across our website for analytics and marketing efficiency. Learn more. - Facebook Pixel: Measures the effectiveness of our advertising on Facebook and Instagram by tracking visitor actions on our Site. Learn more. - LinkedIn Insight Tag: Tracks conversions and enables audience insights from LinkedIn campaigns. Learn more. - Apollo.io: Identifies the organisation you are browsing from based on your IP address, and connects that activity to our sales and marketing outreach. Learn more. - Microsoft Clarity: Records visitor interactions such as clicks, scrolls, and page navigation to help us understand how our Site is used and improve the user experience. Learn more. - HubSpot: Tracks visitor sessions and form submissions to support our marketing and sales activities, including contact deduplication and lead attribution. Learn more. - Spotify: Enables embedded audio content from Spotify on our Site and registers information on user interaction with that content. Learn more. ##### Functionality Allowed by Our Cookies: - Remember your login details (app.strise.ai ). - Ensure consistent website appearance. - Enable social sharing and interactions. - Provide personalized marketing experiences. - Optimize site performance and user engagement. ##### Update or Withdraw Your Consent at Any Time You have the right to withdraw your consent for cookie usage at any time. You can update or withdraw your consent at any time by clicking the "Consent Preferences" button in the bottom left corner of our website. Note that blocking certain cookies may affect the functionality of the Site. ##### Use of Necessary Cookies Our Site uses certain cookies that are essential for the operation and security of our website; these are known as necessary cookies. Necessary cookies help make the Site usable by enabling basic functions like page navigation and access to secure areas of the Site. The use of these cookies is not subject to consent according to data protection laws. Therefore, you cannot withdraw consent for these cookies as they are critical to the functionality and integrity of the Site. ##### Updates to This Policy This Cookie Policy may be updated periodically to reflect changes in our cookie usage or regulatory changes. Please revisit this policy regularly to stay informed about our use of cookies. ##### Questions? For questions about our use of cookies, contact us at privacy@strise.ai . ##### Cookie list: --- ### Privacy Policy –– Strise URL: https://strise.ai/privacy-policy Learn how Strise AS processes your personal data, our sources of information, and your rights under GDPR and Norwegian data protection regulations. #### Privacy policy Policy owner: Magnus Hagem Effective Date: 2025-08-27 This privacy policy (the "Privacy Policy") explains how Strise AS ("Strise", "we" or "us") process your personal data, our sources of information, and what rights you have pursuant to the prevailing data privacy regulations, including the Norwegian Personal Data Act (2018) that incorporates the EU General Data Protection Regulation (GDPR). ##### 1. Data controller Strise acts as the data controller for any processing of personal data in connection with providing you with information and offering you services on our website www.strise.ai and through the Strise Software (the "Services"), as well as personal data we process for the purposes listed below. Our contact information is: Strise AS Rådhusgata 9, 0151 Oslo, Norway ‍privacy@strise.ai ##### 2. Purpose and legal basis for the processing of personal data In general, Strise collects and processes personal data to be able to provide you with relevant information related to the Services, including as required by legal obligations. More specifically, we collect and process personal data on the basis of your explicit consent (ref. GDPR art. 6(1) letter a), to uphold our legitimate interest in fulfilling our contractual obligations with the company you act on behalf of, to uphold our legitimate interest in providing you with the best experience possible in relation to our webpage information and provided Services, to uphold our legitimate interest in providing the Strise Software to our customers, and to uphold our legitimate interest in processing any legal claims (ref. GDPR art. 6(1) letter f), as well as for compliance with our legal obligations (ref. GDPR art. 6(1) letter c). We only process your personal data to the extent that it is necessary for the performance of our Services or any other contractual obligations, including as required by legal obligations, for pursuing our legitimate interests, or to the extent you have consented to any processing. Below we have listed all the specific ways we process personal data and what categories of personal data we typically process, as well as the purpose and legal bases for such processing: a) Website: When you visit our website and use our digital services, we collect certain information from your computer or internet connection. This may include your IP-address, date and time of your visit, duration of your visit, content of your request (such as the specific page you visit), the website you were referred from, which pages you visit on our website, your internet service provider, your browser type and version, as well as your operating system. We collect this information to uphold our legitimate interests in being able to display our website to you, measuring and improving the performance of our digital channels, as well as marketing our Services (ref. GDPR art. 6(1) letter f). Please see further information about our use of cookies in the Cookie Policy on our website, including what cookies that will require your explicit consent (ref. GDPR art. 6(1) letter a). b) Use of Strise Software: When you register as an individual user for your company in the Strise Software, we may process your personal data (e.g., name, e-mail, company, IP address, and usage necessary to deliver the Service). We process this data to deliver the agreed Service to the customer you represent. The legal basis is our legitimate interest in performing our contract with your company (GDPR art. 6(1)(f)). We also collect certain interaction and usage data (such as clicks, navigation patterns, and feature consumption) to improve, secure, and further develop our Services. For this processing, Strise acts as the data controller. The legal basis is our legitimate interest in improving and developing our Services (GDPR art. 6(1)(f)). When we enter into a customer agreement with a company that you represent, we will enter into a data processing agreement with the customer. If you act as a contact person for your company, we may process your personal data as an individual data controller on the basis of our legitimate interest in entering into and fulfilling a contract with your company (ref. GDPR art. 6(1) letter f). We may further store your personal data as a contact person, such as your name, work email, company and role, on the basis of our legal obligations. More specifically, we may need to store the customer contract with your company as well as any invoices on the basis of our bookkeeping obligations in the Norwegian Bookkeeping Act Section 13 (ref. GDPR art. 6(1) letter c), which requires Strise as a company to store bookkeeping information for bookkeeping purposes up to 3,5 or 5 years. c) Personal data in the Strise Software: The Strise Software is an artificial intelligence driven decision support service which collects and analyses data to provide information about companies for i.e. prospecting, sales, anti-money laundering and due diligence purposes. The Software targets business- related information, but a limited amount of personal data concerning physical persons may be processed in the Software. This is due to the person's public role in a company, such as CEO, board member, steering committee member, beneficial owner, or minority shareholder. This may include the following categories of personal data: ‍ - Personal and contact information, such as name, date of birth, gender, country, zip code and address, phone, e-mail, spouse; - Business-related information such as title, role, position, company/legal entity, business history, income, connections; and - Other information such as news stories where the person is mentioned, legal proceedings where the person is publicly announced, PEP/sanctions status of the person. ‍ The Software may further process personal data through the use of flags/flagging in the Software. By this we mean that the Software may indicate to the user that a physical person is connected to a predetermined factor by showing a flag. A physical person may be flagged in the Software in three different scenarios: i) if they have a role in a company at the time a flagged event occurs with the company, i.e. the company is bankrupt, ii) if a person is on a Politically Exposed Person (PEP) list or a sanctions list, or iii) if the person is linked to hits from Adverse Media Screening (AMS), which entails a combination of name match and relevant, predetermined AMS terms (like tax evasion, fraud, litigation etc.), as well as additional factors to classify the connection between the two. The personal data is collected from sources within the public domain, such as news, public records, social media posts, company websites, press releases or otherwise disclosed. Strise also uses third party providers to collect some of the personal data. The Software only provides the customer with a functionality to shorten and streamline the access to any relevant company related information. Strise uses intelligent systems to filter the information before providing it to its customers. All personal data is updated when the source of information is updated. Strise processes personal data in the Software for the purpose of providing the services within the Software to Strise's customers, hereunder providing information about legal entities in the system that is as complete as possible, to update and improve the Software and to offer support and functionality. The legal basis for the processing of personal data in the Software is our legitimate interest in providing the Software to our customers for the abovementioned purposes (ref. GDPR art. 6(1) letter f). Please note that you have the right to object to our processing of your personal data in the Strise Software, ref. GDPR art. 21(1). In such instances, Strise may only continue to process your personal data if we can demonstrate compelling legitimate grounds for the processing which override your interests, rights and freedoms, or for the establishment, exercise or defence of legal claims. If the personal data in the Software reveals special categories of personal data pursuant to GDPR article 9 no. 1, such as information regarding sexual orientation, health data and political opinions or religious beliefs, the legal basis for the processing is GDPR art. 9(2) letter e – the processing relates to personal data which are manifestly made public by the data subject. The personal data will not be used for any other purposes than for providing the Software to Strise's customers. d) Contacting Strise: When you use our contact form on the website to talk to sales or when you contact us with questions or requests by other means, you may provide us with information about yourself or your company. Upon doing so, we collect the information provided by you, such as; name, e-mail, phone number, company information and role, as well as any other information that you provide us with. We collect this information to be able to carry out our contract with the company you act on behalf of (ref. GDPR art. 6(1) letter f) or to uphold our legitimate interest in replying to your requests or questions (ref. GDPR art. 6(1) letter f). e) Recordings of meetings for sales, product development and support: We may process personal data, including video, audio and transcriptions from recorded calls, to enhance our prospecting, sales, customer support, product development and onboarding activities. These recordings and transcriptions allow us to improve service quality, ensure effective onboarding, and support our internal training and product development. Each participant will be informed in advance and can choose not to consent to the recording for the meeting. The personal data processed may include names, contact information, job titles, image, and likeness of participants, and any other information shared during recorded calls that identifies or relates to participants. We process information to support our legitimate interest in enhancing sales processes, customer support, onboarding, and business and product development efforts, including improved interactions with both current and prospective customers, and our service in general (GDPR Art. 6(1)(f)). f) Customer stories on our website: We may ask our customers if they are willing to share their customer stories on our website in order to advertise and inform about our Services. If you represent your company in one of these stories on our website, we may process personal data about you, such as your name, role, company and all other personal data you choose to share in the customer story. The legal basis for processing your personal data through customer stories is our legitimate interest in promoting our Services and sharing information about our customers to other potential customers (ref. GDPR art. 6(1) letter f). g) Newsletter and other information: If you subscribe to our newsletter, we collect your name, e-mail and role. If you are an existing customer or user of the Strise Software, we may also send you necessary information even though you do not expressly subscribe to our newsletter. The legal basis for sending newsletters is your explicit consent (ref. GDPR art. 6(1) letter a). The legal basis for sending e-mails to our existing customer relations or users is to uphold our legitimate interest in following up our customers by providing relevant news and relevant information about our Services (ref. GDPR art. 6(1) letter f, see also the Norwegian Marketing Act art. 15 (3)). Anyone receiving the information items above can easily opt out using the link included in our e-mails. h) Applying for a position with us: You are welcome to apply for a position with us, either by contacting us or by applying through our third-party provider Amby AS. If you decide to apply for a position with us, we may process the following categories of personal data: - Basic information: e.g. name, date of birth and nationality; - Contact information; e.g. your address, e-mail and phone number; - Qualifications: e.g. your CV, application, diplomas and transcripts of records, language knowledge, courses, certifications, tests done in connection with the recruitment, and any other information you provide us with in your application or interviews with us; - Background information: e.g. information provided to us by your references, which we either collect through official sources or through references you provide us with; and - Our assessments: e.g. assessments and comments we make in relation to our internal assessment of your application. Strise will not ask candidates to share special categories of personal data, however, Strise cannot rule out that such personal data will not be processed in case the candidate chooses to share this information with Strise. The legal basis for processing personal data for recruitment purposes is to enter into a potential employment contract with you (ref. GDPR art. 6(1) letter b). If the application includes special categories of personal data, the legal basis is the employer's obligations in the field of employment law (ref. GDPR art. 9(2) letter b). Further, Strise may process personal data during and after the recruitment process on the basis of our legitimate interests in performing our own research to find the right candidate for the position to be filled (ref. GDPR art. 6(1) letter f). Strise may also keep job applications after the recruitment process is finished on the basis of your explicit consent (ref. GDPR art. 6(1) letter a and GDPR art. 9(2) letter a), if you wish for us to keep the application in the event of future job openings. i) Social Media: When you visit or interact with our social media pages (e.g., LinkedIn, Facebook, TIkTok, Instagram, and YouTube), the platform providers process your personal data according to their own privacy policies. If you like, comment, share, or otherwise engage with our content, some personal data (such as your name, profile picture, job title, company name, and comments) may become visible to us. We may use this publicly available professional information to respond to your engagement, track trends, and—where relevant—to contact you regarding Strise’s products and services if your professional role suggests a legitimate business interest. We may also collect and store publicly available professional data from sources such as LinkedIn (e.g., name, job title, company, and business contact details) in our CRM system to facilitate B2B marketing and outreach. If we contact you, we will do so in a professional context and provide you with the opportunity to opt out of further communications. We rely on our legitimate interest (GDPR Art. 6(1)(f)) as a legal basis for this processing, ensuring that our outreach is relevant, non-intrusive, and limited to business-related interactions. We will only process personal data when users engage with Strise in a professional context. We retain this data only as long as necessary to fulfill the purpose for which it was collected. This means we periodically review stored data and delete it when it is no longer relevant for outreach or engagement. If you opt out of communications, we will register that you have opted out. You may request access to, correction of, or deletion of your data at any time by contacting us at privacy@strise.ai . For more information on how social media platforms process your personal data, please refer to their respective privacy policies: ‍ - https://www.facebook.com/privacy/policy/ - https://www.linkedin.com/legal/privacy-policy - https://privacycenter.instagram.com/policy/ - https://www.tiktok.com/legal/page/us/privacy-policy/en https://policies.google.com/privacy (Youtube) ##### 3. Sources of personal data Within the Software, Strise collects personal data primarily from open and publicly accessible sources regarding legal entities. All the data we collect is within the public domain or has been made public by the legal entities or the data subject themself, such as news, public records, social media posts, company websites, press releases or otherwise disclosed. We merely use intelligent systems to filter this information and provide this to our customers through our Services. All personal data is updated when the source of information is updated. For other processing activities, the personal data is generally collected from the data subject. Our customers may, however, share personal data with Strise relating to their individual users of the Software. ##### 4. Third party providers and recipients of personal data We will not share your personal data with others unless you either give us your consent to do so (ref. GDRP art. 6(1) letter a), or if we have legal basis to share your data, e.g. if we are required by law to disclose your personal data (ref. GDPR art. 6(1) letter c), or it can be justified on the basis of our legitimate interest in doing so (ref. GDPR art. 6(1) letter f). Our customers and users of the Software have access to some or all of the personal data in relation to the legal entities they search for. Strise's customers processes personal data in the Software for their own purposes and legal bases, without any joint determination with Strise Personal data may further be disclosed to public authorities if this is necessary to fulfill a legal obligation. Strise is currently using third parties in providing the Services, including third parties that provide cloud infrastructure, software bug reporting, and account administration. When we use third party sub- contractors or service providers to provide our Services, we will take appropriate legal precautions and corresponding technical and organizational measures in order to ensure that your personal data is protected in accordance with applicable data protection law. Our service providers may be based in locations all over the world. This means that your personal data may be transferred outside of the EU/EEA. If that is the case, we will implement appropriate security measures in accordance with chapter five of the GDPR, in order to sufficiently protect your personal data, such as agreements including EU standard contractual clauses (SCC). A copy of the list of third parties used by Strise may be available upon request by emailing at privacy@strise.ai . ##### 5. How we protect your personal data Both our data processors and we have implemented appropriate technical and organizational measures to ensure a sufficient level of security when processing your personal data and to prevent loss or unlawful processing (ref. GDPR art. 32). Such measures are, for example, internal routines, data processing agreements and IT-security procedures to verify access rights. More specifically, the employees of Strise have bound themselves to comply with professional secrecy and concealment regarding the information they receive during the processing of personal data. Privacy and security guidelines have been communicated to employees and privacy safeguards are strictly enforced within Strise. We will also carry out data protection impact assessments when it is likely that processing your data may result in a high risk with respect to your rights and freedoms in relation to your personal data. ##### 6. Retention of personal data Personal data will not be stored for longer than needed for the purposes mentioned in this Privacy Policy. In relation to personal data in the Software, this entails that personal data will be stored in the database only as long as and only to the extent that it is necessary to provide timely and accurate information to customer decision-makers as part of providing the Software. The database is automatically updated on a regular basis, and personal data will not be stored beyond a reasonable time after it has been removed from the original source. For our customers and users of the Software, this means that when our customer relationship with your company is terminated, we will erase your personal data as soon as our purpose with processing such data is no longer relevant, unless we are compelled by law or have other legal basis to store this data any longer. ##### 7. Rights of the data subject You have several rights under the applicable data protection regulations. We have provided a list of the rights you can exercise in your relationship with us as a data controller below. If you wish to exercise your rights, please contact us and we will respond to your inquiry as soon as possible, but in general no later than a month after the receipt of your enquiry. - Access: You have a general right of access to the personal data we have registered about you. - Rectification and erasure: You have a general right to request that we should rectify any incorrect personal data about you and erase personal data about you. Please note that personal data that is essential to the customer relationship with us cannot be deleted, unless you also explicitly request termination of the customer relationship with us. - Restriction: You have a general right to ask us to stop (“freeze”) the processing of your personal data, e.g. where you are of the opinion that we process personal data about you illegally and you do not wish us to erase these data pursuant to our routines for such erasure until the matter has been clarified. - Data portability: You have a general right to request transfer of your personal data in a common, machine-readable format. - Objection: You have a general right to object to our processing of personal data about you if this is justified by special circumstances on your part. - Right to appeal: If you do not agree with the way in which we process your personal data, you may submit an appeal to the Norwegian Data Protection Authority (in Nw: Datatilsynet). We ask that you contact us beforehand, so that we may clarify any misunderstandings. Withdraw your consent: If our processing of personal data is based on your consent, you have the right to withdraw your consent at any time, without affecting the lawfulness of processing based on consent before its withdrawal. ##### 8. Changes to this Privacy Policy We evaluate this Privacy Policy from time to time in light of changing business practices, technology and legal requirements. The most up-to-date version of our Privacy Policy is available on our website Version Date Cell Author Approved by 1.0 2023-03-15 Initial version Patrick Skjennum Sigve Søråsen 1.1 2024-05-14 Amendments to section 2 “Purpose and legal basis for the processing of personal data”, c) “Personal data in the Strise Software”. Magnus Hagem Patrick Skjennum 1.2 2024-11-14 Added a new section 2 e) for “Recordings of meetings for sales and support” Trine Hammervold (DPO) Magnus Hagem (Head of Legal) 1.3 2025-03-07 Amendments to section 2: Added product development as a purpose under section e), and amended section i) “Social media Trine Hammervold (DPO) Magnus Hagem (Head of Legal) 1.4 2025-08-27 Amendments to section 2 b) Magnus Hagem (Head of Legal) Trine Hammervold (DPO) --- ### Book a Meeting –– Strise URL: https://strise.ai/book-a-meeting-with-strise Talk to a Strise product expert. Get a walkthrough of AML automation solutions, API integration insights, and pricing estimates for your compliance needs. #### Schedule a demo ##### What to expect? - Walkthrough of your use cases and project goals - Overview of products and plans - Insight into API and integration capabilities - Pricing estimates based on volumes and product needs - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay ##### Enter details Business email* First name* Last name* Company name* What use cases are you looking to solve?* How did you hear about Strise?* Website By proceeding, you confirm that you have read and agree to [Strise’s privacy policy.](https://strise.ai/privacy-policy) Book a demo --- ### ChangeLog –– Strise URL: https://strise.ai/changelog Every release, fix, and improvement we ship, in one place. Updated as we go. ##### Strise changelog Every release, fix, and improvement we ship, in one place. Updated as we go. ###### Week 32 · 2026 Aug 3 Aug 9 Screening reaches more sanctioned companies, alert settings get a full draft-mode preview, and ownership charts stay legible on widely held companies. Adverse media arrives in the AFCD case report, and admins get a portfolio-size limit. See everything that shipped Screening Catch more sanctioned companies, and work adverse media in the AFCD case report. - Sanctions screening now also matches company names that keep their legal form, such as AS or SARL, so more sanctioned organisations are caught. You may see new hits on the next run. - The assessment AI summary includes adverse-media hits an analyst has confirmed relevant. - The AFCD case report gains an adverse-media view: a subject-grouped queue with a deep dive, the full article body, and localised copy. Monitoring Draft-mode preview now covers PEP, Sanctions and Relations alerts. - Draft-mode preview now covers PEP, Sanctions and Relations alert settings, so you can see how many alerts your changes would produce before you apply them. Ownership & Structure Sub-threshold beneficial owners collapse into a searchable Other shareholders node. - Widely held companies stay legible: beneficial owners below the display threshold collapse into the "Other shareholders" node, with a labelled, searchable list, instead of one node each. Portfolio Health Set a maximum number of portfolio companies per customer, with usage shown. - Admins can set a maximum number of portfolio companies per customer, and see usage against that limit. Roles & Permissions Delete actions only appear when your permissions allow them. - Delete actions for tags, custom entities and adverse-media dispositions respect your permissions, so you only see actions you can complete. - The admin role editor adds a role-owner picker and greys out permissions too privileged for the role you're editing. Platform & UX Strise-generated events read in the viewer's language, including historical ones. - Strise-generated events read in your own language, including historical events. Platform & UX Reliability and polish across IDV, notifications, portfolio and screening. - Identity verification lists no longer break for the whole team when the person who started a verification has left. - Notifications load once per open, surface failures as errors, and no longer break when an author is unreachable. - Entity-page banners scroll away with the page, and the Location map no longer steals page scroll. - The portfolio horizontal scrollbar is visible again. - Four-eyes review shows the reviewer's comment and verdict to the assessment author. - The assessment AI summary no longer labels suggested-false hits as awaiting disposition. - Registry-check document actions are hidden when not enabled, and an unknown document id returns "not found" rather than "forbidden". - Inviting a member defaults to team member, with no role required. - Service and API-client accounts are excluded from the assignee picker. - "Create private person" registry search is limited to Norway, with a note pointing to manual entry. - The sanction match-details tooltip shows the weight divisor, so the figure matches the percentage. ###### Week 31 · 2026 Jul 27 Aug 2 The notification panel becomes a full side sheet with filters, grouping and an unread count, and a cross-user feed issue is closed. The Screening Matrix gains a Network group, and assessment mode works without four-eyes review. See everything that shipped Platform & UX A side-sheet notifications panel with filters, grouping, an unread count, and a security fix. - The notification panel is a right-side sheet matching the full page, with date-grouped rows, the connected company on each row, a numeric unread badge, a "mark all as read" button, a filter bar, and a shared "unread only" toggle that persists. - Long notification feeds scroll faster. - The notifications feed and unread count are scoped to you, closing an issue where a teammate could read another user's feed. Screening Network-screened entities appear in a dedicated group, each naming its connection. - The Screening Matrix honours the Risk Summary "network risk" toggle: network-screened entities appear in a Network group, each row naming the company it connects through. Monitoring Preview alert volume for company-information settings before applying. - Alert settings draft mode covers Company information alerts: preview the projected alert volume, active against draft, before you apply, with a per-field breakdown and an impact tile. Platform & UX Assessment workflow, ownership chart, comments and invites. - Assessment mode works without four-eyes review: teams with four-eyes off can complete an assessment with no reviewers. - The entity status selector is locked while an assessment is out for four-eyes review. - Risk assessment cards show a dotted border while pending review. - The "network risk" tooltip reflects your actual screening scope. - The ownership chart's registered-UBO banner no longer squashes nodes, and the strip is renamed "Beneficial owners without ownership". - Section comments stay anchored to their section, and @-mentioning a teammate inserts correctly. - Inviting a user to another team keeps their language preference. - The create-private-person modal keeps the name, national ID, country and birth date you typed when you switch to manual entry. ###### Week 30 · 2026 Jul 20 Jul 26 Customer-Facing Forms gain an Inconclusive outcome for when document verification needs redoing. Screening draft-mode previews open up to customers with affected-entity drill-downs, the ownership chart gets a safer reset, and search now covers Greenland and the Faroe Islands. See everything that shipped Identity Verification Forms can finish as Inconclusive when document verification needs redoing. - Customer-Facing Forms can finish with an Inconclusive outcome when document verification needs redoing, so a form no longer reads as a full success. A reason explains what to redo. - Find these on a new Inconclusive tab on the Data Collection page and a matching portfolio filter, with in-app and email notifications you can opt in or out of. - The Inconclusive outcome is available on the Connect API and the form.status.changed webhook. Screening Paginated affected-entity drill-downs across sanction, PEP and geo/structural, visible to customers. - Screening settings draft mode adds paginated affected-entity drill-downs across sanction, PEP and geo/structural dimensions, so you see exactly which entities a draft change would affect. - Draft-mode preview metrics are available to anyone who can draft screening settings. Ownership & Structure Choose exactly which chart edits to undo instead of wiping everything. - Reset is a selection screen: shareholder and beneficial-owner edits are listed by entity, so you choose exactly which to undo instead of wiping the chart in one click. Platform & UX New search coverage, screening layout, localisation and performance. - Search returns Greenland and Faroe Islands companies and persons via Denmark's CVR registry, in the app and the Connect API. - Dow Jones sanction preview metrics group into collapsible cards by issuing authority (OFAC, UN, CFSP and so on). - Ownership chart completeness warnings come from the server, so the banner figure matches the highlighted nodes. - Backend-rendered text follows your active UI language. - Private-person creation is much faster on large teams. - Connect API company queries no longer error for entities with very large alias lists. - The reassess form shows a proper loading state. ###### Week 29 · 2026 Jul 13 Jul 19 Screening gains manageable manual verifications and deeper draft-mode drill-downs, Portfolio Health v2 moves to beta, and a privilege-escalation path in role editing is closed. Companies House document requests work again. See everything that shipped Screening Manageable manual verifications, deeper draft-mode drill-downs, and an evidence-backed PEP deep dive. - Manage manual PEP and sanction verifications as rows in the disposition modal, each with a Manual badge and a Remove action. Add a manual verification at any time, even after screening hits appear. - Sanctions settings draft mode adds affected-entity drill-downs across all five sections (source, Dow Jones list, roles, matching, scoring), from one query, with nationality flags and a score-difference histogram. - The AFCD PEP deep dive renders a resolved evidence graph with verified citations, "also cited" badges and direct source links. Portfolio Health Portfolio Health v2 is now in beta. - Portfolio Health v2 moves to beta, with help text explaining it switches Home to the new experience. Roles & Permissions The admin role editor enforces node-level assignment floors. - The admin role editor enforces per-permission node-level assignment floors, so a system-scoped permission cannot be placed into a customer-assignable role. Platform & UX Registry document fixes, ownership-chart accuracy, and admin clarity. - Request documents from UK Companies House again. - The "Compare with local registries" action is back in the Documents section. - The ownership control card no longer warns that shares don't add up to 100% when they do, and the self-ownership edge highlights correctly. - Resetting an ownership chart clears orphaned beneficial-owner edits that hold no shares. - Review PDF ownership nodes show each owner's country flag and name. - Adding an assignee no longer errors on teams without monitoring enabled. - Admin permission-denied messages name the specific missing permission. - Member role pickers preselect the current role. - The "PEP in high-risk jurisdiction" risk factor surfaces for teams that configured risk scores without flagging a highlighted bucket. - Viewing an entity assigned to someone you can't see no longer blocks the whole page. ###### Week 28 Jul 6 Jul 12 Ownership charts gain person subsidiary trees and clearer control labelling. Assessments open in a split-screen drawer, so you can read the entity and work the assessment side by side, and you can @-mention teammates in section comments. Private-person portfolios gain CSV upload and export. See everything that shipped Ownership & Structure Person nodes expand into subsidiary trees, with clearer control labelling and share totals. - Person nodes expand into a subsidiary tree in the ownership chart and fullscreen, replacing the old fan-out layout. - Beneficial owners with no ownership path are surfaced in the chart, anchored to their originating company. - Register-reported ownership is labelled on the control card. - The "doesn't add up to 100%" warning shows the actual share total. Platform & UX A split-screen assessment drawer, a merged risk card, and @-mentions in section comments. - Assessments open in a resizable split-screen drawer that pushes the entity page aside, so you read the entity and work the assessment together. - Risk class and outcome are combined into a single card in the assessment panel. - @-mention teammates in section comments, and they get notified. - Add comments on the Screening Matrix card. Portfolio Health Upload private persons by CSV, optionally add them to monitoring, and export the portfolio. - Upload private persons to your portfolio by CSV, and optionally add them to monitoring in the same step. - Download a portfolio export for private persons. Screening A redesigned metrics draft preview, affected-hit lists on sanction panels, and manual edits feeding risk. - Sanction metric panels list the affected hits under each role and match-percentage band, so you can see exactly which entities fall where. - The screening-metrics draft preview is redesigned with clearer added and removed visuals. - Manual industry and legal-form edits on global entities feed into the calculated risk score. Monitoring Cut through a busy monitoring feed with suggested filters, now in the Strise app. - Suggested alert filters are available in the Strise app, a quicker way to cut through a busy monitoring feed. Roles & Permissions URL-driven tabs, clickable members, per-row revoke, and node-scoped holders. - Roles and Permissions tabs are URL-driven, so deep links work, and assignment pickers are scoped to valid targets. - Role assignments have clickable member rows and per-row role revoke. - The Assignments tab shows role holders scoped by node, including customer and system nodes. Connect API The entity status v2 field is available on Connect API v2. - The entity status v2 field is available on Connect API v2. Platform & UX Ownership chart polish, private-person screening, and admin tweaks. - Editing a private person re-triggers PEP and sanctions screening, so results are never stale. - Kyckr company lookup falls back to searching by name when an org-number search finds nothing, auto-picking only on a single unambiguous match. - Screening metrics keep "self" hits visible even when relation-kind toggles are off. - Draft screening metrics show their data freshness, with a caveat when settings change. - Ownership chart nodes keep share ranges on one line and order rows by share, grouped shareholders last. - Ownership chart popovers close on click-away. - The team-user picker keeps results visible while you type. - The invite wizard skips Customer and Team steps when they are already filled in. - Customer status expiry can be set for any status. ###### Week 27 Jun 29 Jul 5 This week sharpens screening transparency and the Connect API. Screening hits show the matched name and term behind them, and the Connect API returns alternative beneficial owners for every team, adds a per-request language header, and lets you fetch matches that still need a disposition. See everything that shipped Screening Screening hits show the matched name and term, and settings previews gain scoring histograms. - Screening alerts show the matched name and term behind each hit, and the same detail is available on the Connect API. - The PEP and Sanctions settings previews gain scoring-distribution histograms, splitting each bucket into alerts and below-threshold potentials. Connect API Alternative beneficial owners for every team, a per-request language header, and matches needing disposition. - The relationships field returns alternative beneficial owners (by voting power, or ownership including family) for every team, matching the app. - Set a per-request language with the new X-Strise-Language header. - Fetch PEP and sanction matches that still need a disposition. Platform & UX Teams are grouped by customer for people who work across several, plus data collection page polish. - The team switcher groups teams under their customer for people who work across several customers, and shows the current one. - The Data Collection page gets a clearer header, underline tabs and a filter. Platform & UX Registry error clarity, ownership edit persistence, forms polish and data-source updates. - Clearer errors when the Norwegian population register (FREG) is unavailable or returns bad data, instead of a misleading message. - Beneficial owners you remove from registry data stay removed across refreshes, and re-sync correctly after a reset. - Entity resolution keeps valid findings when one is malformed, and resolves org numbers by direct lookup when a search misses. - BRREG roles include the deregistered (avregistrert) field. - The EU high-risk third-country list is updated to the January 2026 version. - Forms show entries created after the page loaded, and there is a new form-invite email template. - Sanction screening usage is attributable to its source, such as Dow Jones or Pliance. - The alert filter dimensions are reordered for quicker scanning. - A grouped portfolio section with rows no longer hides behind a stale count. ###### Week 26 Jun 22 Jun 28 Identity verification now covers private persons, and a new Data Collection page brings verification and forms together. Screening separates PEP and Sanction review states, the ownership section gets a redesign, and the Connect API gains status mutations and richer form data. Monitoring alerts get sharper and the metrics preview truer to your settings. See everything that shipped Identity Verification Run identity verification on private persons, with documents and events surfaced. - Run identity verification on private persons. - Manual verification documents appear on business persons and link back to the request that collected them. - Identity verification events and values appear in the entity change log. Screening PEP and Sanction review states on separate axes, adverse media on its own, and a custom-verification audit trail. - Screening shows PEP and Sanction review states on separate axes, with adverse media as its own section. - Custom verification records an audit trail, with a link to it in the custom verification dialog. - Dow Jones lists are marked as premium under PEP sources. Ownership & Structure A new ownership toolbar, banner and fullscreen view, plus a polished chart import. - Work in a redesigned ownership section with a new toolbar, banner and fullscreen view. - Import an ownership chart with a polished draft preview, clearer matched states and node dragging. - The chart's control view shows voting power from computed beneficial-ownership values. Portfolio Health Consistent drilldown navigation and a private-person risk distribution. - Drill down consistently across Portfolio Health. - See a private-person risk distribution alongside companies. Monitoring Monitor from the backlog modal, preview screening that matches your settings, and catch more status changes. - Add an entity to monitoring straight from the backlog modal. - The screening metrics preview reflects more of your settings: shareholder and subsidiary thresholds, excluded role titles, and excluded beneficial-ownership reasons, so the preview matches what gets screened. - Alert filter suggestions are smarter, offering composite suggestions and single-facet ones once a filter is applied. - Screening change checks include unchanged hits, and PEP screening re-runs when an entity's countries change, so status changes are not missed. Roles & Permissions Read-only users can see high-risk industry codes, and some views drop the admin requirement. - Read-only and non-manager users can view the high-risk industry code panel. - Migration requests and screening-usage views are available without admin access. Connect API Status mutations, richer form queries, source metadata on roles, and graph completeness warnings. - New companyUpdateStatus and privatePersonUpdateStatus mutations set an entity's portfolio status directly, with an addToMonitoring flag. - Query forms with formProcesses, including includePendingForms and orderId filters, and fetch a completed form's documents with a documents field on FormProcess. - sourceMetadata, with origin source and timestamps, is available on the Role type, matching Ownership and BeneficialOwner. - Ownership-chart completeness warnings are exposed on the ShareholderGraph type. - form.status.changed webhook values align with the DocumentStatus enum. Platform & UX A Data Collection page for verification and forms, plus a Forms section on the entity page. - A new Data Collection page brings identity verification and forms together under tabs. - A customer-facing Forms section appears on the entity page. - The portfolio action bar is consolidated into Filter and Display popovers. - When a registry check has no local data, the card suggests requesting official documents instead of dead-ending. Platform & UX Exports, search, ownership-chart fixes and data-source labelling. - Export private-person portfolios, with high-risk and custom-risk-field columns added to the company export. - Fuzzy matching is back across entity search views. - Private-person reviews restore your saved progress, with Continue review and Minimise parity. - Inputs have a built-in clear button. - The small-shareholder (under 5%) grouping box is back in the ownership chart. - The ownership chart handles companies with circular ownership without crashing. - User-edited source rows show a Last edited date. - Norwegian financial statements are labelled by fiscal year-end. - Registry outages are surfaced instead of creating name-less monitored persons. ###### Week 25 Jun 15 Jun 21 Identity verification takes centre stage this week. You can record manual checks, start verification from a person's page, and see at a glance who is verified. Monitoring alerts get cleaner, company life-status gets clearer, and the Connect API now tells you when registry data actually changed. See everything that shipped Identity Verification Record manual checks, run verification from a person's page, and spot who's verified. - Record a manual identity verification for someone checked outside the digital flow. The method, legal name and date of birth fill in for you. - Start verification straight from a business person's page, and see how each person connects to the company by role or ownership. - Track verification across a whole team from a new overview page. - Spot verified people by an ID-verified badge on their name. - Every verification shows a clear status of pending, approved or declined, with the reason when it is declined. Monitoring Filter unowned alerts, read company life-status clearly, and get role alerts that fire only on real changes. - Filter monitoring alerts to companies with no assigned case worker, so unowned alerts still get handled. - A company counts as inactive only when it is terminally dissolved. Live proceedings such as bankruptcy now show a clear status of active, notice or inactive. - Role change alerts fire only on genuine changes, after reconciling refreshed start dates. Connect API Source metadata separates registry-change time from fetch time, plus machine-readable error codes. - Source metadata separates when a registry says its data changed from when Strise last fetched it. You see both in the app and the Connect API. - GraphQL responses now carry machine-readable error codes. Platform & UX Your team's settings changelog is now available to every team. - Your team's settings changelog is now available to every team, so you can see what changed and when. - The app updates in place rather than forcing a full reload. Platform & UX Quality-of-life updates across reviews, screening accuracy, ownership and webhooks. - Review PDFs now include assessment reviews. - Review PDFs render private-person addresses correctly and show custom risk field names. - Portfolio Health custom risk fields show which entity type they apply to, with country flags and private-person risk factors. - Scored risk values with no colour now show as risky on the entity page and in review. - RCA screening keeps relevant matches for smaller shareholders who share connections with the company. - The ownership chart tidies subsidiary and small-shareholder grouping, with labels that fit. - Webhook settings now link to the webhook documentation. ###### Week 24 Jun 8 Jun 14 Identity verification gets a big upgrade this week. Run it from the company page, send links by email, and let a finished Signicat check approve automatically. Ownership structures open as a searchable subsidiary tree, screening results read more clearly, and the Connect API gains annual financial statements. See everything that shipped Identity Verification Run, track and complete identity checks from the company page, with links sent by email. - Run identity verification from the company page. People sit in pending, failed or completed buckets, and a details drawer lets you decline, withdraw or regenerate a request. - Verifications complete on their own once Signicat finishes, and the status email goes out at the right moment. - Send each person their verification link by email through Signicat. - Record a manual verification for people already checked during onboarding, without a Signicat journey. - Choose the verification workflow from the initiate dialog. Screening PEP and RCA shown independently, cleared sections stay visible, and capped counts read as 100+. - A person who is both a PEP and an RCA is shown as both, and risk scoring credits the higher-scoring rule. - Cleared sections stay visible, with a count of the entities hidden. - Hit counts above the cap show as "100+" rather than an exact-looking number. Ownership & Structure A searchable subsidiary tree, a tidy former-subsidiaries box, and ownership chart import. - Open large groups as a collapsible "N subsidiaries" card that expands into a searchable, indented tree with ownership and adverse-flag badges. - Dissolved subsidiaries gather into a "Former subsidiaries" box, keeping the live chart clean. - Import an ownership chart from a file and let entity resolution prefill it. Confident matches are pre-selected, and you pick from candidates where it is ambiguous. - The ownership chart renders for companies that have no register country. Portfolio Health Track your own risk fields in the dashboard, with drill-down and entity-type scoping. - Track custom risk fields in the Portfolio Health dashboard. Single-value fields show distribution cards, multi-value fields show counts, and both open a drill-down panel. - Each field shows which entity type it applies to, and the delete dialog warns when removing a shared field affects both tabs. Monitoring Court-case reschedules raise alerts, and new companies can be monitored from creation. - Court-case reschedules raise an alert and show the new scheduled date. - The custom company create form has a monitoring switch. Connect API A new financial-statements field and human-readable auditor remarks on the Connect API. - A new financial-statements field on the company returns annual figures: period, currency, share capital, revenue and net income. - Auditor remarks come through as human-readable text instead of disclaimer codes. Platform & UX Entity pages get a consistent, redesigned section and card layout. - Entity pages are redesigned. Each section is a consistent card with clearer structure. Platform & UX Permission tweaks, shareable search, webhook fixes and admin visibility. - Resetting a PEP or sanction disposition is limited to managers. - The member role selector shows the role name and respects the role-assignment permission. - Entity search filters live in the URL, so links are shareable and the back button works. - Team managers can create webhooks from the New webhook button. - The screening matrix adverse media cell distinguishes Cleared from No-data. - Review PDFs keep generating when you switch browser tabs. - You can manage your own email-notification preferences. - Outbound webhook deliveries send a JSON content-type header. - The Signicat webhooks admin page shows per-team status, the latest event and a warning for duplicate subscriptions. ###### Week 23 Jun 1 Jun 7 Sanctions screening reaches parity with PEP, and you can drill into portfolio risk factor by factor. Identity verification gains link recovery and a confirmation step, and the app now shows you exactly what your permissions allow. Court-case alerts get quieter. See everything that shipped Identity Verification Cancel a pending verification, generate a fresh link, and confirm before resetting. - Cancel a pending identity verification and generate a fresh link, straight from its pending status. - Resetting a verification asks for confirmation first. - Detail modals show the company a verification started on when it differs from the one you are viewing. Screening A missing-name penalty for sanctions, flagged entities on count scores, and clearer provenance. - Add a missing-name penalty to sanctions settings, matching PEP screening. - PEP, RCA and sanction count scores surface the flagged entities behind them. - Reactivating an entity re-enables its screening. - Risk-score matched values show where each value came from. Ownership & Structure Share classes and share counts on the shareholder graph, with voting weights kept on merge. - The shareholder graph edge exposes share classes, including the number of shares. - Voting-rights weighting holds when share classes are merged. Portfolio Health Drill down from the pie chart, and set assignee and monitoring when you add entities. - Click a pie segment to drill down, with paginated risk-factor drilldowns and page counts. - When you add entities to a portfolio, set an assignee and choose a monitoring opt-out in the same step. - Custom risk fields gain per-entity-kind scoping, help text shown as a tooltip, and a copy button in calculated-risk settings. Monitoring Set a court-case active window per team, and stop date-only changes raising alerts. - Set a court-case active window per team, so older cases stop generating alerts. - Court-case alerts fire on real case changes, not on date-only updates. Roles & Permissions The interface is gated on your permissions, not your role name. - The interface is gated on your actual permissions rather than your role name, so what you see matches what you can do. Connect API Member roles on team and current-user edges, and a clear 404 from sendForm. - Member roles are exposed on team member-list edges, and per team and customer on the current-user edge. - sendForm returns a clear 404 when the company ID does not resolve to a company. Platform & UX Assessment comments, recently-visited, document compare and settings fixes. - Assessment AI summaries include the analyst's entity-section comments. - Recently-visited keeps your newest entries. - The ownership chart toolbar is decluttered. - Compare very large documents without memory errors, with clear, localised messages when a comparison fails. - Screening-usage monitored counts respect source-preset filtering. - The active tab stays selected after saving calculated-risk settings. ###### Week 22 May 25 May 31 Monitoring becomes fully explicit this week, with a clear toggle, a portfolio column and typed identifier alerts. The Screening Matrix reaches the person page, identity verification follows a person across the team, and the ownership chart gains a control view. On the Connect API, name search is faster and the AMS AI Search API is removed. See everything that shipped Identity Verification A verification follows a person across every company in the team, and shows the method used. - Verify a person once and reuse it across every company they appear in within a team. - The Verify flow shows which method was used, recorded when Signicat completes. Screening See the Screening Matrix on the person page, hide cleared entities, and tune with new PEP and RCA metrics. - Open the Screening Matrix on the person entity page. - Hide cleared entities with a new toggle. - New PEP and RCA metrics panels in settings break down current hits by role, country signal, match percentage and how recently roles ended, so you can see the effect of your screening settings. Ownership & Structure A control view, weight-based voting rights on any company, self-ownership edits and source metadata. - Switch between ownership and control with a new control view on the chart. - Apply voting-rights adjustments to any company in the ownership graph, using weight-based share classes. - Edit self-ownership in the chart. - See source metadata on holdings. Portfolio Health Drill down behind each risk factor, in a refreshed dashboard. - Drill down behind each risk-factor metric in Portfolio Health. - A refreshed dashboard shows side-by-side risk-distribution charts, inline data freshness and a Configure link. Monitoring Turn monitoring on explicitly, with a portfolio column, a header badge and typed identifier alerts. - Turn company monitoring on explicitly, with a Monitored column in the portfolio, a badge on the company header and an opt-in toggle. - Company change alerts use typed identifiers (LEI, registration number, VAT, charity or other), and you can mute alerts per identifier type. - Company-information alerts highlight every changed field. - Relation alerts show data-update latency and edit-state changes. Roles & Permissions A new Assignments sub-tab in admin roles and permissions. - Manage role assignments from a new Assignments sub-tab in admin roles and permissions. Connect API Name search defaults to exact match, webhooks deliver raw JSON, and the AMS AI Search API is removed. - Breaking change: the AMS AI Search API is removed. The amsEventsSearch query, its result types, and the amsGoogleAiSearchTerms field no longer exist, so callers must stop requesting them. Existing AI-search events and stored search terms are kept. - Name search defaults to exact match and returns immediately. Fuzzy matching is available as an opt-in that fills in alongside. - Webhook deliveries arrive as raw event JSON with metadata in HTTP headers, and a new Webhooks guide covers the events, request format and OIDC verification with Node, Python and Go samples. Platform & UX Fixes and polish across search, ownership, extraction and screening. - The desktop search bar is back in the header. - The home and search pages lay out correctly on mobile. - Share classes in the ownership chart sort alphabetically. - Risk distribution renders as a filled pie chart in Portfolio Health. - Adverse media snippets show the exact text around each detected mention. - Document and image extraction uses a stronger model for cleaner results. - Identity verification can be reset even when a Signicat session is broken. - Life status reads correctly for entities seen only through secondary data sources. - Beneficial-owner removals are honoured across all ownership paths. ##### Don’t miss any updates --- ### UBO Mapping Under AMLR: The Complete Guide — Strise URL: https://strise.ai/guides/ubo-mapping-amlr How AMLR changes beneficial ownership from 10 July 2027: the accumulation method, the 25% threshold, control assessed separately, the combination rule, and what to do when no UBO is found. #### UBO Mapping Under AMLR: The Complete Guide Author Miguel Ochoa Growth and GTM engineer — Strise® Table of contents Table of contents Key takeaways What AMLR changes about UBO identification Ownership: the accumulation method and the 25% line Control: the part most processes miss Combination: owning at one level, controlling at another Why the register stops being your verification step When no beneficial owner can be identified What this means for compliance teams Where UBO mapping breaks down in practice How Strise approaches UBO mapping under AMLR An AMLR UBO mapping checklist From 10 July 2027, the Anti-Money Laundering Regulation (Regulation (EU) 2024/1624 , AMLR) changes how beneficial ownership is identified across the EU. Ownership is no longer a single trace to the largest owner at each level. AMLR asks you to calculate ownership by accumulation, assess control in parallel, and name anyone who reaches 25%. For Nordic teams the change runs deeper than the maths: national registers can no longer stand alone as the verification step. You determine ownership yourself, then check the register against your result. This guide covers what changes, the accumulation method in practice, why control is the part most processes miss, how the combination rule catches mixed structures, what replaces the register shortcut, and what it means for your team. ##### Key takeaways - Ownership uses the accumulation method : multiply percentages down each chain, then add what each person holds across all chains (Art. 52 ). - The threshold is 25% or more , not “more than 25%” (Art. 52 ). Exactly 25% now qualifies. - Control is assessed separately (Art. 53 ); a person can be a beneficial owner through control with little or no equity. - A combination rule catches people who own at one level and control at another. - Registers are no longer your verification step : verify yourself, compare, and report discrepancies within 14 calendar days of detection. - No owner found means identify and verify all senior managing officials , not one stand-in. - Net effect: more named individuals per company , each flowing into identity verification, screening, and ongoing monitoring. ##### What AMLR changes about UBO identification Most processes today follow the chain to the largest holder at each level and stop. AMLR makes that insufficient. Beneficial ownership is assessed through ownership, control, and the combination of the two , and a person caught by any one of them is a beneficial owner. A process that only multiplies ownership chains will under-identify beneficial owners against the regulation. ##### Ownership: the accumulation method and the 25% line Under Art. 52 , indirect ownership is calculated by multiplying the shares or voting rights held through each intermediate entity in a chain, then summing a person’s holdings across every chain that involves them, against a threshold of 25% or more. The calculation is cumulative, so you cannot stop following a chain because one holding looks small. And the wording moved from “more than 25%” to “25% or more”, so exactly 25% is now in scope. For certain higher-risk sectors, a delegated act may set the threshold below 25%. Worked example. Shady Sharks Ltd. is the company being onboarded. Its ownership runs through two chains: Beneficial ownership of Shady Sharks Ltd., mapped across two chains to a single ultimate beneficial owner at 64% Owners can’t hide behind a holding structure. Strise traces every layer, accumulates across chains, and names the beneficial owner, here, Corruption Carlson at 64%. Illustrative example from the Strise app. - Mammal Money Laundering Ltd. owns 30% of Shady Sharks. Mammal is owned by Corruption Carlson (80%) and Bribery Barnes (20%, a PEP). - Llama Tax Havens Inc. owns 40% of Shady Sharks. Llama is owned 100% by Octopus Offshore Holdings, which is in turn owned 100% by Corruption Carlson. Running the accumulation method on Corruption Carlson: - Via Mammal: 80% × 30% = 24% . - Via Octopus and Llama: 100% × 100% × 40% = 40% . - Accumulated: 24% + 40% = 64% . Beneficial owner, and the ultimate beneficial owner of Shady Sharks. The point is the accumulation. Assessed one chain at a time, Carlson looks like a 24% holder through Mammal and a separate 40% holder through the offshore chain. Only by adding the chains do you see the true 64%, sitting behind two layers of holding companies. Bribery Barnes, by contrast, accumulates to just 6% (20% × 30%) and falls below the 25% ownership threshold, but as a PEP still needs screening if named on the structure. None of this runs without first mapping the full structure: every holding company and individual with capital or voting rights. You cannot multiply across chains you have not mapped. ##### Control: the part most processes miss Control is assessed independently of ownership (Art. 53 ). Someone can be a beneficial owner through control alone, with little or no equity, which is exactly why it is missed: these people rarely appear in ownership data. Triggers typically include a majority of voting rights, the right to appoint or remove most of the board or management body, and veto, profit, or asset-disposition rights. Softer forms (nominee arrangements, voting agreements, family influence) are a documented, case-by-case judgement. Worked example. Dana holds 8% of TargetCo but, through a shareholders’ agreement, has the right to appoint and remove a majority of the board. On ownership Dana is far below 25%; on control, Dana is a beneficial owner. It appears in no register, so it has to be asked directly at onboarding: voting agreements, appointment rights, nominee arrangements. ##### Combination: owning at one level, controlling at another AMLR also catches mixed structures [confirm Art.]. Worked example. HoldCo controls TargetCo through a 55% stake. Priya owns 40% of HoldCo. Her indirect ownership of TargetCo is 22% (40% × 55%), below the threshold. But because HoldCo controls TargetCo and Priya holds 25% or more of that controlling entity, the combination rule names her a beneficial owner. Ownership maths alone would have stopped at 22% and missed her. ##### Why the register stops being your verification step Consulting the register stays a requirement; being your verification step does not [confirm Art.]. Today, for many Nordic teams, a match between the company’s declaration and the register is treated as enough. From July 2027 you determine and verify beneficial ownership through your own means first, then compare against the register. Verification should rest on methods you can stand behind: a document check, qualified electronic identification, or other reliable sources, applied on a risk-proportionate basis with the reasoning recorded. This favours original documents: when your result comes from registry filings, articles of association, and shareholder lists, you can show exactly how you reached it and defend it. When it comes from a data provider whose method you cannot see, you cannot. Where findings differ, you act: report discrepancies to the central register within 14 calendar days of detection, with a derogation allowing you to ask the customer to correct the register first in defined cases [confirm Art.]. ##### When no beneficial owner can be identified If, after exhausting all means, no beneficial owner can be found, AMLR requires you to identify and verify all of the company’s senior managing officials, not a single stand-in. Senior managing officials are the executive members of the management body and the people responsible for day-to-day management. For a company with a large management body, that can mean several identity checks and screening runs where a previous process recorded one. ##### What this means for compliance teams Every change points the same way: more named individuals per company. Control adds people ownership maths misses, accumulation pulls in split owners who look small in any one chain, combination catches mixed structures, and the fallback expands from one senior manager to all of them. Every beneficial owner named carries the same downstream work: identity verification, screening against PEP and sanctions lists, and a place in the ongoing monitoring queue. Run manually or across disconnected tools, that volume compounds across the portfolio, and the result becomes hard to reconstruct when a regulator asks how you got there. This is the connected-data expectation underneath AMLR. A UBO graph in one tool, screening in another, and monitoring in a third adds a manual handoff at every step. Preparing for AMLR means onboarding, screening, and monitoring on the same customer record, with every named person flowing automatically from the ownership map into screening and monitoring. It is the same operational lesson behind clearing a back book: see our guide to KYC remediation under AMLR. ##### Where UBO mapping breaks down in practice The regulation is one thing; running it across a real portfolio is another. A few recurring problems decide whether a UBO process holds up under AMLR. Cross-border chains do not trace themselves. Each jurisdiction means a different registry, in a different format, with different freshness and access. Chains that cross borders are time-consuming to follow, so under pressure they get traced partially, or not at all. The accumulation method only works if the whole chain is followed, wherever it leads. Aggregate control slips through individual thresholds. Three shareholders each holding 18% do not individually trip a 25% flag, but together they control 54%. Without cumulative logic across related parties, a process clears the entity and misses the people behind it. The same applies to families with sub-threshold stakes, and to share classes that carry votes out of proportion to equity. Some chains end at a locked door. Trusts and foundations are not always required to declare their controllers. When a chain ends at one, the answer is not a blank field: AMLR expects a documented, targeted effort to establish control, and a clear record of where the trail ends and why. Ownership drifts after onboarding. A director is added, a shareholding is transferred, a new entity appears in the structure. None of it surfaces on its own. A UBO mapped at onboarding and left until the next periodic review is, in practice, a UBO you no longer know. ##### How Strise approaches UBO mapping under AMLR Strise is built around the way AMLR expects beneficial ownership to be established: mapped from the company number to the ultimate natural persons, calculated, verified across multiple sources, and kept current, with the audit trail assembled as the work runs. - Map the full structure automatically. From one company number, Strise traces upstream to the ultimate natural persons and downstream to subsidiaries, across jurisdictions and through holding layers, with diluted percentages calculated and every data source cited at each node. - Cumulative ownership and real control. Direct and accumulated ownership are calculated at every level, so a 60% stake in a company that owns 50% of another shows as 30%, not 60%, against whatever threshold your policy sets. Family members with sub-threshold stakes are aggregated, and share classes are mapped by voting control, not just headline ownership. - Verify across multiple sources. Registries, AI-read documents, and analyst-verified data are cross-referenced into one ownership record, every source cited and every change timestamped. When a registry hits a wall, Strise generates a targeted document request, then extracts and maps the response with no re-entry. - Stay current. Changes to ownership, directors, or structure are picked up automatically and trigger a review when they affect risk, rather than waiting for the next cycle. Every named person flows into PEP and sanctions screening and ongoing monitoring on the same record. In practice that means around 80% of routine cases handled automatically, complex cases resolved in about 30 minutes rather than three hours, and roughly six times more complex cases per analyst with no new hires, drawing on data from more than 250 jurisdictions. See how UBO discovery works. ##### An AMLR UBO mapping checklist - Map the whole structure first. Every owner and holding company, every level, ownership and voting rights recorded separately. You cannot calculate across chains you have not mapped. - Run ownership by accumulation. Multiply down each chain, add across chains, and apply the 25% or more threshold (Art. 52 ). - Assess control in parallel. Voting rights, appointment rights, veto and profit rights, and softer arrangements (Art. 53 ). - Check for combination. Anyone who owns at one level and controls at another . - Ask about control at onboarding. Voting agreements, appointment rights, nominee arrangements. - Verify yourself, then check the register , and report discrepancies within 14 calendar days. - No owner found? Name every senior managing official , not one stand-in. - Connect each named person to the same record. Screening and ongoing monitoring, kept current as the structure changes. Published: 26 June, 2026. This guide is published by Strise. See AMLR-ready UBO mapping in action. [Book a demo with Strise →](https://strise.ai/book-a-meeting-with-strise) ##### Find answers to common questions about KYC remediation How does UBO identification change under AMLR in 2027? AMLR assesses beneficial ownership through ownership and control in parallel (Art. 52 and Art. 53 ), plus a combination rule, rather than tracing to the largest owner at each level. A person caught by any test is a beneficial owner, usually meaning more named individuals per company. What is the AMLR accumulation method? You follow every ownership chain, multiply the percentages down each one, and add what each person holds across all chains (Art. 52 ). Anyone reaching 25% or more is a beneficial owner, including someone whose stake is split across several chains and looks small in each. What is the UBO threshold under AMLR? 25% or more (Art. 52 ). Previously usually "more than 25%", so exactly 25% now counts. A delegated act may lower it for certain high-risk sectors. What is the combination rule under AMLR? It names a person who owns at one level and controls at another within the same chain, for example holding 25% or more of an entity that controls the target. Can I still rely on the beneficial ownership register under AMLR? Not on its own. You consult it, but determine beneficial ownership yourself, compare against the register, and report any discrepancy within 14 calendar days of detection. How is control assessed under AMLR? Alongside ownership (Art. 53 ): majority voting rights, board appointment or removal rights, and veto, profit, or asset rights generally qualify; softer forms are a documented judgement. A person can be a beneficial owner through control with little or no ownership. What happens when no beneficial owner can be identified? You identify and verify all of the company’s senior managing officials, not one stand-in. What are senior managing officials under AMLR? The executive members of the management body and the people responsible for day-to-day management. Where no beneficial owner can be identified after all means are exhausted, all of them must be identified and verified, not just one. ##### Success stories - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### BDO automates the fight against financial crime with Strise | Strise Blog URL: https://strise.ai/blog/bdo-automates-the-fight-against-financial-crime-with-strise BDO, one of Norway's leading advisory and audit firms, has selected Strise as its technology provider for customer due diligence and anti-money laundering (AML). The agreement makes Strise's AI platform a central part of how BDO meets its anti-money laundering obligations. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - AML #### BDO automates the fight against financial crime with Strise Published on: July 22, 2026 Updated on: August 13, 2026 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents Tougher requirements from the EU Everything on one platform A strategically important agreement for Strise Press contact Oslo, 22 July 2026: BDO , one of Norway's leading advisory and audit firms, has selected [Strise](https://strise.ai) as its technology provider for customer due diligence and anti-money laundering (AML). The agreement makes Strise's AI platform a central part of how BDO meets its anti-money laundering obligations. With this, BDO joins the ranks of leading audit and advisory firms adopting Strise to streamline their AML compliance work. Strise already provides similar solutions to several of the largest players in the industry. ##### Tougher requirements from the EU The audit and advisory industry is subject to strict requirements for ongoing customer monitoring under anti-money laundering legislation. This work has traditionally been manual and time-consuming, involving the collection of ownership data, risk assessments and documentation across a wide range of sources. Strise brings this together in a single, AI-powered platform where much of the process is automated. The need is being reinforced further by developments at the European level. The EU's new Anti-Money Laundering Regulation (AMLR) takes effect on 10 July 2027, introducing a single, harmonised European framework, the so-called "single rulebook", with more detailed requirements for ongoing customer due diligence and the mapping of [beneficial owners](https://strise.ai/product/ubo-discovery). This means expectations of even greater precision and traceability in compliance work. Technology that automates and streamlines customer due diligence therefore becomes an important success factor for many regulated industries. ##### Everything on one platform With Strise, BDO gains an end-to-end solution that automates much of the customer due diligence process. The platform gathers and connects company and ownership data in real time using AI, and makes it possible to configure [risk scoring](https://strise.ai/product/risk-engine) directly against BDO's own risk models. Digital customer forms simplify [onboarding](https://strise.ai/product/kyb-onboarding) for clients, while integrated ID verification and automated [screening against sanctions and PEP lists](https://strise.ai/product/screening-and-alert-triage) ensure regulatory requirements are met. In addition, Strise's new module for [ongoing due diligence](https://strise.ai/product/ongoing-due-diligence) continuously monitors the risk picture around both clients and the networks behind them. "Customer due diligence and compliance are core tasks for us, but also an area where manual work ties up a great deal of time." Anne Reisch Bruset , Head of KYC, BDO "With Strise, AI can take on much of the time-consuming routine work in customer due diligence. The system gathers, connects and structures the data, gives us valuable insight into our client assessments, and continuously alerts us when something needs attention. That frees our advisors to apply their expertise where it truly matters." Anne Reisch Bruset , Head of KYC, BDO ##### A strategically important agreement for Strise The partnership underpins Strise's strong momentum in the enterprise market and marks another important step toward modernising the compliance infrastructure for the largest players in Europe. "We are very proud to become BDO's strategic partner. With our AI technology, we will help them navigate new regulations and ensure they are as well prepared as possible for the future. That one of the country's most respected professional communities places its trust in us shows that AI is essential to keeping pace going forward." Marit Rødevand , CEO, Strise Strise delivers an end-to-end AI platform for KYB, KYC and anti-money laundering, available as an application, via API, and as an agentic "AI workforce" that can carry out parts of the compliance work. ##### Press contact Lars Lunde Birkeland , CMO, Strise lars@strise.ai ##### FAQ Why did BDO choose Strise for AML compliance? BDO selected Strise as its technology provider for customer due diligence and AML because manual compliance work tied up significant advisor time. Strise's AI platform gathers, connects, and structures company and ownership data automatically, alerts BDO when something needs attention, and frees advisors to apply their expertise where it matters. The agreement, announced 22 July 2026, makes Strise a central part of how BDO meets its anti-money laundering obligations. What does the EU's AMLR mean for audit and advisory firms? The EU's Anti-Money Laundering Regulation (AMLR) takes effect on 10 July 2027 and introduces a single harmonised rulebook across Europe, with more detailed requirements for ongoing customer due diligence and the mapping of beneficial owners. For audit and advisory firms, that means greater precision and traceability in compliance work, and it makes technology that automates customer due diligence a success factor rather than a nice-to-have. What will BDO use the Strise platform for? BDO gets an end-to-end platform for customer due diligence: company and ownership data gathered and connected in real time, risk scoring configured against BDO's own risk models, digital customer forms for onboarding, integrated ID verification, and automated screening against sanctions and PEP lists. Strise's ongoing due diligence module also monitors the risk picture around clients and the networks behind them continuously. Who else uses Strise in the audit and advisory industry? Strise already provides AML and customer due diligence technology to several of the largest players in the audit and advisory industry, including PwC Norway. With the BDO agreement, Strise strengthens its position as the compliance platform for leading professional services firms in the Nordics and Europe. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Best AML monitoring tools for 2026: AI‑native platforms comparison | Strise Blog URL: https://strise.ai/blog/best-aml-monitoring-tools-in-2025-ai-native-platforms-comparison Compare AI-native AML platforms in 2026. See how Strise, Duna, Greenlite, Spektr, and Bits Technology handle automation, risk, and compliance scale. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - AML #### Best AML monitoring tools for 2026: AI‑native platforms comparison Published on: August 21, 2025 Updated on: January 7, 2026 Author Miguel Ochoa Growth and GTM engineer — Strise® Table of contents Table of contents Best AI‑native AML software platforms in 2026 2026 vendor comparison: Top 5 AI-native AML tools When to pick Strise ##### Best AI‑native AML software platforms in 2026 AML compliance in 2025 is more complex than ever. And as we move into 2026, financial institutions across the UK, EU, and globally face rising transaction volumes, fast changing regulations, and increasingly sophisticated financial crime. Legacy tools can’t keep up. This guide compares modern and AI-native AML platforms built to automate onboarding, monitoring, risk scoring, and alert resolution. You’ll find practical insights into how each platform works, their strengths, and when to choose Strise as your solution. ###### What defines a modern AML platform? We selected platforms that meet five core criteria: - AI automation : Uses AI to reduce manual work in onboarding, risk checks, and alert resolution. - Unified data & APIs : Aggregates registries, sanctions lists, and adverse media, with enterprise-grade integrations. - Continuous monitoring : Triggers alerts on any change in risk, eliminating the need for periodic reviews. - Configurable workflows : No-code or low-code tools to build your own processes and risk policies. - Proven impact : Demonstrates real-world results: faster onboarding, fewer false positives, and lower costs. ##### 2026 vendor comparison: Top 5 AI-native AML tools ###### 1. Strise Best for: Enterprises that need scalable AML automation across onboarding, monitoring, and alert resolution. Overview: Strise flips the traditional AML model, starting not with workflows, but with data. At its core is a proprietary data engine that harmonizes registry filings, internal notes, analyst inputs, documents, and third-party data into continuously updated, living entity profiles. Built on a graph-based foundation, Strise uncovers hidden relationships and network risks. It applies AI and configurable policies to classify risk in real time, while analysts resolve alerts faster in a unified workspace with full audit trails and contextual insights. Strengths: - Unified risk profile across IDV, KYC/KYB, UBO, and monitoring - Graph intelligence + AI detect hidden risks early, not just faster - 90% faster due diligence and ~30% cost savings in production - Flexible architecture: API, UI, or hybrid integrations - Trusted by PwC Norway, Corpay, Vipps MobilePay, Nordea and other leading enterprises ###### 2. Duna Best for: Fast, user-friendly business onboarding with deep KYB focus. Overview: Duna provides a no-code platform for building KYB flows with embedded ID checks, AML screening, and e-signatures. Focused on user experience and conversion, it pre-fills data from 210+ registries and supports dynamic forms, real-time scoring, and multilingual onboarding. Strengths: - High-conversion onboarding UX - Full verification stack: ID, bank, and more - Policy automation with audit trails ###### 3. Greenlite AI Best for: Automating alert handling in existing AML systems. Overview: Greenlite deploys AI agents to triage and resolve sanctions, PEP, and transaction monitoring alerts. These agents mimic human analysts to auto-clear low-risk hits, following guidance aligned with U.S. and UK regulators. Strengths: - Cuts 90–95% of repetitive analyst work - Audit-ready AI aligned with OCC/FINRA guidelines - Fast, non-disruptive integration with legacy tools ###### 4. Spektr Best for: Teams needing fully customizable, no-code compliance workflows. Overview: Spektr lets users design and automate every part of the AML lifecycle; onboarding, screening, and monitoring, without code. Its visual builder integrates with any data source or risk engine and supports real-time compliance. Strengths: - Hyper-customizable workflows and risk logic - Full client lifecycle management - Real-time, trigger-based monitoring ###### 5. Bits Technology Best for: Fintechs and growth-stage companies expanding across Europe. Overview: Bits is a cloud-native compliance hub with modular onboarding, monitoring, and verification tools. It connects identity, risk, and document checks from multiple providers into a centralized layer. Strengths: - Modular, market-adaptable setup - Built for fintechs scaling across jurisdictions - Collaborative UI with built-in case management ##### When to pick Strise Strise is the right choice if you: - Struggle with fragmented data : Replace spreadsheets, PDFs, and disconnected systems with unified, real-time risk profiles. - Need earlier risk detection : Graph-based analytics reveal indirect exposures before onboarding begins. - Face alert overload : Analysts see only prioritized, high-quality alerts with full audit trails and rich context. - Want fast, flexible deployment : Strise integrates easily via API and can run alongside your existing stack. - Plan to scale with AI : Strise starts with the foundation—data —making automation reliable, scalable, and regulator-ready. ##### FAQ What criteria define a modern AML platform? Modern AML platforms should combine AI automation, unified data and APIs, continuous monitoring, configurable workflows, and proven real-world impact. These capabilities enable firms to detect suspicious activity faster while reducing false positives. How do leading AML platforms like Strise, Duna, and Greenlite compare? Each platform offers different strengths. Evaluation should focus on your primary pain point: whether that is false positive reduction, sanctions accuracy, onboarding speed, or specific regulatory requirements in your jurisdiction. What should drive AML tool selection decisions? Prioritize integration capabilities with your existing systems, regulatory requirements for your industry and jurisdiction, analyst workflow fit, alert quality and tuning options, and total cost of ownership. The best platform is the one your team will actually use effectively. Why do false positive rates matter in AML monitoring tools? High false positive rates waste analyst time on non-suspicious alerts, causing alert fatigue and missing genuine risks. Modern platforms use machine learning and configurable rules to reduce false positives significantly, allowing analysts to focus on alerts with genuine investigative value. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Best AML software for banks and payment companies, and what good looks like in 2026 | Strise Blog URL: https://strise.ai/blog/best-aml-software-for-banks-and-payment-companies Banks are rethinking AML foundations as AI-native, data-driven software enables real-time detection, explainable automation, and lasting compliance trust. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - AML #### Best AML software for banks and payment companies, and what good looks like in 2026 Published on: October 17, 2025 Updated on: June 30, 2026 Author Miguel Ochoa Growth and GTM engineer — Strise® Table of contents Table of contents Why AML software matters more than ever Best AI AML software for banks AML AI compliance software 2026 What AMLR means for AML software in 2026 Top AI anti money laundering solutions for financial institutions AML AI tools bank AML Top AML automation platforms in 2026 Final thought ##### Why AML software matters more than ever Banks and financial institutions today are navigating an unprecedented mix of regulatory pressure, technological possibility, and operational fatigue. Yet despite years of investment, the industry still struggles with effectiveness. According to [The AML Megaminds Report](https://strise.ai/the-laundry), 70% of AML (Anti-Money Laundering) professionals believe current controls are inefficient, and Europol estimates that only 1% of laundered money is ever intercepted. Across Europe, from the Nordics to the UK, banks and financial institutions are rethinking AML modernization strategies as regulations tighten and AI becomes essential. This gap between effort and outcome underscores why banks are rebuilding their AML foundations. Legacy systems and manual workflows can no longer keep pace with evolving risks, driving the shift toward AI-native, data-driven platforms that enable real-time detection and explainable automation. Across recent discussions on Strise's The Laundry podcast , we spoke with experienced AML and compliance leaders from leading banks, fintechs, and advisory firms across Europe and the UK. Despite different business models, one common theme emerged: the future of AML belongs to institutions that treat data as infrastructure, not a by-product. This guide distils those practitioner insights into what “good” looks like, how leading teams are modernising their AML stack, and which vendors are setting the standard for trustworthy automation. You’ll learn: - How practitioners define effective AML modernisation. - What pitfalls slow banks down. - How AI, data quality, and explainability converge. - A practical overview of leading AML platforms. ##### Best AI AML software for banks AML AI compliance software 2026 By 2025, the gap is obvious. Legacy AML tools struggle with scale, speed, and data quality, while AI native platforms handle complexity by default. The best AI AML software for banks focuses on end to end workflows, not isolated checks. That means onboarding, transaction monitoring, risk scoring, and alert resolution all connected through a shared data layer. Banks choosing platforms like [Strise](https://strise.ai), Silent Eight, or Arva are doing so because these tools reduce noise, surface real risk earlier, and stay explainable under regulatory scrutiny. The real differentiator is not just AI, but how well that AI is grounded in clean, unified, and auditable data. ##### What AMLR means for AML software in 2026 The regulatory clock behind these shifts is AMLR. The EU's Anti-Money Laundering Regulation (Regulation (EU) 2024/1624) applies from 10 July 2027, and it reshapes two things AML software has to get right: how beneficial ownership is established, and how current your customer data has to be. On ownership, AMLR replaces single-chain tracing with the accumulation method: ownership is multiplied down each chain and summed across all of them, against a threshold of 25% or more, with control assessed in parallel. The practical effect is more named individuals per company, and each one flows into screening and monitoring. Beneficial ownership registers also stop being a sufficient verification source on their own, so you determine ownership through your own means first, then check the register. We cover this in full in our guide to [UBO mapping under AMLR](https://strise.ai/guides/ubo-mapping-amlr). On data, supervisors will assess firms on the quality of their customer records. A back book of stale profiles and risk scores that were never recalculated is exactly the kind of finding that triggers scrutiny, which is why remediation programmes are accelerating ahead of the deadline. See our guide to [KYC remediation under AMLR .](https://strise.ai/guides/kyc-remediation) What good looks like under AMLR is the same thing this guide keeps returning to: connected data. Ownership mapped across every layer and jurisdiction, verified across multiple independent sources, calculated by accumulation, and kept current as structures change, with every named person flowing automatically into screening and ongoing monitoring on one record. Software that bolts AMLR on in 2027 will struggle; software built this way already meets it. ##### Top AI anti money laundering solutions for financial institutions AML AI tools bank AML Top AI anti money laundering solutions for financial institutions go beyond automation for automation’s sake. They help compliance teams move faster without losing control. Modern AML AI tools for banks combine real time monitoring, adaptive risk models, and transparent decision logic that regulators can actually follow. Solutions like Strise and Spektr give institutions flexibility to tailor workflows, while tools like Bretton AI focus heavily on clearing routine alerts at scale. The strongest platforms balance efficiency with trust, proving that AI can reduce manual work, improve decision quality, and still meet the growing demands of global AML regulation. ###### 1. Data is both the problem and the solution “We still see teams running onboarding and periodic reviews on spreadsheets — 80 columns, 150 rows. It makes me anxious just talking about it.” — Robert E. Bing , Partner, Davies Group From large banks to fast-moving fintechs, the same pattern repeats: compliance teams are buried under data that’s duplicated, outdated, or siloed. The best teams are reversing this. Instead of hoarding, they’re curating, building living, connected profiles that flow across onboarding, monitoring, and review cycles. ###### 2. Perpetual KYB/C monitoring is replacing periodic reviews “We put more emphasis on continuous relationship monitoring. You need to have AML data to verify that against.” — Heili Veskimeister , MRLO & Head of Compliance, Lightyear Institutions are designing event-driven monitoring where [every ownership change](https://strise.ai/guides/ubo-mapping-amlr), sanction update, or new adverse-media signal automatically recalibrates risk scores. This shift eliminates dead-weight reviews and gives analysts real-time context for decisions. ###### 3. AI adoption and explainability in AML software “You still need to understand the methodology. If you’re responsible for building controls, you must know how the tools work.” — Heili Veskimeister , Lightyear Automation will handle document parsing, adverse-media searches, and false-positive clearing. Humans will supervise model design, validation, and risk logic. The emphasis is shifting from “Can AI do it?” to “Can we explain why it did it?” Also, according to Advisense’s 2025 AML State of Play: Special Edition on AI , poor data quality is one of the biggest blocker to effective AI adoption in financial crime compliance. AI models depend on complete, accurate, and structured data, yet most institutions still rely on fragmented legacy systems and manual spreadsheets that hinder automation and insight generation. In our The Laundry episode on AI agents , the experts added that the next evolution will see humans governing AI, not competing with it. Spotify sets its own cookies, so this player stays off until you allow functional cookies. You can change that in your [cookie preferences](https://strise.ai/cookie-policy). Open cookie preferences ###### 4. Innovation and compliance can co-exist “Compliance isn’t a showstopper anymore, it’s something we co-create.” — Karoline Tyan , Executive in AML/CFT, Avanza Bank Banks like Avanza prove that product thinking and AML compliance can, and must, merge. When compliance teams work as product owners, the outcome isn’t just control, it’s customer trust and scalable innovation. “The real differentiator between two banks isn’t their products, it’s how fast and intelligently they onboard.” — Robert E. Bing , Partner, Davies Group At Vipps MobilePay, Øverby calls this the “early-bird approach”, involving risk and compliance early in every product process so teams can design compliance into the business rather than layering it on later. Payment companies face the same regulatory obligations as banks, but with faster onboarding cycles, cross-border merchants, and higher transaction volumes. ###### How payment companies automate AML for efficiency: ###### 5. Culture and collaboration define AML maturity “Each party, banks, asset managers, and payment companies, has data the others need. We should find safer ways to share it.” — Jaypee Soule , Head of Second-Line Compliance, PensionBee Cross-institution data sharing remains primitive, but collaboration is now seen as the ultimate unlock for speed and trust. Vipps MobilePay provides a case study in cultural design: open Slack channels where product and risk teams share memes, questions, and quick clarifications, making compliance part of everyday conversation, not a gatekeeping function. “Having the right technology in place to react faster instead of implementing old systems that take forever, that’s what modern compliance looks like.” — Jaypee Soule , PensionBee ##### Top AML automation platforms in 2026 Note: This overview is not an endorsement or recommendation from any guest featured on The Laundry podcast. It’s an independent, curated look at AI tools for financial crime prevention gaining strong traction in the banking sector as we move into 2026. Each supports modern, AI-enhanced compliance operations, from onboarding to perpetual monitoring. Many leading AML platforms now automate UBO discovery and verification, helping banks maintain transparency across complex corporate ownership structures. ###### Data providers - Sayari – Provides commercial risk intelligence with deep visibility into ownership structures, trade networks, and cross-border relationships. - Kyckr – Supplies verified, direct-from-registry company data to support reliable KYC and KYB checks. - LexisNexis – Delivers sanctions, PEP, and adverse media screening data, including extensive historical coverage. - Dow Jones Risk & Compliance – Offers premium datasets for sanctions, PEPs, and adverse media, trusted by global financial institutions. ###### AI tools for AML compliance - [Strise](https://strise.ai) – [AML automation](https://strise.ai) built on trusted data. Fuses registry, watchlists, and media sources into explainable risk profiles. Used by leading banks, financial institutions, and payment companies such as PwC, Nordea, and Corpay. - Napier AI – AI-powered AML compliance platform built for scale, combining transaction monitoring, sanctions screening, and adaptive analytics. - Lucinity – AI-driven case management platform that applies behavioral analytics to help analysts investigate and resolve alerts faster. - Bretton – Deploys AI agents that automate screening, due diligence, and alert handling, reducing repetitive analyst work. ###### Case management and lifecycle tools (CLM) - Fenergo – Enterprise CLM platform covering onboarding through ongoing reviews, with built-in regulatory workflows. - Pega – Unified case & alert management platform that consolidates AML, fraud, sanctions, and transaction alerts into a single investigative framework. - KYCPortal – A highly configurable client lifecycle management solution that centralises due diligence, risk scoring, and customer outreach. - KYC Hub – A cloud-native compliance automation platform combining onboarding, AML screening, ongoing monitoring, and case management. ##### Final thought “Compliance can be a competitive advantage when it’s built on curiosity, not control.” — Marit Rødevand , CEO, Strise The next generation of AML teams won’t just meet regulations, they’ll compete on trust, speed, and explainability. The winners will be the institutions that start with data, apply explainable AI, and design compliance as a product, not a constraint. ##### FAQ What percentage of laundered money is actually intercepted? Only approximately 1% of laundered money is intercepted by AML systems. This statistic underscores why effective AML software is critical for banks and payment companies that rely on outdated or manual processes. Why is data infrastructure critical for AML compliance? Effective AML detection depends on data quality, integration from multiple sources, and real-time processing capabilities. Banks that treat their AML data infrastructure as a strategic asset significantly improve detection accuracy and reduce manual investigation burden. What unique AML challenges do payment companies face? Payment companies handle high transaction volumes across diverse corridors and customer types, making pattern detection complex. They need real-time transaction monitoring that can distinguish legitimate high-velocity activity from structuring or layering schemes. What do AML practitioners say are the biggest operational challenges? Practitioners consistently cite alert fatigue, difficulty understanding customer behavior at scale, balancing compliance with customer experience, and keeping up with evolving regulatory requirements. Successful AML programs address these through better data, smarter tools, and clear governance. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Best KYC and KYB software tools in 2026: A practitioner’s guide | Strise Blog URL: https://strise.ai/blog/best-kyc-kyb-software-tools The best KYC and KYB software tools in 2026, compared by category: data providers, AI platforms, identity verification and lifecycle tools, plus what good looks like for perpetual KYC. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - KYC #### Best KYC and KYB software tools in 2026: A practitioner’s guide Published on: September 16, 2025 Updated on: October 15, 2026 Author Miguel Ochoa Growth and GTM engineer — Strise® Table of contents Table of contents What is the difference between KYC and KYB? Why does a risk-based approach to KYC/KYB matter? What does good KYC/KYB look like? How do mature compliance teams keep improving? What are the best KYC and KYB software tools in 2026? KYC (Know Your Customer) verifies the identity of individuals, while KYB (Know Your Business) verifies companies, their legal existence, and their ultimate beneficial owners. Both are shifting from one-off checks at onboarding to perpetual, event-driven monitoring. This guide covers what good looks like, where teams get stuck, and which vendors to evaluate in 2026. Know Your Customer (KYC) and Know Your Business (KYB) sit at the heart of modern financial crime prevention. - Done well: they speed up onboarding, protect your brand, and keep regulators confident. - Done poorly: they create backlogs, friction, and blind spots. This guide distills a practitioner's view of KYC/KYB, based on a conversation with Martin Walker (PA Consulting) in our podcast The Laundry . Spotify sets its own cookies, so this player stays off until you allow functional cookies. You can change that in your [cookie preferences](https://strise.ai/cookie-policy). Open cookie preferences You'll learn: - What "good" looks like. - How leading teams are modernizing. - Where the traps are. - A pragmatic vendor landscape and a ready-to-use FAQ at the end. ##### What is the difference between KYC and KYB? KYC verifies people; KYB verifies companies. Both are automated compliance processes that collect and analyze data from trusted sources like government registries, credit databases, and sanctions lists to confirm who a customer or business really is. By using KYC and KYB software, financial institutions detect fraud faster, reduce onboarding friction, and meet global AML regulations efficiently. KYC - [Verifies people (customers, individuals)](https://strise.ai/product/id-verification) - Identity checks - Sanctions / PEP / adverse media screening - Risk scoring - Moving from one-off checks to perpetual monitoring (pKYC) - Unlike periodic reviews (e.g., every 1–3 years), pKYC updates risk profiles continuously based on new signals. KYB - Verifies companies (entities, businesses) - Legal existence & registration - [Ultimate Beneficial Owners (UBOs)](https://strise.ai/product/ubo-discovery) & controlling parties - Sanctions / PEP / adverse media screening - Risk scoring - Moving from one-off checks to perpetual monitoring (pKYB) - pKYB means company profiles are automatically refreshed when ownership, jurisdiction, or sanction status changes. Both [KYC and KYB](https://strise.ai/product/kyb-onboarding) are moving from point-in-time checks to event-driven, perpetual monitoring across the customer lifecycle. ##### Why does a risk-based approach to KYC/KYB matter? A risk-based approach (RBA) means calibrating effort to actual risk, rather than treating all customers the same. - Define “good enough”: policies, standards, and playbooks that translate to the front line. - Use data smartly: combine internal activity with external sources. - Automate the routine: let analysts focus on exceptions and high-risk cases. ###### Benefits - Customer experience: lighter touch for low-risk customers means fewer drop-offs. - Operational efficiency: avoid drowning in manual reviews and alerts. - Brand & ESG: doing business with the right customers protects reputation and reduces board-level risk. “We don’t know the criminals as well as we should; we’re always lagging behind. A checkbox mentality doesn’t cover the risk-based approach that the regulators emphasise,” notes an expert in [Strise’s AML Megaminds Report](https://strise.ai/the-laundry) ##### What does good KYC/KYB look like? ###### 1. Start with standards, not tools Before buying technology: - Define required data points (per entity type). - Set verification rules (what, how, when). - Map the full lifecycle: onboarding, due diligence, monitoring, offboarding. Without this, technology amplifies chaos. ###### 2. Map reality, then break into phases Diagnose first: - Policies & procedures: where are the gaps? - Customer data health: what’s missing? - Process gaps: onboarding, periodic reviews, offboarding. Then prioritize manageable phases (e.g., fix the data you collect when a customer first signs up, [remediate high-risk segments](https://strise.ai/guides/kyc-remediation), automate monitoring). ###### 3. Build for perpetual KYC/KYB [Perpetual KYC/KYB](https://strise.ai/product/ongoing-due-diligence) means your system ingests changes (e.g., new UBO, sanctions event, registered address shift) and interprets: - Non-material change means log, no action. - Material change means auto-trigger review, request info, or escalate. Requires: - Signal detection (internal + external data). - Central risk model (consistent interpretation). - Automation (pre-filled outreach, audit trails). ###### 4. Solve the hard part: data Even in transparent jurisdictions: - Registries differ in coverage, format, freshness, and access. - Businesses must standardize postcodes, company numbers, names, addresses, ownership models. Expect: - Many integrations with edge cases. - Long tail of rules (the last 20% is hardest). - AI is powerful, but still depends on trusted reference data. In our Laundry podcast episode, Martin Walker emphasised: “The quality of the data is the single biggest challenge financial institutions have in making sense of their customers.” Martin Walker ###### 5. Orchestrate the humans - First line wants growth. - Second line wants control. - IT is attached to legacy systems. Treat change management as a first-class workstream: clear governance, incentives, and communication. ##### How do mature compliance teams keep improving? Two axes of improvement: - Effectiveness: catching the right risk earlier. - Efficiency: less effort to get there. How leaders improve: - Better data fusion (registries + ownership + activity + media). - Clearer explainability (why risk changed, what was done). - Removing copy-paste work between disconnected tools. ##### What are the best KYC and KYB software tools in 2026? There is no single best KYC/KYB tool. Most compliance teams combine a trusted data provider (registry, ownership and sanctions data) with a platform that automates screening, risk scoring and perpetual monitoring, and an identity verification layer for onboarding. The vendors below are grouped by the job they do. Note: The following overview of top KYC/KYB tools is for context only. It is not endorsed by Martin Walker. ###### Data providers - Sayari – commercial risk intelligence, ownership and trade mapping. - Kyckr – direct-from-registry company data. - LexisNexis – sanctions, PEPs, adverse media, historical data. - Dow Jones Risk & Compliance – sanctions/PEP/adverse media datasets. - Refinitiv (LSEG World-Check) – industry-standard sanctions/PEP/adverse media dataset. ###### AI platforms - [Strise](https://strise.ai) – AML automation that runs on trusted data. Used by leading enterprises such as PwC and Corpay. - Lucinity – AI-powered case management with behavior analytics. - Spektr – modular compliance agents and no-code workflows. - Bretton – AI agents for screening, due diligence, and monitoring. - Bits Technology – compliance orchestration, no-code workflow builder. - Duna – AML/KYC/KYB platform with daily screening. ###### Identity verification and onboarding - GBG – identity verification, KYB/UBO checks and AML screening in one platform, covering 195+ countries. - iDenfy – all-in-one identity verification platform that offers KYC/KYB/AML compliance solutions. - Sumsub – global ID & business verification, AML screening. ###### Case management and lifecycle - Fenergo – enterprise CLM, onboarding through reviews. - WorkFusion – AI workflow automation for compliance tasks. - Napier – modular AML suite (screening, monitoring, risk scoring). - SmartSearch – UK-based AML/KYC/KYB provider. - Salv – orchestration and institution-to-institution intelligence sharing. ##### FAQ What is the difference between KYC and KYB? KYC (Know Your Customer) verifies individuals and their identity, while KYB (Know Your Business) verifies companies, their ultimate beneficial owners, and their legitimate business purpose. Both are essential to prevent financial institutions from becoming vehicles for money laundering. Why are financial institutions moving to perpetual KYC monitoring? Traditional periodic KYC reviews miss changes in customer risk between review cycles. Perpetual KYC monitors customers continuously, detecting adverse media, sanctions designations, or beneficial ownership changes in real-time, enabling faster regulatory response. How should organizations evaluate KYC/KYB software vendors? Compare coverage of data sources, speed of verification, integration with onboarding workflows, support for perpetual monitoring, and pricing structure. The right vendor should balance comprehensive coverage with usability. Why is ongoing KYB monitoring important after initial onboarding? Companies change ownership structures, enter new high-risk jurisdictions, or experience changes in beneficial ownership that elevate risk. Ongoing monitoring catches these changes quickly, preventing institutions from servicing customers who later become high-risk or sanctioned. What are the best KYC and KYB software tools in 2026? The strongest KYC/KYB stacks in 2026 combine three layers: trusted data providers such as Sayari, Kyckr, LexisNexis, Dow Jones and LSEG World-Check; AI platforms such as Strise, Lucinity and Spektr that automate screening, risk scoring and perpetual monitoring; and identity verification and lifecycle tools such as GBG, iDenfy, Sumsub and Fenergo. The right mix depends on your customer base, jurisdictions and how much of the review work you want automated. What is the difference between a KYC data provider and a KYC/KYB platform? A data provider supplies the reference data, such as company registries, beneficial ownership, sanctions, PEP and adverse media lists. A KYC/KYB platform uses that data to run the process: collecting information, screening, scoring risk, monitoring for changes and keeping an audit trail. Most teams need both, because automation is only as good as the data underneath it. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### More control, less noise: Customise your sanctions matching | Strise Blog URL: https://strise.ai/blog/customise-your-sanctions-matching Strise now lets compliance teams customize sanctions matching criteria, aligning flagged results with company risk policy for better workflow control. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Screening #### More control, less noise: Customise your sanctions matching Published on: December 5, 2024 Updated on: December 5, 2024 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents Tailor sanctions matching to your risk appetite How it works Why it matters With this latest update, team managers have control over how Strise matches sanctions alerts , based on your company’s risk policy, not just default thresholds. ##### Tailor sanctions matching to your risk appetite Instead of casting a wide net and filtering later, you can now define: - How strict or broad you want your sanctions matching to be - What match percentages Strise uses when flagging entities - How alerts are triggered across your team This means you’re no longer bound to one-size-fits-all risk thresholds. You’re in control of what gets flagged. ##### How it works Head to sanctions settings and you can: - Adjust match percentage thresholds (e.g. name similarity) - Test and experiment with data results in real time - Apply chosen thresholds across your entire team Once you set the match confidence level you’re happy with, the system will use that logic across your workflows, keeping things consistent, auditable, and aligned with your internal policy. ##### Why it matters This update empowers compliance teams to: - Reduce false positives without missing real threats - Make sanctions screening more aligned with your policy - Fine-tune controls as your risk appetite evolves Data and workflows working together—that’s the Strise effect. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Cut the noise: Smarter Adverse Media Screening (AMS) | Strise Blog URL: https://strise.ai/blog/cut-the-noise-smarter-adverse-media-screening-ams Strise links entities to official registry records, adds precise AMS filtering, and streamlines navigation to make adverse media screening faster. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Screening #### Cut the noise: Smarter Adverse Media Screening (AMS) Published on: August 6, 2025 Updated on: August 6, 2025 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents Tune your AMS. Surface only what matters When the registry isn’t there yet, keep moving More helpful navigation: recently visited pages One platform, fully in sync Adverse Media Screening (AMS) often feels like sifting through noise. Too many compliance teams waste time wading through irrelevant hits, outdated stories, or untrusted sources, all while trying to spot real red flags. That’s why we’ve launched the new AMS Settings page in Strise. It gives you direct control over your AMS filters, helping your team focus on what matters and move faster. ##### Tune your AMS. Surface only what matters The new AMS Settings page allows you to: - Filter by language : Align screening with your key markets by selecting only the languages that matter. - Control the timeframe : Limit your lookback period to remove old, irrelevant articles from your view. - Exclude irrelevant topics : Tired of hits on unrelated subjects? Exclude specific topics entirely. - Curate your sources : Remove publishers that don’t meet your trust or relevance standards. Result: fewer false positives, faster risk assessments, and clearer focus for your team. ##### When the registry isn’t there yet, keep moving Adverse Media Screening is just one part of the onboarding picture. Often, you’re screening companies that don’t yet exist in an official registry. Maybe it’s a startup in formation. Maybe it’s a cross-border case with incomplete records. Either way, you can now start the KYB process in Strise without waiting. You’ve always been able to create custom entities in Strise to get a head start on: - KYB onboarding - Ownership mapping - PEP/sanctions screening - Risk documentation Now, once that company appears in a registry, you can link your initial work to the official profile, in a single action. This means: - No duplication - No audit gaps - A continuous thread from placeholder to permanent record ##### More helpful navigation: recently visited pages One last update: we’ve added a “recently visited” view to help you stay oriented. Need to jump back into a previous investigation or entity? Just click, no more retracing your steps manually. ##### One platform, fully in sync Whether you’re: - Fine-tuning your Adverse Media Screening - Onboarding a company under formation - Or jumping between KYB cases and views Strise helps you reduce noise, preserve context, and accelerate AML workflows. ##### FAQ How can organizations reduce false positives in adverse media screening? AMS filtering settings allow screening by language, timeframe, topics, and sources. This precision filtering eliminates irrelevant matches dramatically reducing false positives without compromising risk detection. What filtering options should adverse media systems provide? Effective systems should allow filtering by language, publication timeframe, topic categories, and source quality. These controls transform adverse media from noise into signal. Why should adverse media screening filter by topic? Not all negative news carries compliance risk. A company involved in litigation is different from one sanctioned or involved in financial crime. Topic filtering lets analysts focus on risk-relevant adverse media while ignoring noise. How does source filtering improve adverse media screening? Limiting screening to credible sources improves signal quality. Organizations can configure systems to prioritize established news outlets, regulatory announcements, and official watchlists while filtering low-quality sources that generate irrelevant matches. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### New: Enhanced editing capabilities | Strise Blog URL: https://strise.ai/blog/edit-2-0 Strise adds comment functionality to UBO map edits, improving collaboration and reporting while streamlining AML workflows for compliance teams. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Product Update #### New: Enhanced editing capabilities Published on: December 5, 2024 Updated on: December 5, 2024 Author Thilo Schmidt Head of Product — Strise® Yes, yes, we realise it wasn't that long ago when we released our edit company ownership feature. And while most of Europe took a summer break, our team worked hard on significantly enhancing the editing capabilities of our UBO (Ultimate Beneficial Owner) maps. Now, you can add comments when editing a company's owner. The comments will appear both in your Strise Review and the PDF report. Solving even the tiniest pain point is top priority to us at Strise. We aim to listen closely to our customers and gather intel on how they're using Strise and what could be improved. Our product and tech teams then collaborate to find smart solutions to ease every step of the AML process. Tedious, manual AML processes are a thing of the past. We're delivering an AML intelligent system that is intuitive and effective. We have a lot more to share about what we've been up to, watch this space. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Faster sanction insights, without the clicks | Strise Blog URL: https://strise.ai/blog/faster-sanction-insights-without-the-clicks Get faster access to detailed sanction information. Strise's latest update shows exactly why a company or person has been sanctioned, fewer clicks needed. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Screening #### Faster sanction insights, without the clicks Published on: November 18, 2024 Updated on: July 3, 2024 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents What’s new Designed for sanctions. Built for speed Quick access to accurate information is essential for making smart compliance decisions. Strise’s latest update now lets you view detailed sanction metadata instantly, just by hovering . You no longer need to open new windows or dig through multiple pages. Simply hover your cursor over any sanction alert , and the key metadata will appear on screen, immediately. ##### What’s new - Instant metadata access via hover - Hover on any sanction alert to get structured, contextual information without leaving your workflow. - No more digging - Skip the manual search. Everything you need is available with one gesture. - Context at a glance - See why a person or company is sanctioned, like control type, ownership details, country, and more. This feature is designed to keep analysts moving quickly—without sacrificing precision or traceability. ##### Designed for sanctions. Built for speed The metadata was always in Strise, but now it’s surfaced where and when you need it most . Whether you’re screening a new client or reviewing a flagged entity, this update makes it easier to: - Make decisions faster - Reduce false positives - Stay fully audit-ready Sanctions are complex. Strise makes them clear. When you can access relevant details in context, your team spends less time chasing fragmented data and more time acting on it. This update supports our broader goal: helping compliance teams fight financial crime more efficiently —by giving them the right data at the right time . Informed decisions prevent money laundering and stop financial crime attempts before they escalate. This feature makes those decisions easier to make. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Formue chooses Strise to end manual KYB | Strise Blog URL: https://strise.ai/blog/formue-chooses-strise-to-end-manual-kyb Formue partners with Strise to automate KYC and AML workflows, speed up onboarding, and improve risk assessment for complex wealth management clients. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Partnership #### Formue chooses Strise to end manual KYB Published on: December 1, 2025 Updated on: December 1, 2025 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents About Formue About Strise OSLO, Norway – Formue , the largest independent wealth manager in the Nordics, today announced a new partnership with [Strise](https://strise.ai), an AI company focused on helping financial institutions automate the fight against money laundering. This collaboration allows Formue to overhaul its client verification and financial crime risk assessment processes by replacing time-consuming manual KYC workflows with Strise’s automation platform. Wealth managers face a unique challenge: their clients’ financial situations are often highly complex. High-net-worth individuals typically own wealth through multiple holding companies and complex family structures, creating a maze of paperwork for compliance teams. Historically, ensuring compliance in this environment requires meticulous work. Analysts need to piece together complex ownership structures, verify beneficial owners, and keep information up to date across multiple entities. These processes are thorough, but they demand significant time and resources, time that could have been spent focusing on the real risks. Formue will use Strise’s technology to solve this problem. Strise’s platform uses smart risk mapping to instantly connect all parts of a client’s world. It automatically gathers and checks information against global lists of sanctioned individuals and politically exposed people (PEPs), as well as other risk signals, providing Formue with a complete and immediate risk score. This allows the firm to promptly approve straightforward, low-risk cases while automatically flagging complex cases that warrant a deeper review. This move will significantly speed up the entire process, from the initial client meeting to the completion of all necessary checks. "Our clients often have complex financial lives, and providing exceptional service means having a due diligence process that is both robust and efficient,” said Asia Holt, Head of Financial Crime Prevention at Formue. "The manual work involved in mapping corporate structures and aggregating risk across an entire network was a major bottleneck. By integrating Strise, we are empowering our team to focus their expertise on true financial crime risks, rather than manual data collection. This partnership allows us to future-proof our compliance, ensuring that we meet regulatory requirements while delivering a great experience for our clients.” _"Wealth management firms are at the forefront of the fight against sophisticated financial crime, and their operations demand a level of intelligence that old, legacy systems simply cannot provide,” _commented Marit Rødevand, CEO of Strise . "Formue is demonstrating a forward-thinking approach by adopting Strise. We are providing them with the intelligence to instantly map complex relationships and keep an always-on watch over their entire client portfolio. This not only drives significant efficiency, cutting the time for simple checks, but fundamentally strengthens their defense against financial crime across the Nordic market.” ##### About Formue Formue is a pioneer in independent wealth management in the Nordics. The company enjoys high client satisfaction, offers award-winning technology, and provides a breadth and depth of advisory services that are unique in the markets where it operates. Formue manages NOK 180 billion in assets under advisory and management and invests across most public and private markets and asset classes. ##### About Strise [Strise](https://strise.ai) provides regulated businesses with automated anti-money laundering processes and makes compliance teams more efficient. We combine automation, AI and a user-friendly design that strengthens KYC processes, ongoing due diligence and automated onboarding. Strise stands out by solving both the data foundation and the workflow in one solution. Customers report up to a 90 percent reduction in time spent on previously heavy and manual AML work. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Fremtind chooses Strise to streamline customer case handling and enhance compliance efficiency | Strise Blog URL: https://strise.ai/blog/fremtind-chooses-strise Fremtind adopts Strise's AML automation platform to streamline Enhanced Due Diligence and standardise compliance in Norway's insurance sector. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Partnership #### Fremtind chooses Strise to streamline customer case handling and enhance compliance efficiency Published on: December 5, 2024 Updated on: December 5, 2024 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents Fremtind adopts Strise to enhance AML case handling and EDD efficiency Why this matters Leadership comments Strise’s momentum About Fremtind About Strise ##### Fremtind adopts Strise to enhance AML case handling and EDD efficiency Strise, a leading provider of Anti-Money Laundering (AML) automation solutions, has announced that Fremtind, a major player in the Norwegian insurance sector, has become a client. Fremtind’s corporate markets team will use Strise Case to enhance efficiency, reduce workload, and standardise outputs, especially for customer cases requiring Enhanced Due Diligence (EDD). This will free analysts from time-consuming manual work and allow them to focus on higher-value decision-making. ###### Why this matters By implementing Strise Case, Fremtind will: - Streamline case-handling processes. - Standardise outputs across teams. - Maintain consistency and compliance in EDD cases. ###### Leadership comments Mariann Fløten , Head of AML Corporate Markets at Fremtind: “Becoming a Strise client marks a significant step forward in our approach to AML compliance. Strise will allow us to better streamline our customer case handling processes, ensuring our team can handle Enhanced Due Diligence cases more efficiently.” Marit Rødevand , CEO of Strise: “We’re thrilled to welcome Fremtind as a Strise customer. The ability to automate and standardise customer due diligence processes, especially in EDD cases, is a game-changer for many organisations.” ###### Strise’s momentum This new client win comes after Strise’s Atomico-led $10.8M Series A in September 2023. The company now has nearly 50 employees, an expanded data model covering over 1 billion international entities and 11 million private individuals, and a new London office. ###### About Fremtind Fremtind is Norway’s largest provider of property and casualty insurance through banks, serving private individuals, agricultural clients, and businesses. Website: fremtind.no ###### About Strise Strise’s AML Automation Cloud powers KYC, KYB, sanctions and PEP screening, ongoing due diligence, and automated onboarding. Clients report: - 90% reduction in due diligence time (days to minutes) - 30% reduction in cost Website: [strise.ai](https://strise.ai) ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### How to clear your KYC remediation backlog before AMLA supervision begins | Strise Blog URL: https://strise.ai/blog/how-to-clear-your-kyc-remediation-backlog-before-amla-supervision-begins Most KYC remediation projects stall because they treat a data problem as a headcount problem. Here's how to clear your back book in weeks, not quarters. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Remediation #### How to clear your KYC remediation backlog before AMLA supervision begins Published on: April 27, 2026 Updated on: June 24, 2026 Author Miguel Ochoa Growth and GTM engineer — Strise® Table of contents Table of contents Why most remediation projects stall What automated KYC remediation actually looks like The numbers that matter What this means for AMLR readiness Next steps KYC [remediation](https://strise.ai/product/remediation) is the process of reviewing, updating, and correcting customer files so they meet your current data standard and regulatory requirements. For most banks, it means reopening thousands of cases, identifying what's missing or outdated, and getting the right documentation in place before a regulator asks why it wasn't done sooner. The pressure is real. The EU's Anti-Money Laundering Regulation (AMLR) applies from July 10, 2027. AMLA is finalising guidelines on business-wide risk assessments through 2026, with final text expected in Q4. A separate RTS on how supervisors will score the risk profile of regulated firms applies from December 31, 2027, and it pulls from the same data points obliged entities are expected to assess themselves on. Translation: supervisors are going to score you on the quality of your customer data. If your back book still contains stale profiles, [missing UBO chains,](https://strise.ai/product/kyb-onboarding) or risk scores that haven't been recalculated since onboarding, you have a window to fix it. That window is closing. The problem is not that compliance teams don't know their data is stale. The problem is that traditional remediation approaches don't scale. ###### Why most remediation projects stall A [KYC remediation](https://strise.ai/guides/kyc-remediation) project at portfolio scale is not a compliance task. It is a programme. And most programmes fail for the same reasons. Scope is discovered, not defined. Without systematic gap analysis, teams start with the most visible cases and discover the true scope only as they work through the pile. Three months in, the backlog looks bigger than it did on day one. Outreach falls on the wrong people. Document collection defaults to whoever owns the client relationship. That means senior relationship managers spending weeks on administrative follow-up instead of the work that actually needs their judgment. Prioritisation is backwards. Without portfolio-wide ranking by risk and gap severity, effort concentrates where it's easiest, not where the regulatory exposure is highest. The most urgent cases wait longest. Velocity is linear. Manual remediation scales with headcount. The backlog grows faster than most teams can clear it, especially when day-to-day compliance work doesn't stop just because a remediation project started. Documentation is an afterthought. Remediated files still need a proper audit trail. When that trail is assembled manually after the fact, the quality reflects whatever time was left at the end of the project. ###### What automated KYC remediation actually looks like The shift is simple in principle: instead of analysts finding and assembling data for every case, the platform does that work, and analysts make decisions on pre-prepared files. Here's what that means in practice. ###### 1. Every file compared. Every gap surfaced Automated remediation starts by comparing every entity in your portfolio against your current data standard and live registry data. Missing documents, outdated ownership information, stale risk scores, all surfaced before a single analyst opens a case. This is the step that transforms a remediation project from a multi-quarter slog into a structured, prioritised programme. The scope is defined by data on day one. ###### 2. Cases ranked by risk, not by alphabet Once gaps are identified, cases are ranked by regulatory exposure, [risk score](https://strise.ai/product/risk-engine), and gap severity. Your team works the highest-risk cases first, not the ones that happen to be at the top of a spreadsheet. ###### 3. Client outreach runs itself When data is missing, the right form goes out automatically. ID requests, UBO declarations, source of wealth documentation, whatever your policy requires. Submission is tracked in real time. Follow-up runs automatically until the form is completed. Your team doesn't chase. They review what comes back. ###### 4. AI reads the documents. Not your analyst When documents arrive, AI [classifies, extracts, and validates the data](https://strise.ai/product/document-analysis) automatically. Updated information flows into the case file without manual re-keying. The analyst opens a pre-prepared case, reviews the changes, and makes the decision. ###### 5. The audit trail builds as the project runs Every remediation action is logged from the first comparison run. What was checked, what was found, what was updated, and by whom. Progress reporting shows completion rates, outstanding cases by risk tier, and gap resolution status. The trail is complete before anyone asks for it. ###### 6. Remediation ends. Monitoring begins This is the part most projects miss. Entities that have been remediated should flow directly into [continuous monitoring](https://strise.ai/product/ongoing-due-diligence), so the back book doesn't rebuild itself by the time the next review cycle arrives. If your remediation ends with a clean file that sits untouched until the next periodic review, you're building a future remediation project right now. ###### The numbers that matter The comparison and case preparation work that typically takes months runs in days when automated. Processing happens in parallel, so ten thousand entities take the same elapsed time as one hundred. The bottleneck shifts from data gathering to analyst decisions, and since cases arrive pre-prepared and ranked by priority, the backlog clears faster than a traditional programme allows. For teams that have run remediation projects with Strise: - Portfolio-wide gap analysis completed in days, not months - Automated outreach replaces manual document chasing - Cases ranked by actual risk exposure, not by order received - Full audit trail generated automatically, not assembled retrospectively ###### What this means for AMLR readiness The AMLR is built on a risk-based approach. Your business-wide risk assessment, your customer due diligence, and your ongoing monitoring all depend on the same thing: customer data that is accurate, current, and defensible. A back book full of stale profiles and undocumented risk decisions is exactly the kind of finding that triggers deeper supervisory scrutiny once direct supervision begins. For a wider view of how the KYC and KYB tooling landscape is shifting toward perpetual, event-driven monitoring, see our [practitioner's guide to the best KYC and KYB software tools in 2026](https://strise.ai/blog/best-kyc-kyb-software-tools). The firms that will be ready are the ones treating remediation as an infrastructure problem, something the platform handles, not as a project that gets staffed, run, and then repeated two years later. If your team is still scoping the problem, the clock is running. Spotify sets its own cookies, so this player stays off until you allow functional cookies. You can change that in your [cookie preferences](https://strise.ai/cookie-policy). Open cookie preferences ##### Next steps If your firm is facing a remediation backlog, or if your periodic review cycle has turned into a perpetual backlog, here's where to start. [Book a meeting with Strise →](https://strise.ai/book-a-meeting-with-strise) ##### FAQ What is KYC remediation? KYC remediation is the process of reviewing, updating, and correcting existing customer files so they meet current regulatory standards and internal data requirements. It typically involves identifying gaps in documentation, ownership structures, and risk scores across a portfolio of existing clients. Why do KYC remediation projects take so long? Most remediation projects stall because they rely on manual processes that scale linearly with headcount. Common blockers include undefined scope that grows as teams work through cases, document collection falling to relationship managers instead of dedicated workflows, and cases being worked in order received rather than ranked by risk exposure. What is back book remediation? Back book remediation refers to the process of bringing legacy customer files up to current compliance standards. The back book is your existing portfolio of clients onboarded under older policies or data standards that no longer meet regulatory expectations. How does automated KYC remediation work? Automated remediation compares every entity in a portfolio against current data standards and live registry data simultaneously. Gaps are surfaced, cases are ranked by risk, client outreach is triggered automatically, and incoming documents are processed by AI. Analysts review pre-prepared cases and make decisions rather than gathering data manually. What does the new EU AML framework mean for remediation deadlines? The EU Anti-Money Laundering Regulation (AMLR) applies from July 10, 2027, and is built on a risk-based approach that depends on accurate, current customer data. AMLA, the new EU supervisor, is finalising guidance through 2026 and will start direct supervision of selected firms from 2028. Stale profiles, incomplete UBO chains, and outdated risk scores in your back book are the kind of findings that will draw supervisory scrutiny once the new framework is live. Firms that wait until enforcement begins will be remediating under pressure rather than on their own timeline. How do you prevent the back book from going stale again after remediation? By flowing remediated entities directly into continuous monitoring instead of waiting for the next periodic review cycle. Event-driven monitoring detects ownership changes, adverse media, and registry updates in real time, so customer files stay current without requiring another remediation project. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### The reality of identity verification in compliance | Strise Blog URL: https://strise.ai/blog/identity-verification-kyb Strise connects IDV directly to KYB, automating verification, reducing drop-offs, and centralising compliance workflows in one secure platform. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - IDV #### The reality of identity verification in compliance Published on: April 2, 2025 Updated on: June 12, 2026 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents Manual steps slow you down and add risk Embed identity verification into KYB Strise IDV: built for seamless compliance No more copy-paste workflows Behind the build Let’s be honest. Identity verification is still a mess in most compliance workflows. Teams jump between KYB platforms, IDV providers, and KYC vendors just to answer a straightforward question: Can we trust this customer? The result? A patchwork of: - Screenshots - PDFs - Handwritten notes - Even WhatsApp messages And most of it barely holds up in an audit. ##### Manual steps slow you down and add risk That patchwork of tools and disconnected steps does more than slow things down. It adds friction, risk, and customer frustration. - 68% of customers drop off during a confusing onboarding process - Operational overhead increases - Growth slows - Teams spend more time verifying identities than assessing risk But it doesn’t have to be that complicated. ##### Embed identity verification into KYB When identity verification is built directly into the KYB process , instead of bolted on as an afterthought: - Teams move faster - Processes stay compliant - Risk is reduced across the board No more re-entering info. No more switching between systems. Just a workflow that works. The real benefit isn’t just speed. It’s confidence. And that’s exactly what we built into our IDV offering. ##### Strise IDV: built for seamless compliance With that objective in mind, Strise offers a unique identity verification function to make compliance seamless. - Built in partnership with Signicat , Europe’s leader in digital identity and fraud prevention - Fully integrated with Strise, from onboarding to continuous monitoring - Connected and compliant across the AML lifecycle Verifying beneficial ownership is often a legal requirement. But handling it outside your compliance flow causes delays and errors. Combining IDV and KYB keeps onboarding consistent , compliant , and scalable . ##### No more copy-paste workflows Strise solves one of the most frustrating parts of compliance: Copy-pasting between disconnected systems. Maybe you’re: - Collecting data in one tool - Verifying in another - Maintaining a third as your source of truth - Or verifying the same person multiple times across related companies With Strise’s graph-based model , once someone is verified, they stay verified. - No duplication - No repetition - No wasted effort Whether your setup is basic, fragmented, or still evolving, Strise helps you: - Move faster - Reduce risk - Scale smarter , without the chaos ##### Behind the build Henrik Nårstad, Senior Software Engineer at Strise Strise’s Identity Verification system connects with Signicat’s Mint platform to verify individuals linked to companies in Strise. The state machine architecture ensures verifications follow a strict, auditable process. ###### What happens when a verification is requested: - Creates a new workflow through Signicat - Generates a secure URL that expires after a set time - Tracks status through PendingDocument , PendingReview , Approved , or Declined ###### Technical highlights: - Only authorized users can access verifications - Background status updates prevent workflow delays - Fully integrated with Strise’s company and person data - Workflow URLs expire automatically for security - Clear separation between API layer , service layer , and storage layer for easier maintenance and faster feature delivery ##### FAQ What impact does identity verification have on customer conversion? Approximately 68% of customers abandon the onboarding process when encountering complex identity verification requirements. This creates a critical tension between strong IDV for compliance and friction that drives customer drop-off and revenue loss. Why is identity verification integration into KYB critical? IDV embedded directly into KYB workflows reduces friction by eliminating separate, sequential verification steps. Integrated IDV captures biometric and document data once and uses it throughout the onboarding process, improving completion rates while meeting compliance requirements. What does Signicat provide for identity verification? Signicat is a leading identity verification provider offering biometric matching, document validation, and global coverage across multiple identity sources. Integration with Signicat enables institutions to offer reliable IDV that works for diverse customer types. What compliance standards does identity verification need to meet? IDV must meet KYC requirements for customer authentication, AML risk assessment rules, and jurisdiction-specific regulations that often require document-based verification. The challenge is meeting these standards without creating enough friction that customers abandon the process. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### KLP joins the growing list of financial institutions connecting with Strise's AML Automation Cloud | Strise Blog URL: https://strise.ai/blog/klp-connecting-with-strises-aml-automation-cloud KLP joins Strise’s AML Automation Cloud with Stacc to automate AML processes, cut manual work, and unify compliance data. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Partnership #### KLP joins the growing list of financial institutions connecting with Strise's AML Automation Cloud Published on: December 5, 2024 Updated on: December 5, 2024 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents Benefits of the collaboration Leadership statements Context and growth About KLP About Strise Oslo, 13 November – Strise, an Anti-Money Laundering (AML) automation company, announces that KLP , one of Norway's largest pension fund managers, is now a customer. KLP will implement Strise’s solutions together with Stacc to enhance AML processes. This will help KLP manage customer measures more efficiently and reduce manual work. ###### Benefits of the collaboration - Improved AML data processes – By connecting to Strise’s AML Automation Cloud, KLP can unify relevant AML data into one up-to-date system. - Workflow efficiency – Analysts will spend less time on repetitive tasks and more on decision-making. - Risk reduction – Strise’s AI and graph models support better workflow management and data accuracy. ###### Leadership statements Marit Rødevand , CEO and co-founder of Strise: “We are thrilled to have KLP as a customer. They are one of Norway’s most respected financial institutions, and KLP is also our first public sector client. Their commitment to operational excellence aligns perfectly with our mission to fight financial crime through innovative technology.” Thomas Nielsen , SVP of Financial Crime at KLP: “The solution from Strise, in combination with Stacc, will help manage customer measures more efficiently and improve customer experience. It will automate processes that were previously partially manual and allow better reuse of customer data across the company.” ###### Context and growth This collaboration follows Strise’s recent partnerships with Storebrand , Vipps MobilePay , and PwC . Since raising $10.8M in Series A funding led by Atomico in September 2023, Strise has: - Grown to nearly 50 employees - Expanded data capabilities to cover 1 billion international entities and 11 million private individuals ###### About KLP - Name: Kommunal Landspensjonskasse - Sector: Pension management for public sector employees - Members: Over 900,000 - Assets under management: Over NOK 1,000 billion - Employees: 1,000+ - Ownership: Municipal and healthcare employers; all profits returned to owners - Website: www.klp.no ###### About Strise Strise’s AML Automation Cloud provides: - KYC and KYB - Sanctions and politically exposed person (PEP) screening - Ongoing due diligence - Automated onboarding of retail and corporate clients Impact metrics: - 90% reduction in due diligence time (days to minutes) - 30% cost reduction ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Are low-risk KYB onboarding cases clogging your pipeline? | Strise Blog URL: https://strise.ai/blog/low-risk-kyb-cases-clog-your-pipeline Automate onboarding screening with risk-based prioritisation. Strise sorts KYB and AML queues so high-risk cases get reviewed first, automatically. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - KYB #### Are low-risk KYB onboarding cases clogging your pipeline? Published on: May 28, 2025 Updated on: December 14, 2025 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents The hidden cost of treating every screening the same Start with risk, not randomness The right cases, in the right order Real impact, not just time saved ##### The hidden cost of treating every screening the same Many compliance teams still review every onboarding case the same way. Full review. Same queue. Same process. Low risk, medium risk, high risk. All mixed together. As onboarding volumes grow, this creates quiet but serious problems. Low risk customers eat up analyst time. High risk alerts get buried. Clean cases slow everything down. Analysts spend time sorting instead of solving. Nothing is missed. But the wrong work happens first. ##### Start with risk, not randomness The smartest teams flip the order. They start with risk. Strise helps compliance teams automatically prioritize onboarding queues using real risk signals. Not ticket order. Not gut feel. Not manual sorting. That means you can: - Review the riskiest cases first - Automatically handle low risk cases based on your rules - Flag complex or unclear cases for immediate attention Every case is still screened. They are just handled in the right order. ##### The right cases, in the right order Think of it like airport security. Everyone is screened. But not everyone stands in the same line. Higher risk passengers are routed differently. Trusted travelers move faster. Security focuses where it matters most. That is what automated prioritization does for onboarding. This is not just queue sorting. It is workflow level decision making. Because it is fully configurable, you stay in control: - Set your own risk thresholds - Build logic around your team’s expertise - Route the right cases to the right people ##### Real impact, not just time saved You cannot scale your team every time onboarding spikes. But you can make sure the time you already have is spent on real risk. Some Strise customers automate KYB reviews for millions of entities each year. Analysts focus on judgment calls instead of routine checks. For regulated businesses, automating low risk onboarding leads to: - Lower operating costs - Faster response to real risk - A smoother customer experience when most competitors still treat everyone the same Less noise. More signal. Better outcomes. ##### FAQ How should KYB onboarding queues be prioritized? Prioritize high-risk prospects first while automating low-risk cases through configurable risk thresholds. This risk-based approach ensures analysts focus on genuinely complex cases rather than processing everyone identically. What low-risk cases can be automated in KYB onboarding? Prospects meeting predefined low-risk criteria such as established companies in low-risk jurisdictions with standard ownership structures can be automatically approved without manual review. This accelerates pipeline velocity while maintaining compliance. Why do KYB platforms need configurable risk thresholds? Different businesses have different risk appetites and regulatory requirements. Configurable thresholds let your organization define what low, medium, and high-risk look like based on your specific risk policy, industry, and jurisdiction rather than forcing a one-size-fits-all approach. What causes KYB onboarding queues to back up? Manual review of low-risk cases consumes analyst capacity without adding risk mitigation value. Pipeline clogs form when all cases receive identical scrutiny regardless of risk profile. Automating routine approvals while reserving human judgment for complex cases eliminates this bottleneck. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### UI: New Settings modal | Strise Blog URL: https://strise.ai/blog/new-settings-modal Stop repeating the same compliance checks on a schedule. Strise automates KYB, KYC, screening, and monitoring. Your analysts handle the judgement work. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Product Update #### UI: New Settings modal Published on: November 18, 2024 Updated on: July 3, 2024 Author Thilo Schmidt Head of Product — Strise® There's been a bit of spring cleaning behind the scenes in the Strise system 🧼 You might've already noticed it but we're excited to announce that Settings has had a refresh. Now taking up less space, the new Settings modal has been streamlined to provide you with an even more intuitive user experience. You'll find all of the same Settings options as before, only now, they're even easier to navigate and understand. Whatever tweaking you need to do to your account, the upgraded Settings modal makes it a lot easier to do. Enjoy the new look and feel! Get in touch if there's anything else we can do to make your Strise experience even better. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### PEP and sanctions screening and dispositioning, done right | Strise Blog URL: https://strise.ai/blog/pep-and-sanctions-screening-and-dispositioning-done-right Structured, auditable PEP and sanctions review, with Agentic AI making autonomous, explainable decisions. Faster reviews, smaller backlog, more time for high-value cases. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Screening #### PEP and sanctions screening and dispositioning, done right Published on: April 30, 2025 Updated on: April 30, 2025 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents Context, clarity, and confidence in screening From augmented to autonomous Why agentic AI is different AI that executes, not just assists What Strise agentic AI does (Beta) PEP and sanctions screening has always been noisy. Most systems flag matches, but stop short of giving users the context and control to make confident calls. We’ve taken that foundation and built something better. ##### Context, clarity, and confidence in screening Strise’s upgraded dispositioning gives teams complete control over each hit : - See what a hit is, why it’s flagged, and what to do next - Review fuzzy matches with transparent scoring (name similarity, date of birth, country, and more) - Confirm or reject a hit with a full audit trail - Document your decision and export for regulators - Track system-suggested vs. user-confirmed outcomes at a glance This is more than flagging. It’s verification with context . ##### From augmented to autonomous Since launching the new dispositioning feature, AML teams have: - Reduced false positives - Structured decision-making - Documented actions for audit-readiness But as alert volumes grow and pressure on L1 teams increases, a new question keeps surfacing: Can Agentic AI do this for me? That’s the challenge we’ve taken on in the Strise Product Lab, testing a new way to handle PEP and sanctions alerts using Agentic AI (Beta) . ##### Why agentic AI is different There’s a lot of hype around AI in compliance right now. But here’s the thing: Not all AI is built the same. Most tools are passive. They offer: - Suggestions - Summaries - Scores Agentic AI acts. ##### AI that executes, not just assists Agentic AI doesn’t just assist, it autonomously executes decisions using: - Predefined logic - Real-time data - Risk context It behaves more like a digital team member than a tool. Where traditional AI says, “Here’s a score.” Agentic AI says, “Here’s what I did—and why.” ##### What Strise agentic AI does (Beta) - Automatically reviews and dispositions PEP and sanctions alerts - Justifies each decision with human-readable reasoning - Updates outcomes , logs actions, and escalates edge cases to humans This creates a new layer that handles high-volume alerts quickly and consistently—while your team focuses on the edge cases that require human insight Currently in beta with a small group of early testers, this feature is designed to: - Reduce manual workloads - Ensure transparent audit trails - Enable faster, consistent remediation Adrian Trzeciak, Head of Engineering at Strise “Our goal with autonomous dispositioning is to let compliance teams focus on judgement calls, while the system handles the rest with speed and reliability.” ##### FAQ How does fuzzy matching improve PEP screening accuracy? Fuzzy matching accounts for spelling variations, transliteration differences, and name order variations that exact-match filters miss. By displaying match confidence scores, it enables analysts to quickly distinguish high-confidence matches from potential false positives. What is agentic AI in sanctions screening and how does it help? Agentic AI automatically evaluates match confidence, contextual data, and disposal rules to make autonomous decisions on clear matches. This Level 1 automation eliminates manual review of obvious matches, freeing analysts to focus on genuinely ambiguous cases. Why is contextual information important in sanctions screening? Raw name matches generate false positives. Contextual dispositioning uses available data like date of birth, nationality, and entity type to confirm or reject matches, dramatically reducing false positives without missing genuine sanctions hits. What does Level 1 automation mean in sanctions compliance? Level 1 automation refers to fully autonomous decisions on clear-cut cases such as sanctions matches with high confidence scores and no conflicting data. This accelerates case closure while maintaining governance through documented decision rules and audit trails. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### PEP & Sanctions Matching: Fewer false positives | Strise Blog URL: https://strise.ai/blog/peps-matching-one-to-the-other Two reasons why a Strise search yields fewer false positives than any other PEP and sanctions screening tool. Precision matching that actually works. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Screening #### PEP & Sanctions Matching: Fewer false positives Published on: November 18, 2024 Updated on: July 3, 2024 Author Thilo Schmidt Head of Product — Strise® Fewer false positives? Yes 🥳 Some of Strise's best features don't always make it to the top of an update page. But we want to let you now two reasons why a Strise search yields fewer false positives than any other search. One of the latest Strise's latest update now extends filters for PEPs by full date of birth (if available), reducing the number of false positives when name and birth years identical. By leveraging full date of birth information, the Strise system can more accurately identify politically exposed people and prevent regulatory compliance issues. We've also improved how we match subsets with regards to names. Many users received false positives when a the full names of potentially sanctioned people differed from list to list. This often happened if an individual's initial or middle name was, or wasn't, included in a sanction list. Strise takes care of those sneaky changes by being intelligent enough to know when initials or middle names are omitted. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### PSA Solutions taps Strise to tackle AML for law firms | Strise Blog URL: https://strise.ai/blog/psa-solutions-partners-with-strise-to-help-law-firms-meet-stricter-aml-requirements PSA and Strise partner to give law firms AML automation within Custodian, unifying due diligence, monitoring, and compliance in one platform. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Partnership #### PSA Solutions taps Strise to tackle AML for law firms Published on: August 20, 2025 Updated on: August 20, 2025 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents Responding to regulator pressure What this partnership enables About PSA Solutions About Strise Strise and PSA Solutions have entered a strategic partnership to help law firms meet growing anti-money laundering (AML) compliance demands. The collaboration brings together PSA’s Custodian Client Onboarding system , used by leading law firms, with Strise , enabling firms to handle complex AML requirements from a single platform. ###### Responding to regulator pressure The Norwegian Advokattilsynet and the UK’s Solicitors Regulation Authority both highlight, in their July 2025 sectoral risk assessments , gaps in source-of-funds checks, opaque ownership structures, and weak ongoing monitoring. The PSA–Strise partnership addresses these issues by providing law firms with a modern toolkit to: - Automate KYB and due diligence : Quickly clear low-risk clients and escalate high-risk ones automatically. - Map ownership and UBOs : Surface complex or cross-border structures to identify beneficial owners. - Strengthen sanctions compliance : Monitor global watchlists and avoid accidental breaches. - Enable continuous monitoring : Get alerts on changes in sanctions, ownership, PEP status, or adverse media. - Assess risk holistically : Unify ownership, sanctions, PEP, media, and network signals into one clear view. ###### What this partnership enables Through this integration, law firms using PSA’s Custodian platform can now access Strise’s AML automation capabilities without switching systems or managing fragmented client data. “Firms face rising expectations around anti-money laundering and sanctions compliance. By connecting Strise’s proven AML technology to Custodian, we provide law firms with a powerful solution to meet these obligations in a more efficient and reliable way.” — Bjørn Tore Aurlien , Senior Director, PSA Solutions “This partnership combines the strengths of two industry leaders. PSA is a trusted provider to law firms, while Strise has pioneered AML automation for financial institutions. Together, we bring this expertise to the legal sector.” — Marit Rødevand , CEO, Strise ###### About PSA Solutions PSA is a Nordic legal tech and consulting company serving over 280 clients across the Nordics. With offices in Oslo, Stockholm, Warsaw, and Helsinki, PSA delivers cloud platforms like Custodian and partners with leading legal tech providers including iManage, DocuSign, and Litera. Learn more at www.psasolutions.com ###### About Strise Strise is a leader in AML automation, trusted by top financial institutions including Corpay, Storebrand, and Nordea. The platform enables faster, more effective compliance by unifying data into living customer profiles across KYB, KYC, onboarding, and risk monitoring. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### PwC Norway selects Strise to automate AML processes, significantly reducing hours of manual work | Strise Blog URL: https://strise.ai/blog/pwc-strise-aml-automation PwC Norway implemented Strise's AML Automation Cloud to streamline compliance, reduce manual work, and boost AML efficiency from day one. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Partnership #### PwC Norway selects Strise to automate AML processes, significantly reducing hours of manual work Published on: February 17, 2025 Updated on: June 26, 2025 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents Reducing manual AML efforts Why this matters Expanding Strise’s reach Key features that set Strise apart Oslo, Norway – Leading AML automation company Strise has signed PwC Norway, the Norwegian member firm of PwC’s global professional services network, as a client. By adopting Strise, PwC Norway will streamline AML operations, reduce costs, and save thousands of hours of manual work, all while strengthening compliance from day one. ##### Reducing manual AML efforts Strise is built for enterprises looking to simplify and accelerate compliance. For PwC Norway, Strise will: - Automate sourcing and triangulation of AML data - Provide pre-populated, analyst-ready insights - Support onboarding, data management, and ongoing due diligence With Strise’s centralised AML platform, PwC Norway replaces scattered vendor tools, serving engagement teams conducting pre-screening through to the risk and quality department managing full KYC and KYB. ##### Why this matters Financial crime costs banks over $2.1 trillion each year. Many organisations still rely on manual AML processes, a model that’s no longer scalable. Strise changes this by: - Unifying data on businesses, individuals, roles, and networks - Automating region-specific data ingestion from third parties - Replacing fragmented workflows with a single source of truth “Securing PwC Norway as a client is a tremendous achievement for Strise. Strise is specifically designed to alleviate the burden of time-consuming and costly AML processes for large organisations like PwC Norway, empowering them to focus on what truly matters, delivering excellence to their clients.” ** — Marit Rødevand, CEO and Co-Founder, Strise ** ##### Expanding Strise’s reach Already powering AML operations for 70% of top-tier Nordic banks, Strise now extends its reach to one of the world’s largest professional services firms. This milestone follows - A $10.8M Series A round led by Atomico - Growth to 50 employees - Opening of a London office and expansion into additional European markets - Platform scale-up to cover over 1 billion entities and 11 million individuals globally - Recognition in Fast Company’s 2024 Most Innovative Companies list About Strise Strise leads the AML automation revolution, transforming compliance from a cost center to a strategic driver. - Automation that removes compliance backlogs - AI-powered insights for faster decision-making - User-centered design for intuitive workflows Its AML Automation Cloud powers: - KYC and KYB - Sanctions and PEP screening - Ongoing due diligence - Automated onboarding for corporate and retail clients ###### The impact: - 90% reduction in due diligence time (from days to minutes) - 30% cost savings for compliance teams ##### Key features that set Strise apart Strise solves the three biggest pain points in AML : - Data fragmentation : unified and continuously updated entity profiles - Hidden risk : detect issues earlier with explainable, rules-based scoring - Alert overload : resolve false positives at scale and reduce analyst fatigue Media Contact: Lars Lunde Birkeland CMO, Strise lars@strise.ai ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Real-time continuous monitoring at portfolio level | Strise Blog URL: https://strise.ai/blog/real-time-continuous-monitoring-at-portfolio-level The Strise Portfolio Health Dashboard gives compliance leaders a real-time view of portfolio risk with automatic AML updates and centralised monitoring. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Product Update #### Real-time continuous monitoring at portfolio level Published on: March 19, 2025 Updated on: August 15, 2025 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents Traditional risk monitoring is broken Designed for fast, confident decisions Behind the build Traditionally, getting a full picture of your customer portfolio risk meant juggling internal data, batch screenings, and business intelligence tools, a slow, fragmented, and resource-heavy process. By the time everything is pieced together, the risk has already changed. Which means remediation is delayed, and exposures are missed. Not anymore. ##### Traditional risk monitoring is broken The Strise portfolio health dashboard gives you a real-time view of your risk exposure , enabling compliance teams to act immediately. - Continuously monitors your portfolio across PEPs, sanctions, RCAs, and overdue high-risk reviews - Reflects any changes in your risk policies instantly - Auto-updates with live global AML data for always-on risk visibility With continuous monitoring built-in, you never fall behind on emerging risk signals. ##### Designed for fast, confident decisions Strise removes blind spots and enables faster KYB remediation : - Risk signals are surfaced in real time - Dashboards provide instant clarity on exposure - Teams can drill down, export reports, and take immediate action This reduces the time between risk detection and resolution, helping you stay ahead of auditors, regulators, and evolving risks. For many compliance teams, this is a shift from reactive workflows to real-time control. ##### Behind the build We designed this like a pilot’s cockpit , every risk indicator in view, every alert flagged the moment it happens. Whether you’re tracking overdue reviews, sanction updates, or PEP exposure, Strise lets you act with confidence. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Real-time ownership editing is here | Strise Blog URL: https://strise.ai/blog/real-time-ownership-editing-is-here Edit UBO structures directly in Strise with real-time updates when ownership changes. Cleaner data, faster decisions, and audit-ready KYB from day one. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - UBO Discovery #### Real-time ownership editing is here Published on: November 18, 2024 Updated on: July 3, 2024 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents The role of UBO maps in business intelligence Introducing edit ownership: update company data in real time When you hear breaking news about a company, you don’t wait a week to find out more, you search immediately. Platforms like Wikipedia update every few seconds, meaning you can expect the latest facts almost instantly. That expectation for up-to-the-minute accuracy should apply to business intelligence too, especially when it comes to company ownership. In regulated industries and high-stakes business environments, outdated information can lead to costly mistakes. ##### The role of UBO maps in business intelligence At Strise, UBO maps (Ultimate Beneficial Owner maps) are a core part of how our customers see and understand corporate structures. They reveal the people and entities who truly control a business, information that’s critical for due diligence, KYB checks, and risk management. But ownership structures change constantly: - Investors join or exit - Subsidiaries merge or dissolve - Corporate control shifts to new hands When these changes aren’t reflected in your system, your data isn’t just incomplete, it can be misleading. That’s why being able to update UBO maps the moment you have new intelligence is so important. ##### Introducing edit ownership: update company data in real time Strise’s new edit ownership feature lets you make immediate, reliable edits to company ownership data without disrupting your workflow. You can: - Edit company ownership structures instantly when changes happen - Add, remove, or adjust ownership links in your UBO maps - Keep all updates in one central, accurate source - Maintain data quality without losing efficiency Whether the update comes from a client conversation, investigative research, or a breaking news alert, you can now act on it immediately. Fast, accurate ownership data enables better decision-making in compliance, investment, and partnership evaluations. With real-time ownership data , you can: - Approve customers with confidence - Spot risk factors before they escalate - Stay aligned with regulatory expectations - Ensure teams across the business work with the same verified information Just like you expect Wikipedia to reflect the latest facts, you can now expect Strise to keep your UBO maps and ownership structures as current as the news cycle. With edit ownership , stale data is no longer a risk factor, and every decision you make is based on the freshest information available. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Signicat and Strise launch unified platform for Digital Identity Verification and KYB automation | Strise Blog URL: https://strise.ai/blog/signicat-strise-unified-digital-identity-verification-kyb-automation Signicat and Strise partner to deliver modular KYC and AML compliance. Streamline onboarding, identity verification, and monitoring with no-code setup. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Partnership #### Signicat and Strise launch unified platform for Digital Identity Verification and KYB automation Published on: February 26, 2025 Updated on: February 26, 2025 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents Eliminate fragmented compliance workflows Built for analysts, compliance teams, and RMs A scalable platform for modern compliance Available now About Strise About Signicat Media Contact London, UK – Strise, the AML automation leader, and Signicat, Europe’s top provider of digital identity and fraud prevention solutions, have entered a strategic partnership to unify digital identity verification and AML automation into a single platform. The result is a modular, end-to-end solution that allows businesses to meet complex compliance requirements, efficiently and at scale. “We’re bringing together Signicat’s trusted identity verification and orchestration platform with Strise’s flexible AML products to create a powerful solution that drives both simplicity and reliability.” — Stein-Olav Davidsen , Chief of SMB and Online Channels at Signicat ##### Eliminate fragmented compliance workflows The partnership integrates Signicat Mint , a no-code platform for digital identity verification, directly into the Strise. Together, the joint solution delivers: - Digital ID verification with support for multiple eID schemes and eIDV providers - Fully integrated KYB and AML data and workflows - Dynamic customer-facing webforms - No-code workflow orchestration for onboarding, authentication, and electronic signatures - End-to-end auditability and control “By joining forces with Signicat, we’re helping businesses navigate AML compliance with ease while reducing the manual work involved.” — Edward Bragg , Strategic Partnerships at Strise ##### Built for analysts, compliance teams, and RMs The solution is designed for: - AML analysts and KYC teams needing structured workflows - Relationship Managers handling onboarding with high regulatory scrutiny - Compliance Officers responsible for maintaining end-to-end oversight With Signicat Mint embedded in Strise, users can tailor workflows, define risk logic, and activate automation, without writing code. ##### A scalable platform for modern compliance This integrated solution gives organisations: - A single view of customer verification and risk status - Configurable settings to meet local regulations and internal policies - Real-time monitoring and faster remediation - Improved auditability and lower cost of compliance Whether for automated onboarding, fraud prevention, or ongoing AML monitoring, the solution simplifies decision-making and adapts to local markets. ##### Available now The Strise x Signicat integrated compliance platform is live and available to new and existing customers. Businesses looking to eliminate inefficiencies, unify fragmented tools, and future-proof their compliance workflows can adopt the solution today. ##### About Strise Strise leads the AML automation revolution, helping compliance teams move from reactive to strategic. The AML Automation Cloud powers: - KYC and KYB onboarding - Sanctions and PEP screening - Ongoing due diligence - Automated monitoring and remediation With Strise, users report: - Up to 90% faster due diligence - 30% lower compliance costs ##### About Signicat Signicat is Europe’s leading digital identity company, trusted by financial institutions, governments, and enterprises. Its platform supports: - Identity verification and e-signatures - Fraud prevention - Authentication and consent journeys Founded in 2006, Signicat now has over 450 employees across 17 European offices. Learn more at www.signicat.com ##### Media Contact Lars Lunde Birkeland CMO, Strise lars@strise.ai ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Storebrand partners with Strise to transform AML operations | Strise Blog URL: https://strise.ai/blog/storebrand-entrusts-strise-to-drive-automation-of-aml-operations Storebrand partners with Strise to integrate AML Automation Cloud across its business units, serving 55,000+ corporate and 2.2M retail customers. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Partnership #### Storebrand partners with Strise to transform AML operations Published on: March 11, 2025 Updated on: March 11, 2025 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents One platform to accelerate compliance at scale AI-driven solution for modern AML The bigger picture: why this matters Strise momentum and market position About Storebrand About Strise Media contact Oslo, Norway – AML automation leader Strise has announced a strategic partnership with Storebrand, one of the Nordic region’s largest financial groups. Storebrand will integrate Strise across its entire portfolio, spanning over 55,000 corporate customers and 2.2 million retail customers, covering insurance, pension, banking, and asset management. ##### One platform to accelerate compliance at scale Strise will enable Storebrand to: - Reduce manual work in data management and onboarding - Streamline ongoing due diligence and KYC/KYB workflows - Centralize AML operations across business units - Enhance analyst productivity through automation and decision-ready data By consolidating fragmented tools into one intelligent platform, Storebrand’s teams, from operational analysts to Group AML, gain control, speed, and visibility. “We are excited to partner with Strise to enhance and automate our AML processes,” said Ine Solberg , Group AML at Storebrand. “Their smart technology aligns with our vision of building trust, improving operations, and fighting financial crime with precision.” ##### AI-driven solution for modern AML This partnership follows Strise’s strategic product shift to API-first infrastructure , giving Storebrand: - Continuous data enrichment from global regulatory sources - A unified, AI-powered graph model combining person, role, and entity data - A future-proof foundation for smart, scalable AML workflows “Securing a deal with an international financial service provider like Storebrand represents a monumental achievement for Strise,” said Marit Rødevand , CEO and Co-Founder of Strise. “Their commitment to sustainable practices aligns with our mission to fight financial crime through technology.” ##### The bigger picture: why this matters According to PwC’s EMEA AML report: - 38% of institutions cite AML pressure as their top challenge - 1 in 3 highlight data quality and manual processes as barriers - Over 50% of asset managers are increasing digital budgets - 94% of Nordic institutions plan to implement AI in AML by 2026 In this landscape, Storebrand’s move positions them at the forefront of automated, AI-driven AML operations ##### Strise momentum and market position Strise now provides AML automation to: - 70% of top-tier Nordic banks - Storebrand, Norway’s largest asset manager - Enterprises across banking, fintech, and professional services This follows: - A $10.8M Series A led by Atomico - Team growth to nearly 50 employees - Opening of a London office and expansion across Europe - A platform scaled to over 1 billion global entities and 11 million individuals - Recognition in Fast Company’s 2024 Most Innovative Companies ##### About Storebrand Storebrand is a leading Nordic financial group offering sustainable financial services to individuals and businesses. With NOK 1,281 billion under management, Storebrand is headquartered in Lysaker, Norway, and listed on the Oslo Stock Exchange . Visit: www.storebrand.no/en ##### About Strise Strise leads the AML automation revolution, transforming compliance teams into high-impact business drivers. The AML Automation Cloud powers: - KYC/KYB onboarding - Sanctions and PEP screening - Ongoing due diligence - Automated monitoring and data enrichment With Strise, customers report: - Up to 90% reduction in due diligence time - 30% lower compliance costs ##### Media contact Lars Lunde Birkeland CMO, Strise lars@strise.ai ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Strise partners with Kyckr to automate KYB beyond document retrieval | Strise Blog URL: https://strise.ai/blog/strise-kyckr-to-automate-kyb-beyond-document-retrieval Strise and Kyckr automate KYB with real-time data from 300+ registries, enabling instant verification and auto-flagging discrepancies globally. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Partnership #### Strise partners with Kyckr to automate KYB beyond document retrieval Published on: March 6, 2025 Updated on: March 6, 2025 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents Most KYB tools stop at document retrieval. Strise doesn’t. Built directly into your KYB workflow One platform. Global reach. Scalable compliance. About Strise About Kyckr Oslo, Norway – Strise has partnered with Kyckr , a trusted provider of real-time registry data, to launch a fully automated KYB verification experience. This integration enables instant access to official records from over 300 global registries , moving KYB workflows from document collection to real-time decision-making. ###### Most KYB tools stop at document retrieval. Strise doesn’t. Traditionally, KYB workflows leave analysts with the manual burden of: - Retrieving documents - Cross-checking declarations - Flagging discrepancies - Chasing missing data With the Strise x Kyckr integration, analysts can now: - Instantly verify entities across jurisdictions - Access registry data in real time - Auto-compare against internal records and declarations - Detect discrepancies instantly This removes delays, cuts onboarding times from days to minutes , and eliminates manual overhead. ###### Built directly into your KYB workflow When a company is searched in Strise, analysts get an auto-populated KYB checklist enriched with harmonised registry data. Now, powered by Kyckr, the platform includes: - Real-time company verification from Companies House (UK) and 300+ global registries - Instant discrepancy detection across filings, ownership, and entity details - One-click updates to sync registry data with internal records All data is verified, auditable, and visible in a single, streamlined workflow. “Instead of just collecting documents, we’re connecting the dots—giving analysts instant clarity on who they’re doing business with. This is KYB without the guesswork.” — Edward Bragg, Partner Lead, Strise ###### One platform. Global reach. Scalable compliance. For businesses operating in multiple jurisdictions, this partnership means: - No more switching tools - No more manual verification - No more missed discrepancies Everything happens in one place, with full support for third-party verification requirements across the UK, EU, and beyond. “With Kyckr’s best-in-class registry data and Strise’s KYB solutions, we’re making compliance faster, more accurate, and truly automated.” — Steve Lamb, Chief Operating Officer, Kyckr ###### About Strise Strise leads the AML automation revolution, transforming compliance from a cost center into a business enabler. With real-time workflows and data-driven insights, Strise powers: - KYB and KYC - PEP and sanctions screening - Perpetual due diligence - Retail and corporate onboarding ###### About Kyckr Kyckr provides real-time, legally authoritative company data from over 300 global sources , helping businesses: - Reduce fraud - Maintain compliance - Eliminate outdated corporate records Explore more at www.kyckr.com ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Strise and Stacc join forces to replace manual AML with scalable automation | Strise Blog URL: https://strise.ai/blog/strise-stacc-replace-manual-aml-with-scalable-automation Strise and Stacc join forces to replace manual AML with scalable automation, combining advanced data intelligence with user-focused compliance tools. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - Partnership #### Strise and Stacc join forces to replace manual AML with scalable automation Published on: December 5, 2024 Updated on: December 5, 2024 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents How the integration works Why this partnership matters Market context and growing need About Stacc About Strise Oslo, Norway – Strise and Stacc have formed a strategic partnership to reshape how financial institutions combat money laundering and financial crime. The collaboration brings together: - Strise’s AML Automation Cloud , powered by real-time data, AI, and unified entity models - Stacc’s AML & KYC Management Portal , trusted by 280+ financial institutions across the Nordics By integrating these systems through API, the partnership delivers seamless, analyst-ready AML data directly into customer-facing compliance tools—removing friction and enabling faster, more effective decision-making. ###### How the integration works This joint solution enhances efficiency across the compliance lifecycle: - Strise consolidates global AML data into a single, analyst-ready format using advanced entity resolution - Stacc ensures streamlined case handling through automated workflows, digital signing, and real-time alerts Together, they offer: - Pre-linked, continuously updated AML data - API-driven workflows that reduce manual tasks - End-to-end support from customer onboarding to ongoing due diligence “Financial crime is not just a regulatory issue—it’s a global crisis,” “Together with Stacc, we’re delivering solutions that help institutions meet their compliance goals and fight financial crime more effectively.” — Marit Rødevand, CEO, Strise ###### Why this partnership matters Financial crime fuels corruption, terrorism, and organised crime. Yet many institutions struggle with: - Fragmented data sources - Outdated manual workflows - Siloed compliance systems This partnership addresses these root problems through: - Integrated, no-touch data enrichment - Analyst-ready insights directly in the KYC/AML portal - Collaborative innovation from two leading Nordic compliance tech providers “By combining our strengths, we empower institutions with tools that are smart, fast, and built for modern AML demands,” — Philip Røer, Managing Director, AML, Stacc ###### Market context and growing need A 2024 PwC report on EMEA AML found: - 38% of financial institutions see AML compliance as their biggest challenge - 33% cited data quality and manual processes as their top pain points - 94% of Nordic institutions are actively looking to adopt AI in AML workflows - Over 50% of asset managers plan to increase investment in digital tools before 2026 The Stacc–Strise partnership responds to this demand with a ready-to-deploy, API-first infrastructure tailored to the Nordic market and scalable globally. ###### About Stacc Founded in 2016, Stacc delivers software solutions to 280+ financial institutions across the Nordics. With 250 employees and offices in Bergen, Oslo, Stockholm, Skövde, and Aarhus , Stacc provides AML & KYC management tools known for combining legal compliance with operational efficiency . Features include: - Automated workflows - Digital signing - Triggers and notifications - Integrated case management - Customer self-service portals Learn more: www.stacc.com ###### About Strise Strise is leading the AML automation revolution. Its AML Automation Cloud powers: - KYC and KYB - UBO mapping - PEP and sanctions screening - Ongoing due diligence - Automated onboarding of corporate and retail clients By consolidating AML data and workflows in one solution, Strise enables: - Up to 90% reduction in due diligence time - 30% cost savings - Faster, AI-powered decisions ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### The hidden owner. The clear UBO map | Strise Blog URL: https://strise.ai/blog/the-hidden-owner-the-clear-ubo-map Strise centralises UBO discovery in one structured view so teams spend less time verifying ownership and more time assessing risk. No backtracking. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - UBO Discovery #### The hidden owner. The clear UBO map Published on: June 11, 2025 Updated on: December 14, 2025 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents Why is UBO discovery still difficult in KYB? How does Strise simplify UBO discovery? How does Strise simplify UBO discovery? Why does minor shareholder visibility matter for UBO discovery? How does Strise make UBO identification clearer? How does Strise improve KYB decision making? How do custom entities work inside Strise? Is Strise ownership data available via API? ##### Why is UBO discovery still difficult in KYB? UBO discovery remains difficult in KYB because ownership data is fragmented, incomplete, and often missing key relationships. Teams using KYB processes spend hours reconciling registries just to answer who actually controls an entity. UBO discovery breaks down when registries omit indirect ownership, minor shareholders, or non standard control structures. UBO discovery failures create compliance risk because unclear control leads to weak risk assessments. KYB teams often rely on manual workarounds to fill gaps, which slows reviews and increases error rates. A reliable UBO discovery process must handle missing data without breaking the workflow. ##### How does Strise simplify UBO discovery? Strise simplifies UBO discovery by automatically surfacing beneficial owners and control relationships in one ownership map. The ownership map shows companies, individuals, and connections in a single structured view. UBO discovery becomes visual instead of investigative Strise ownership mapping works even when corporate structures are complex or indirect. UBO discovery no longer depends on stitching together external documents or spreadsheets. Teams see control clearly and can act faster. ##### How does Strise simplify UBO discovery? Strise simplifies UBO discovery by automatically surfacing beneficial owners and control relationships in one ownership map. The ownership map shows companies, individuals, and connections in a single structured view. UBO discovery becomes visual instead of investigative. Strise ownership mapping works even when corporate structures are complex or indirect. UBO discovery no longer depends on stitching together external documents or spreadsheets. Teams see control clearly and can act faster. ##### Why does minor shareholder visibility matter for UBO discovery? Minor shareholder visibility matters because control risk often hides below reporting thresholds. UBO discovery improves when all ownership links are visible, not just majority stakes. Strise includes minor shareholder data directly in the ownership map. This expanded view helps teams spot subtle patterns that indicate hidden control. UBO discovery becomes more accurate because indirect influence is no longer invisible. Risk signals emerge earlier. ##### How does Strise make UBO identification clearer? Strise makes UBO identification clearer by prominently highlighting Beneficial Owners in the ownership map. UBO discovery becomes faster because key individuals stand out immediately. No manual calculations are required. Each highlighted UBO includes hover details explaining why they are flagged. UBO discovery is supported by clear ownership percentages and control types. Decisions are easier to justify during review and audit. ##### How does Strise improve KYB decision making? Strise improves KYB decision making by replacing fragmented ownership data with a structured network view. UBO discovery happens in one system, not across emails and spreadsheets. Teams move faster with more confidence. The ownership map is ready for review, export, and audit at any time. UBO discovery outputs remain consistent across compliance workflows. This reduces rework and second guessing. ##### How do custom entities work inside Strise? Custom entities in Strise behave like any other company in the platform. UBO discovery stays consistent even when entities are manually created. There is no downgrade in functionality. Custom entities support full KYB and KYC review flows. Ownership roles, risk documentation, and audit trails are all preserved. UBO discovery remains defensible and traceable. ##### Is Strise ownership data available via API? Strise ownership data is available via API for automated workflows. UBO discovery results can flow directly into downstream systems. Teams avoid manual exports. API access ensures ownership structures remain synchronized across tools. UBO discovery becomes part of a broader compliance infrastructure rather than a one off task. ##### FAQ Why is discovering ultimate beneficial owners so difficult? UBO discovery is hard because beneficial ownership information is fragmented across multiple registries with incomplete or outdated data. Complex ownership structures using shells, trusts, and nested entities further obscure true beneficial owners. What value does visual ownership mapping provide? Visual mapping translates complex ownership chains into understandable diagrams showing all direct and indirect ownership relationships. This clarity enables faster analysis, identifies hidden risk exposure, and provides documentation regulators can easily validate. How should compliance systems handle indirect and complex ownership? Systems should map both direct and indirect ownership, tracking chains through shells, trusts, and nominee arrangements. This requires consolidating data from multiple registries and intelligently resolving ownership across jurisdictions. What challenges does fragmented UBO registry data create? Each jurisdiction maintains different registries with varying data quality and accessibility. UBO information in one jurisdiction might be incomplete or outdated, requiring cross-referencing against multiple sources, making comprehensive beneficial owner discovery manual and time-intensive without specialized tools. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### The problem no one talks about in KYB | Strise Blog URL: https://strise.ai/blog/the-problem-no-one-talks-about-in-kyb Strise transforms KYB from slow manual checks into real-time verification, connecting to global registries and flagging discrepancies automatically. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - KYB #### The problem no one talks about in KYB Published on: March 5, 2025 Updated on: August 13, 2025 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents Real-time KYB verification: go beyond document retrieval Instant discrepancy detection = faster case resolution All-in-one verification. No more switching tools. The future of KYB: risk awareness, not paper collection Know Your Business (KYB) checks are the backbone of AML compliance. But for many compliance analysts, the reality is far from efficient. Most KYB tools promise efficiency but fall short. They retrieve documents, sometimes neatly organized, but leave analysts with the heavy lifting: - Cross-checking incorporation records and declarations - Verifying company details manually - Spotting discrepancies and data gaps - Looping in other teams when data doesn’t match This creates friction. Analysts juggle emails, multiple systems, and manual checks, costing hours of productivity and increasing compliance risk. There’s a smarter way. ##### Real-time KYB verification: go beyond document retrieval What makes Strise different. Strise goes beyond document retrieval. It provides real-time KYB verification , delivering analyst-ready data from day one. When you search for a company in Strise, you get a pre-populated KYB checklist , automatically enriched from thousands of trusted records. That includes registry data, ownership structures, documents, and more, all harmonized into a single profile. ###### Registry integrations: 300+ sources, real-time access - UK: Verified instantly via direct connection to Companies House - EU: Unified access to over 300 global registries - All markets: Real-time checks, verified against the latest data Strise ensures you always work from current, verified information, no more digging through PDFs or outdated registry snapshots. ##### Instant discrepancy detection = faster case resolution With one click, Strise pulls fresh data from local registries and compares it to your existing records. It flags: - Mismatched addresses - New beneficial owners - Incomplete filings or gaps Your team sees the issues immediately and can take action, no bottlenecks, no manual rework. This reduces false positives and improves decision speed during onboarding and ongoing monitoring. ##### All-in-one verification. No more switching tools. Strise brings everything into one platform: - Registry data - Verification checks - Discrepancy detection - KYB checklists - Internal notes and decisions Your analysts don’t need to jump between systems. Compliance becomes scalable and efficient across teams and jurisdictions. ##### The future of KYB: risk awareness, not paper collection KYB isn’t about ticking boxes. It’s about understanding risk, identifying bad actors early, and scaling your compliance operations without scaling your headcount. ##### FAQ What's the gap between KYB document retrieval and actual verification? Most KYB tools retrieve corporate documents but leave analysts to manually verify the information against what the customer provided. This creates a completion gap where documents are obtained but not truly verified, consuming analyst time without adding assurance. How do real-time registry connections improve KYB? Connecting directly to 300+ registries enables instant verification rather than document-based checking. This provides current information, catches discrepancies immediately, and eliminates the manual matching work analysts previously had to do. Why is instant discrepancy detection important in KYB? When a customer claims one beneficial owner but registry records show another, immediate detection triggers further investigation. Delayed discrepancy discovery wastes time and creates compliance gaps; instant detection prevents problems from extending onboarding timelines. How can KYB systems reduce analyst workload? By automating document retrieval and immediately verifying information against authoritative sources, analysts focus on exception handling rather than routine matching. This increases efficiency and allows time to be allocated to genuinely complex or high-risk cases. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Top AML software for banks in 2026: AI‑native platforms comparison | Strise Blog URL: https://strise.ai/blog/top-aml-software-for-banks-in-2025-ai-native Comparing six AI-native AML platforms for banks in 2026: Strise, Silent Eight, Arva, Duna, Spektr, and Greenlite AI. Features, strengths, and trade-offs. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - AML #### Top AML software for banks in 2026: AI‑native platforms comparison Published on: June 12, 2026 Updated on: June 26, 2026 Author Miguel Ochoa Growth and GTM engineer — Strise® Table of contents Table of contents 1. Strise 2. Silent Eight 3. Arva AI 4. Duna 5. Spektr 6. Bretton Best AI AML software for banks AML AI compliance software 2026 Top AI anti money laundering solutions for financial institutions AML AI tools bank AML Why choose Strise Summary: What to look for in 2026 As financial crime grows more complex, and as we move into 2026, banks need modern, AI powered platforms to automate compliance workflows, from onboarding to monitoring to alert resolution. This guide looks at AI native AML solutions for 2026, each suited to different operational needs. ###### The most modern AML platforms share six core capabilities: - AI-powered automation and agentic AI : Advanced AI systems go beyond static rule-based logic. They autonomously handle onboarding, monitoring, and alert resolution, freeing analysts to focus only on exceptions and complex reviews. - Real-time monitoring : Surveillance must happen as events unfold. Real-time monitoring enables immediate risk detection across jurisdictions, which is critical in today’s fast-moving financial landscape. - AI-driven risk scoring : Modern platforms use AI to dynamically evaluate risk and reduce false positives. Automated scoring engines prioritize real risks, improving both case handling speed and decision quality. - Unified data and seamless integration : Leading tools harmonise registry data, filings, sanctions lists, documents, internal data, media and more into a single view. Enterprise-grade APIs ensure smooth integration into banking and fintech workflows. - Explainability, governance, and audit traceability : AML systems must generate clear, auditable decisions. Explainable AI, full event logs, and compliance with frameworks like the EU AI Act are critical for trust and regulatory approval. - Proven ROI and enterprise adoption : The strongest vendors show clear results: faster onboarding, fewer false positives, and lower compliance costs. They’re also deployed across banks, fintechs, and regulated enterprises at scale. ##### 1. Strise ###### — Where data powers automation Why it stands out: Strise places trust in data above all. By building living entity profiles through its proprietary data model , it merges registries, internal notes, and documents into a single source of truth. That foundational data enables intelligent AI policies, faster due diligence, and seamless automation. - Impact: Up to 90% faster onboarding, 30% cost reduction, and 40% less false positives, with enterprise-grade audit readiness. Strise is used by leading enterprises such as PwC Norway, Corpay, Storebrand, and Nordea. ##### 2. Silent Eight ###### — Explainable AI for alerts Why it stands out: Silent Eight combines agentic AI with human oversight to deliver auditable, transparent decisions in AML and sanctions screening. - Impact: Resolved over 100 million alerts, saving $19 million in costs with full explainability. ##### 3. Arva AI ###### — Governed AI at scale Why it stands out: Arva automates up to 92% of AML, KYC, and KYB reviews using agentic AI, all while meeting compliance standards across FATF, FCA, MAS, and more. - Impact: Supports deep due diligence and adverse media analysis across 150+ countries with explainable, regulated automation. ##### 4. Duna ###### — Business onboarding, elevated Why it stands out: Duna transforms KYB with intuitive, no-code workflows and design thinking. Supporting over 210 integrated registries, it delivers identity verification, AML screening, and e-signatures, all in a sleek, conversion-optimized flow. - Impact: One client saw onboarding drop from eight days to two hours, with a 38% boost in conversion. ##### 5. Spektr ###### — Compliance, your way Why it stands out: Spektr 2.0 brings modular AI agents and no-code orchestration, empowering fintechs to build customized human-AI workflows. - Impact: Automates real-time onboarding and monitoring with instant risk triggers, all without coding. ##### 6. Bretton ###### — Automated alert resolution engine Why it stands out: Greenlite deploys AI agents to clear up to 95% of routine AML alerts, reducing compliance backlogs and improving efficiency without sacrificing regulations. - Impact: Clients report massive gains in speed, alert resolution and case handling are faster and more accurate. ##### Best AI AML software for banks AML AI compliance software 2026 By 2025, the gap is obvious. Legacy AML tools struggle with scale, speed, and data quality, while AI native platforms handle complexity by default. The best AI AML software for banks focuses on end to end workflows, not isolated checks. That means onboarding, transaction monitoring, risk scoring, and alert resolution all connected through a shared data layer. Banks choosing platforms like Strise, Silent Eight, or Arva are doing so because these tools reduce noise, surface real risk earlier, and stay explainable under regulatory scrutiny. The real differentiator is not just AI, but how well that AI is grounded in clean, unified, and auditable data. ##### Top AI anti money laundering solutions for financial institutions AML AI tools bank AML Top AI anti money laundering solutions for financial institutions go beyond automation for automation’s sake. They help compliance teams move faster without losing control. Modern AML AI tools for banks combine real time monitoring, adaptive risk models, and transparent decision logic that regulators can actually follow. Solutions like Strise and Spektr give institutions flexibility to tailor workflows, while tools like Greenlite focus heavily on clearing routine alerts at scale. The strongest platforms balance efficiency with trust, proving that AI can reduce manual work, improve decision quality, and still meet the growing demands of global AML regulation. ##### Why choose Strise Strise is the only AML platform built on the belief that data is the foundation of automation . Most systems bolt AI onto fragmented or outdated sources. Strise does the opposite: it starts with clean, unified data, merging official registries, analyst notes, filings, documents, and live updates into a trusted living profile for every entity. This trusted data layer powers advanced AI and automation without sacrificing accuracy or auditability. Strise applies configurable rules and AI to automate onboarding, risk scoring, and monitoring, with full context and transparent logic behind every decision. ###### Strise is ideal for banks that want: - A single source of truth across KYC, KYB, UBO, and monitoring - Earlier risk detection using AI + graph analytics - Seamless automation with API-first architecture - Continuous enrichment of customer data - Explainable, regulator-ready outputs for audits and the EU AI Act ###### Proven impact: - 90% faster onboarding - 30% lower compliance costs - 40% fewer false positives - Adopted by PwC Norway, Nordea, Storebrand, Corpay and other leading financial institutions ##### Summary: What to look for in 2026 Banks in 2026 need more than point solutions. The best AML platforms combine agentic AI, real-time monitoring, unified data, and transparent governance. They reduce manual work, surface risk earlier, and meet global compliance demands. From Strise’s graph-powered automation to Silent Eight’s explainable AI and Arva’s scalable governance, each vendor offers distinct strengths. The right platform depends on your institution’s maturity, data foundation, and automation goals. If you want automation you can trust, driven by trusted data, explainable risk scoring, and seamless integration, Strise is the most complete and future-ready AML engine for banks today . ##### FAQ What makes an AML platform AI-native? AI-native AML platforms are built with machine learning as a core capability, not a bolt-on feature. They use AI for transaction monitoring, case prioritization, alert tuning, and decision support rather than relying on static rules, enabling continuous learning and adaptation. What core capabilities should all AML platforms provide? Essential AML capabilities include real-time transaction monitoring, sanctions and PEP screening, adverse media monitoring, case management workflows, and reporting for regulatory filing. Platforms differ in how they combine and prioritize these. What sets Silent Eight apart in the AML platform market? Silent Eight emphasizes explainability and governance in AML investigations, providing clear reasoning for system decisions. This focus on transparency and analyst support makes it valuable for organizations that prioritize regulatory defensibility and audit effectiveness. What is Arva's emphasis in the AML platform space? Arva focuses on transaction monitoring and alert quality, using AI to reduce false positives and surface genuine suspicious patterns. Their approach emphasizes immediate investigation triage to maximize analyst efficiency on meaningful cases. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Why AML and CTF compliance really matter right now | Strise Blog URL: https://strise.ai/blog/why-aml-and-ctf-compliance-really-matter-right-now AML compliance is stricter than ever. Learn how automation, AI, and Strise help financial institutions meet EU/UK rules, reduce risk, and build regulator trust. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - AML #### Why AML and CTF compliance really matter right now Published on: October 13, 2025 Updated on: October 13, 2025 Author Lars Lunde Birkeland CMO at — Strise® Table of contents Table of contents Key obligations in the Nordics UK AML and CTF compliance essentials Why manual compliance puts you at risk How automation changes the entire compliance game What modern AML automation actually does What regulators now expect from your organization How Strise makes AML simple again Go beyond workflows and screening Unify and enrich compliance data Power trusted AML decisions with SuperData™ Find what’s missing before it becomes a risk Collect and verify, all in one flow Accelerate compliance with faster time-to-value Stay ahead of regulations with SuperData™ Regulators have stopped being patient. AML and CTF rules now come with higher penalties and tighter oversight, so banks, fintechs, and brokers can’t afford sloppy compliance. The old way: spreadsheets, manual checks, scattered systems, just doesn’t scale. To keep up, financial institutions are turning to automation: real-time monitoring, AI-driven analytics, and built-in reporting that actually satisfies regulators. In the Nordic countries (Denmark, Norway, Sweden, Finland, Iceland), AML/CFT compliance is mandatory, not optional. Financial institutions must embed a full compliance framework across all operations. ###### Key obligations in the Nordics Just like in the EU, Nordic jurisdictions require institutions to: - Verify every customer’s identity (KYC / CDD). Obliged entities must collect identity documents, verify beneficial owners, and perform due diligence at onboarding and periodically. (General principle across EU and mirrored in Nordics.) - Assign risk levels to clients (risk scoring). Clients must be categorized by their ML/TF risk, triggering enhanced checks where risk is higher. - Continuously monitor transactions. Transactions must be screened (e.g. for anomalies, patterns) in real time or near-real time, with alerts for suspicious behavior. - Report suspicious activity (SAR / STR). When transactions or patterns raise suspicion, institutions must submit reports to national Financial Intelligence Units (FIUs). - Report threshold-based transactions. Transactions exceeding certain monetary thresholds (or crossing risk triggers) may require mandatory reporting. - Maintain full records, audit trails, and verifiable data. All decisions, risk assessments, transaction histories, and compliance actions must be stored in an auditable, traceable manner for the legally required retention period. Every compliance step must be data-driven. Mistakes or gaps can result in fines, regulatory sanctions, reputational harm, or operational restrictions. Also in the UK AML and CTF compliance is mandatory and built on the Money Laundering Regulations 2017, the Proceeds of Crime Act, the Terrorism Act, the Sanctions and Anti Money Laundering Act, and FCA rules. Firms must run a documented, risk based program and follow sector guidance from JMLSG. ###### UK AML and CTF compliance essentials - Verify every customer’s identity (CDD). Identify and verify all customers and beneficial owners at onboarding and beyond. ( Legislation.gov.uk , JMLSG) - Assign clear risk levels. Apply a risk based approach and use enhanced due diligence for higher risk clients. (JMLSG) - Monitor activity continuously. Scrutinize transactions to ensure they align with the customer’s profile and flag anomalies. ( GOV.UK ) - Report suspicious activity (SAR). Submit reports to the National Crime Agency when you suspect money laundering or terrorism financing. ( UKFIU.gov.uk ) - Apply checks at set thresholds. Perform due diligence for transfers over €1,000 and cash deals above €10,000. ( Legislation.gov.uk ) - Keep full records. Retain identification and transaction data for at least five years to support audits. ( Legislation.gov.uk ) - Follow regulator guidance. Meet FCA expectations and align with JMLSG best practices. (FCA, JMLSG) - Comply with sanctions law. Screen and control transactions under the UK Sanctions and Anti Money Laundering Act. ( Legislation.gov.uk ) Every step must be data driven and traceable. Failures risk fines, sanctions, and reputational damage. ##### Why manual compliance puts you at risk Manual AML work isn’t just slow, it’s risky. Teams juggling spreadsheets and emails can’t see the full picture. Risk scoring becomes subjective, large transactions get missed, and real-time alerts simply don’t happen. Reporting turns into a tedious, error-prone mess. The result? Higher costs, regulatory red flags, and in extreme cases, suspended licenses. ###### How automation changes the entire compliance game Automating AML makes compliance faster, more consistent, and a lot less painful. Systems analyze every transaction in real time, spot suspicious behavior instantly, and generate regulator-ready reports. The payoff: fewer errors, lower costs, and stronger audit outcomes. - Faster execution – Automated tools check transactions in seconds, not hours. - More accuracy – Standardized rules remove human bias and cut false positives. - Audit-ready by default – Every action is logged, every report is traceable. - Scales effortlessly – Grows with your business without extra headcount. - Earns regulator trust – Data-backed compliance builds confidence and reduces scrutiny. ###### What modern AML automation actually does Today’s platforms handle everything compliance teams dread. They profile customers, score risks, monitor transactions in real time, and file reports automatically. They also: - Screen continuously against PEP and sanctions lists. - Centralize documentation for internal and external audits. - Unify previously disconnected systems into one live compliance engine. It’s not just automation, it’s an entire compliance infrastructure that never sleeps. ###### What regulators now expect from your organization “Checklist compliance” doesn’t cut it anymore. Regulators want proof that your AML setup is data-driven, documented, and repeatable. They expect quick, accurate suspicious activity reports and systems that match your company’s complexity. ##### How Strise makes AML simple again Strise builds AI native compliance systems for banks and fintechs across Europe. It connects to PEP and sanctions lists, corporate registries, and transaction systems to create a live 360° risk view for every client. ###### Go beyond workflows and screening Most AML tools stop at process automation. Strise powers the data foundation that keeps AI and compliance automation reliable across teams. ###### Unify and enrich compliance data Entity resolution harmonizes fragmented data into verified, single customer profiles. AI fills in missing pieces using client forms, documents, and public records. ###### Power trusted AML decisions with SuperData™ Unified and enriched data flows through every automated workflow, ensuring each risk assessment and alert is backed by accurate, traceable information. ###### Find what’s missing before it becomes a risk Strise’s AI scans registries, news, and filings to uncover hidden links and missing context. Verified insights flow back into customer profiles automatically. ###### Collect and verify, all in one flow Strise automates form requests, document uploads, and ID verification for company representatives and shareholders, creating a complete, [auditable KYC trail](https://strise.ai/product/kyc-onboarding). ###### Accelerate compliance with faster time-to-value Strise delivers over 70% faster time-to-value through a ready-to-deploy solution built for demanding AML deadlines. It sources region-specific, best-in-class data from trusted third parties, so you don’t have to. ###### Stay ahead of regulations with SuperData™ New data points are added continuously to meet evolving regulatory standards. Connecting to the Strise SuperData™ gives institutions instant access to compliant data now and in the future, without complex data-sourcing work. With Strise, compliance teams can detect risk faster, maintain clean audit records, and scale AML operations with confidence. ##### FAQ How has the regulatory environment for AML changed recently? Regulators globally have become less patient with compliance failures and imposed significantly higher penalties. Financial institutions face escalating enforcement expectations, making AML compliance a critical business risk with penalties that impact profitability. What are the key AML/CTF obligations in Nordic and EU jurisdictions? Nordic and EU institutions must conduct KYC, implement risk scoring, perform transaction monitoring, and file Suspicious Activity Reports. These requirements are non-negotiable and subject to frequent regulatory examination with enforcement actions increasingly common. Why is customer risk scoring mandatory in AML compliance? Risk scoring enables proportionate compliance effort. High-risk customers receive deeper monitoring while low-risk customers clear faster, optimizing both compliance effectiveness and operational efficiency. Regulators require documented risk scoring methodologies. What SAR reporting standards must financial institutions meet? Institutions must identify suspicious activity, investigate it appropriately, and file SARs with financial intelligence units within regulatory timeframes. Documentation of SAR decisions is critical for regulatory defensibility. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Your KYB shouldn’t break when data does | Strise Blog URL: https://strise.ai/blog/your-kyb-shouldnt-break-when-data-does Create and review companies without registry data. Strise keeps KYB structured, audit-ready, and traceable even when public records are unavailable. - [Home](https://strise.ai)/ - [Blog](https://strise.ai/blog)/ - KYB #### Your KYB shouldn’t break when data does Published on: May 14, 2025 Updated on: August 20, 2025 Author Thilo Schmidt Head of Product — Strise® Table of contents Table of contents How Strise keeps KYB moving API-ready for real-world edge cases In a perfect world, every company you need to onboard would show up in a public registry. But every KYB team knows that’s not reality. Sometimes you’re handed a company name and: - It’s not in the system - It’s a newly created entity - It’s a startup that hasn’t been fully registered - It’s a cross-border case with no accessible data And yet, you still need to: - Screen the company - Assess its risk - Document and defend every decision Before Strise, that often meant workarounds, spreadsheets, and waiting for registry updates. ##### How Strise keeps KYB moving Now, the entire process stays in Strise, structured, reviewable, and audit-ready from day one . You can create and work with companies that don’t exist in any official registry , treating them like any other entity in the platform. This means you can: - Add ownership structures and roles - Assign and complete KYB/KYC reviews - Document risk decisions - Track every step with a full audit trail Custom-created entities behave exactly like standard companies in Strise, with no blind spots, no detours, and no missing audit trail . ##### API-ready for real-world edge cases And yes, this capability is fully available via API . Not every compliance case starts with perfect data. By enabling teams to handle hard-to-verify edge cases directly in Strise, whether in-platform or through the API, you can: - Eliminate friction - Preserve traceability - Keep your KYB process running without delays No registry or available data? No problem. You can still know your business. ##### FAQ How should compliance systems handle companies not in official registries? Effective KYB systems need mechanisms to handle edge cases through custom entity creation, supporting documentation submission, and risk assessment based on available information rather than failing the entire KYB process when registry data is unavailable. What role do custom entities play in comprehensive KYB? Custom entity creation allows analysts to document companies when registry data is incomplete or unavailable. Systems should preserve audit trails showing what information was verified, what was customer-provided, and what was supplemented through alternative sources. Why should KYB systems be API-ready for edge cases? API-ready KYB systems allow custom integrations for specialized verification sources, internal databases, or third-party data providers specific to your customer base. This flexibility ensures the system adapts to your business requirements rather than forcing rigid workflows. How should audit trails document non-standard KYB cases? Comprehensive audit trails must document verification sources for every piece of information whether from official registries, alternative sources, customer submission, or supplemental research. This demonstrates due diligence was performed even for non-standard cases. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ## Product ### KYB onboarding and automated business verification — Strise URL: https://strise.ai/product/kyb-onboarding Strise uses AI to automate KYB onboarding, pulling data, mapping ownership, and scoring risk before an analyst opens the case. New clients onboarded in hours. - [Home](https://strise.ai)/ - Product / - KYB onboarding #### Onboard new clients in hours, not days. Routine cases clear automatically. Complex ones arrive ready to decide. [Book a demo](https://strise.ai/book-a-meeting-with-strise) KYB icon - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Sound familiar? 01 80% of your cases are low-risk. They’re all hitting L1 review Manually reviewing every case isn’t diligence, it’s overhead. While your team works through the routine pile, the complex cases that actually need attention are waiting. 02 You’re still chasing documents by email. It’s taking 3–5 days per case Three follow-ups. Expired passports. Missing articles of association. Every manual request burns time on both sides. 03 UBO structures take hours to untangle, especially across borders Foreign registries, multiple languages, missing data. What should take minutes takes hours, and that’s before you question whether what you found is actually current. 04 Your case lives in four places at once, nobody has the full picture Screening in one platform, UBO in another, documents over email, notes in a CRM. Decisions get made on whatever’s visible, not whatever’s true. 05 When a regulator asks, the answer is in a Word doc and a prayer When an examiner asks how you reached a decision, you open a file, scroll through screenshots, and hope the timestamps make sense. That’s not documentation, it’s archaeology. 06 The onboarding started three weeks ago. The case is still open The client submitted what was asked. Your team requested more. The client replied to the wrong email. Someone chased it. The case sat in a queue. Three weeks later it is still not closed. ##### The difference Strise makes. 80% of routine cases can be automated. 30 MIN per complex case, down from 3 hours. 6× more complex cases handled per analyst. Same team. No new hires. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## Analyst-ready case file, automatically. - 1 ###### Application in. Work begins A new business arrives. Strise begins preparing the case file immediately. - 2 ###### All data checked. All at once Company records, filings, ownership, and watchlists fetched simultaneously. - 3 ###### Risk scored. Flags surfaced Risk assessed against your policy. Flags surfaced, everything else moves forward. - 4 ###### File ready. Analyst decides Every check done, every exception flagged. One decision to make. ##### Automate KYB onboarding. ###### UBO mapping across any structure. Multi-layer structures, holding companies, cross-border chains, Strise maps it all automatically. Beneficial owners surface without manual research, document requests, or registry rabbit holes. Ownership diagram showing Bribery Barnes owns 6% (politically exposed person), Corruption Carlson as ultimate beneficial owner with 64%, affecting ownership in Mammal Money Laundering Ltd., Octopus Offshore Holdings, Llama Tax Havens Inc., and Shady Sharks Ltd. ###### Client documents, read and extracted. No re-keying. Registry filings, trust deeds, articles of association, AI reads them, parses the key data, and adds it straight to the case file. You never manually transcribe a number or copy-paste a name again. Screen showing extracted entities from PDF Meridian_Trust_Deed.pdf: Finn Shark Director, Shady Sharks AS Shareholder 10%, Shelly Shark Ultimate beneficial owner 49%, with an Add to ownership button. ###### Low-risk cases close without a human. You define what routine looks like. Strise handles everything that fits the threshold, verified, scored, and closed without your team spending a minute on it. Your analysts only see cases that need them. Risk distribution showing 1,247 entities processed: 47 high risk, 178 medium risk, and 1,022 low risk; with 623 hours saved and 1,022 cases automated. ###### AI prepares the narrative. You make the call. For every case Strise processes, AI generates a plain-language summary, what was found, what risk appeared, and why. Your analysts read context and a recommendation, not a stack of raw data fields to interpret themselves. Summary of KYC and AML risks for Shady Sharks Ltd. highlighting legal issues and transparency concerns. ###### Complete audit trail from day one. Every action logged, every decision documented. When a regulator asks how you reached a decision, you open one file — not five. The case is regulator-ready the moment the review closes. Audit trail list showing recent changes including address change to Rådhusgata 9, industry change to rice growing, new and updated sanctions on owners Sleazy Steve and Bambu Bandit, and additions/removals of beneficial owners by user @luna.lionfish. ###### Your CRM and CLM, connected via API. Strise pulls what it needs, works the case, and pushes results back. Your records stay where they are. Your stack doesn’t change. Your team doesn’t learn a new system. Diagram showing integration of Strise’s risk output and audit trail with your existing CRM and CLM stack via API. ##### Explore other products. - [KYC onboarding](https://strise.ai/product/kyc-onboarding) - [Monitoring (ODD)](https://strise.ai/product/ongoing-due-diligence) - [Identity verification](https://strise.ai/product/id-verification) - [UBO discovery](https://strise.ai/product/ubo-discovery) - [Remediation](https://strise.ai/product/remediation) - [Risk scoring](https://strise.ai/product/risk-engine) - [Screening](https://strise.ai/product/screening-and-alert-triage) - [Document analysis](https://strise.ai/product/document-analysis) ##### More onboardings. With the same team. See how the KYB process works when Strise prepares every case, and what 6× throughput looks like for your team. [Book a demo](https://strise.ai/book-a-meeting-with-strise) ##### Things we get asked. Answered. How does Strise connect to our existing systems? Flexibly. Strise can run as a standalone system, link into yours, or integrate fully via API with your CRM and case management system. You choose the model that fits your stack. Nothing gets replaced. What happens with complex ownership structures? Strise maps the structure automatically, corporate chains, multiple UBOs, cross-border entities. Where registry data has gaps, it surfaces a form so the analyst can fill in what’s missing. The ownership graph gets built either way. Which registries do you cover? Strise integrates data from over 250 jurisdictions globally. Coverage includes 10B+ corporate records, 500M+ companies, 600M+ unique individuals, and 2B+ entity relationships. Where a registry has no digital portal, Strise flags it and gives the analyst a clear path to handle it manually. How long does onboarding actually take? For low-risk cases: hours, not days. Strise runs the checks, scores the risk, and routes straight-through where policy allows. Complex cases still get to the analyst faster because the file arrives complete. What do analysts actually do when Strise handles the case? Review, override, approve. The case arrives with everything resolved and scored. The analyst checks it, can override any part of it, and signs off. No data entry. No chasing documents. No building the file from scratch. How does document collection work? When documents are needed, Strise sends a branded client form — specific to what’s missing, not a generic checklist. Submissions are tracked in real time. Reminders fire automatically. Documents get read and filed on arrival. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### KYC onboarding and automated customer verification — Strise URL: https://strise.ai/product/kyc-onboarding Strise uses AI to streamline KYC onboarding with automated ID checks, risk scoring, and digital forms, so your team focuses on decisions, not data collection. - [Home](https://strise.ai)/ - Product / - KYC onboarding #### Verify more customers. With the same team. Routine cases clear automatically. Exceptions land on the right desk, ready to decide. [Book a demo](https://strise.ai/book-a-meeting-with-strise) KYC icon - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Sound familiar? 01 80% of your cases are low-risk individuals. Your team is reviewing all of them Simple cases sit in the same queue as complex ones. The analyst reviews them, finds nothing, and closes them, time spent that a policy decision could have saved. 02 Document request sent. Chased twice. The case has not moved in five days No tracking. No automatic follow-up. The onboarding stalls on a single unanswered email while your analyst waits and the SLA clock runs. 03 Your L1 analyst spent an hour building a case file instead of reviewing it Pulling data from screening, collecting documents, and assembling a risk view from scratch, before any actual compliance judgment has been made. 04 Your screening system from 2009 keeps firing the same alerts Screening without context is just noise. Stale, irrelevant hits consume the same review time as genuine risks, and the same analyst dismisses them again next quarter. 05 The PEP hit came back. Is it actually your client? Someone has to find out Name matching on its own tells you nothing useful. Every potential hit lands on a human who has to cross-reference date of birth, nationality, and jurisdiction manually. 06 Six separate document requests because nobody mapped them upfront Each missing document discovered only after the previous one arrives. The client gets a drip of requests across two weeks and loses patience before you are done. ##### The difference Strise makes. 80% of low-risk cases closed without analyst review. Hours not days to collect and verify documents. 76% efficiency improvement. Same analysts, significantly more capacity. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## Prepared before your analyst opens the case. - 1 ###### Case arrives. Work begins A new individual arrives. Strise begins screening and risk assessment immediately. - 2 ###### Screened and enriched PEP and sanctions checked, adverse media reviewed, source of wealth verified, all at once. - 3 ###### Scored. Cases routed Risk calculated against your policy. Low-risk cases resolved, high-risk cases escalated. - 4 ###### File ready. Analyst decides Your analyst opens a complete case file with every check done. One decision to make. ##### Automate KYC onboarding. ###### Low-risk cases closed without a human. Your policy sets the threshold. Cases that meet it close automatically, verified, screened, scored, and logged without analyst time. You stay in control of what clears and what gets reviewed. Risk distribution showing 1,247 entities processed: 47 high risk, 178 medium risk, and 1,022 low risk; with 623 hours saved and 1,022 cases automated. ###### ID collection. No email chains. Automated ID requests, real-time tracking, and smart follow-up, so documents don’t get lost in inboxes or sit unanswered for days. The client gets one clear request for exactly what’s needed. Nothing more. Illustration of a shark wearing a suit and tie with a facial recognition overlay and a crest featuring a sword and shark, labeled Oceanis Nor. ###### Risk scoring built around your rules. Configure thresholds for jurisdiction, sanctions, PEP proximity, and more, without a developer. Your policy drives the scores. Not a generic default that treats every customer the same. User interface displaying risk scoring settings with high risk level for PEPs and sanctions, alerts on beneficial owner changes at 10% ownership threshold, and a periodic review cycle with a 12-month validity period and high risk class. ###### Every PEP and sanctions hit resolved in context. Every individual checked against global PEP lists, sanctions databases, and adverse media simultaneously. Matching uses name, date of birth, nationality, and jurisdiction together, so false positives don’t waste your team’s time. KYC screening results showing four entries with red warning icons indicating high risk: Boris Briefcase with new PEP hit, Nick Nominee with new sanctions hit, Phil T. Rich with new adverse media hit, one unnamed entry. ###### Adverse media that filters itself. Global news sources scanned and scored for relevance. Recent, material hits surfaced. Stale or irrelevant results filtered before they reach your team, so your analysts respond to real signals, not noise. Text listing legal cases involving Shady Sharks AS and Corruption Carlson, highlighting mentions related to corruption, money laundering, investigation, and bribery. ###### Complete audit trail from day one. Every step logged automatically. Every decision recorded with its source and rationale. When a regulator asks how you reached a decision, you open one file — not five. Audit trail list showing recent changes including address change to Rådhusgata 9, industry change to rice growing, new and updated sanctions on owners Sleazy Steve and Bambu Bandit, and additions/removals of beneficial owners by user @luna.lionfish. ##### Explore other products. - [KYB onboarding](https://strise.ai/product/kyb-onboarding) - [Monitoring (ODD)](https://strise.ai/product/ongoing-due-diligence) - [Identity verification](https://strise.ai/product/id-verification) - [UBO discovery](https://strise.ai/product/ubo-discovery) - [Remediation](https://strise.ai/product/remediation) - [Risk scoring](https://strise.ai/product/risk-engine) - [Screening](https://strise.ai/product/screening-and-alert-triage) - [Document analysis](https://strise.ai/product/document-analysis) ##### More customers onboarded. With the same team. See how Strise prepares every KYC case automatically, and what your team’s workload looks like when they only touch exceptions. [Book a demo](https://strise.ai/book-a-meeting-with-strise) ##### Things we get asked. Answered. What does straight-through processing actually look like? A new individual enters the workflow. Strise initiates screening and risk assessment at the same time. If the case meets your low-risk threshold with all checks clear, it closes automatically, full audit record included. From submission to completion without anyone on your team involved. Can low-risk individuals be approved without L1 review? Yes. You configure the threshold, what qualifies as low risk based on your policy, and cases that meet it close automatically with a full audit trail. No analyst time spent reviewing something that didn’t need a human. Your team’s attention goes to cases that genuinely require judgment. How is individual risk scored, and who sets the thresholds? Risk is calculated across multiple factors, PEP proximity, jurisdiction, source of wealth, screening results, each weighted according to your policy. You set the weights. You define what high and low risk look like for your customer base. The score reflects your rules, not a generic default applied to everyone. How does PEP and sanctions screening work for individual customers? Every individual is checked against global PEP lists, sanctions databases, and adverse media simultaneously, not sequentially. Matching uses name, date of birth, nationality, and jurisdiction together to cut false positive noise. Confirmed hits escalate with full context. Clear checks move forward automatically, each one documented. What documents do you need to collect from individual customers? Strise identifies exactly what’s needed based on the individual’s profile and risk level, and requests only that. ID verification, source of wealth declarations, and any additional documents are collected through a branded portal with automated follow-up. No generic checklist sent to every customer regardless of what applies. When does a case escalate to an analyst, and what does it look like when it arrives? A case escalates when something triggers it, a PEP match, a sanctions hit, a jurisdiction flag, or a score above your threshold. When it does, your analyst opens a complete file: screening done, risk scored, the triggering rule cited, and a recommended action attached. Nothing left to gather. Just a decision to make. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Ongoing Due Diligence and perpetual KYB / KYC — Strise URL: https://strise.ai/product/ongoing-due-diligence Strise monitors your entire portfolio continuously, alerting your team only when something changes. Move from periodic reviews to real-time risk awareness. - [Home](https://strise.ai)/ - Product / - Monitoring (ODD) #### What changed. Nothing else. Strise watches the network behind your customer, not just the name. What changed reaches your analyst. What didn’t, closes automatically. [Book a demo](https://strise.ai/book-a-meeting-with-strise) Ongoing due diligence icon - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Sound familiar? 01 Your review cycle runs on a schedule. Risk doesn’t care about dates Annual reminders fire on the same date regardless of what’s changed. High-risk entities and no-change entities go through exactly the same review process. 02 80% of your reviews find nothing. Your team still does every one Entities with no material changes still hit the full review queue, consuming the same analyst hours as genuinely complex and high-risk cases. 03 Something changed last month. You’ll find out at the next review Calendar-based monitoring leaves material changes undetected for months at a time. Real risk stays invisible until the next scheduled review date arrives. 04 One analyst. Thousands of reviews. Three hours each. Do the maths Review volumes don’t grow linearly, they spike. One team, one year, and suddenly the numbers don’t work no matter how hard your analysts push. 05 Your backlog is already past its SLA. Next year’s cycle is building behind it Two back-books, both past recommended review timelines. And the next periodic review cycle is already queuing up behind the one that’s still running. 06 A UBO three layers up just got sanctioned. Your file says clean The change happened in the ownership chain, not on the customer’s record. Your tool screened the name. The risk slipped through every check. ##### The difference Strise makes. 8/10 reviews close without an analyst. Zero touch. 80% automation rate in production. 55% from day one. 70% less time spent on manual review work per case. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## The review that prepares itself. - 1 ###### Onboarded. Monitoring live Every onboarded entity enters continuous monitoring from day one. Risk tier sets the frequency. - 2 ###### All signals. Always on The whole network around your customer, watched continuously. Ownership, sanctions, directors, PEPs, nothing missed. - 3 ###### Nothing changed. Closes Entities with no material changes confirmed and closed automatically. Audit log built. - 4 ###### Change detected. Your analyst gets a complete case file, the change documented, evidence cited, context provided. ##### Automate Ongoing Due Diligence. ###### Know the moment something changes. Not the month after. Your alert feed surfaces director changes, sanctions hits, and adverse media as they happen. You know immediately — not at the next scheduled review date. List of four high risk companies with warnings: Murky Tide Investments with new sanctions on beneficial owner, Crocodile Capital Pty Ltd. with new sanctions on beneficial owner Sleazy Steve, Bear Market Manipulation Inc. with new beneficial owner above 25% threshold, and one unnamed entry. ###### Alerts triaged. Only real matches reach your analyst. For every PEP, sanctions, and adverse media alert, AI assesses match confidence and separates likely hits from false positives. Analysts confirm, dismiss, or escalate, instead of investigating from scratch. False positives AI agent interface showing screening results for 4 individuals with categories: 1 likely true match, 2 likely false matches, and 1 needing analyst review, and options to confirm match, review evidence, or defer review. ###### Every alert documented. Context attached. Regulator-ready. Each alert includes what changed, the compliance implications, and the recommended next action. Every check, every closure, every analyst decision logged with source, timestamp, and rationale. Comparison of two profiles named Bambu Bandit, showing board membership at Panda Payoffs Ltd., country China, and roles including Minister of Natural Resources. ###### Your risk rules set the thresholds. Not the system. Set what triggers a review and what resolves without one. Different rules for different risk tiers, configured by your team, not locked in by default. You own the policy. Strise enforces it. User interface displaying risk scoring settings with high risk level for PEPs and sanctions, alerts on beneficial owner changes at 10% ownership threshold, and a periodic review cycle with a 12-month validity period and high risk class. ###### Your entire portfolio. Risk-ranked. At a glance. Sorted by current risk level, open alerts, and upcoming review dates, all visible at once. The entities that need attention rise to the top. Everything else stays out of your way. Dashboard showing portfolio risk factors with counts for PEPs 27, Sanctions 3, RCAs 0, and PEPs in high risk countries 15, plus review overview with Reviews 435, Rending Reviews 12, Pending Priority Reviews 421, and Upcoming reviews 56. ###### Connects to your CRM, TMS, and CLM from day one. Push alerts and review outcomes into the tools your team already uses. Strise fits into your workflow, it doesn’t ask your team to change theirs. Diagram showing integration of Strise’s risk output and audit trail with your existing CRM and CLM stack via API. ###### Every decision recorded. Regulator-ready. Every check, every closure, every analyst decision, logged with source, timestamp, and rationale. Your audit trail is complete before anyone asks for it. Audit trail list showing recent changes including address change to Rådhusgata 9, industry change to rice growing, new and updated sanctions on owners Sleazy Steve and Bambu Bandit, and additions/removals of beneficial owners by user @luna.lionfish. ##### Explore other products. - [KYB onboarding](https://strise.ai/product/kyb-onboarding) - [KYC onboarding](https://strise.ai/product/kyc-onboarding) - [Identity verification](https://strise.ai/product/id-verification) - [UBO discovery](https://strise.ai/product/ubo-discovery) - [Remediation](https://strise.ai/product/remediation) - [Risk scoring](https://strise.ai/product/risk-engine) - [Screening](https://strise.ai/product/screening-and-alert-triage) - [Document analysis](https://strise.ai/product/document-analysis) ##### Your monitoring backlog isn’t a people problem. See what changes when Strise watches the portfolio and your team only handles what’s genuinely changed. [Book a demo](https://strise.ai/book-a-meeting-with-strise) ##### Things we get asked. Answered. How often does Strise check for changes? Sanctions and PEP lists are checked daily, overnight, results surface the next morning. Registry changes vary by jurisdiction, but we check as soon as registries publish updates. You’re not waiting a week to find out something changed. What actually triggers a review? Any material change: a new director, an ownership transfer, a sanctions update, a PEP designation, or a significant adverse media hit. Low-noise changes that don’t affect risk don’t trigger anything, your team isn’t flooded with non-events. How do you handle false positives? Matching uses multiple data points together — name, date of birth, nationality, jurisdiction, entity type. When they don’t line up, it’s flagged as a likely false positive automatically. Your team reviews what’s genuinely ambiguous, not everything that shares a name. Do entities ever close without anyone looking at them? Yes — that’s the point. Entities with no material changes are automatically confirmed and closed, with a full audit record. Your analysts spend time on cases that need a human, not on signing off that nothing happened. What data sources does Strise use? Dow Jones for PEP and sanctions lists. Global registry connections for director and ownership changes. Global news sources for adverse media. All checked together so you get context, not just a raw hit that tells you nothing on its own. Can we see the whole portfolio in one place? Yes. The portfolio dashboard shows risk tiers, upcoming and overdue reviews, PEP and sanctions exposure, and recent changes, across your full book. One screen. No manual reporting required to understand where you stand. What does the audit trail look like? Every check, every alert, every decision, logged automatically with timestamp, user, source, and rationale. When a regulator asks how you run ongoing due diligence, you pull the trail and show them. No scrambling, no reconstructing from emails. Does this replace periodic reviews, or sit alongside them? Both. You set a review cadence per risk tier — annual for low, six-monthly for high, whatever your policy requires. Event-triggered reviews fire on top of that when something material changes. You get scheduled coverage and real-time response. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### ID verification and automated identity checks — Strise URL: https://strise.ai/product/id-verification Strise verifies passports, driving licences, and ID documents instantly, using AI and machine learning to validate any format, from any jurisdiction. - [Home](https://strise.ai)/ - Product / - ID verification #### ID verified before your analyst opens the case. Clean submissions move forward automatically. Incomplete ones get chased, without your team doing the chasing. [Book a demo](https://strise.ai/book-a-meeting-with-strise) Identity verification icon - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Sound familiar? 01 The document arrived. Is it valid? Expired? Authentic? Submission is only the start. Every document still needs to be opened, checked for validity, assessed for authenticity, and then manually added to the case file. 02 Document request sent. The client opened it. That is the last you heard No submission tracking. No automatic follow-up. The case stalls on a single unanswered email while your analyst manually chases a client who has forgotten it exists. 03 Three emails, two portals, one confused client. One delayed onboarding Inconsistent collection methods create friction at the worst moment, when a client is trying to get started. Confusion at this stage sets the tone for the whole relationship. 04 The passport uploaded fine. Wrong page. Back to the start. Another week lost Without intelligent validation at point of submission, bad uploads only get caught on review. By then the client has moved on and re-engagement takes days. 05 Documents verified. Then manually transcribed into the system Verification and data entry are treated as separate tasks. Every document becomes a copy-paste exercise before the information it contains reaches your compliance record. 06 Your most senior analyst is chasing documents for thirty minutes every day Manual follow-up defaults to whoever is available or whoever notices the case has stalled. That is rarely the right person spending their time on the right problem. ##### The difference Strise makes. 90% of document requests completed without analyst follow-up. Hours not days to collect and verify documents. 76% efficiency improvement. Same analysts, significantly more capacity. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## ID verified. Case completes. - 1 ###### Gap found. Request sent The moment a data gap is identified, the right request goes out automatically. - 2 ###### Form delivered. Pre-filled Clients receive a mobile-friendly form with exactly what they need, nothing irrelevant. - 3 ###### Follow-up runs. Docs arrive Submission tracked in real time. Automated reminders sent until documents arrive. - 4 ###### Verified. Data extracted Uploaded docs verified by AI. Key data extracted and filed directly into the case. ##### Automate ID Verification. ###### Every ID format, verified instantly. Passport, driving licence, and national ID verified by AI, liveness checks, document authenticity scoring, and optional human review for edge cases. Your team never handles a document manually. Illustration of a shark wearing a suit and tie with a facial recognition overlay and a crest featuring a sword and shark, labeled Oceanis Nor. ###### The right form for every client, built and sent automatically. Configurable declaration forms for every entity type, UBO declarations, source of wealth, beneficial ownership, built automatically based on the client’s profile. Your team doesn’t assemble them. Strise does. Dropdown menu labeled Template with options for identity verification including eIDV passport, national ID card, IDV initiation, individual identity verification selected, and document upload proof of identity. ###### Automated follow-up, no manual chasing. Submission reminders triggered based on status, not fixed timers. The right escalation at the right time, without anyone on your team manually tracking which client hasn’t responded. List of ID verifications showing names, dates, and statuses: pending, active PDF, and cancelled. ###### One portal. Any device. Done in minutes. Branded, mobile-friendly, and accessible from any device, clients upload documents, complete a liveness check, and sign declarations in minutes, without reply-all email chains or confusing instructions. Online form to open a business account with fields for company ID, company name, industry, legal form, and a submit button. ###### Documents read, data filed. No re-keying. Submitted documents read by AI, key fields extracted, and data populated directly into the case. No manual transcription. No copy-paste errors. What the client submitted is what your analyst sees, already in the right field. Document analysis interface showing PDF file Meridian_Trust_Deed.pdf with AI processing complete and three extracted entities: Finn Shark as Director, Shady Sharks AS as Shareholder with 10%, and Shelly Shark as Ultimate beneficial owner with 49%, with an ’Add to ownership’ button. ###### Declarations signed, timestamped, and regulator-ready. Every declaration signed digitally with full chain of custody, timestamped, stored, and instantly retrievable. When your regulator asks, the complete record is already there. Audit trail list showing recent changes including address change to Rådhusgata 9, industry change to rice growing, new and updated sanctions on owners Sleazy Steve and Bambu Bandit, and additions/removals of beneficial owners by user @luna.lionfish. ##### Explore other products. - [KYB onboarding](https://strise.ai/product/kyb-onboarding) - [KYC onboarding](https://strise.ai/product/kyc-onboarding) - [Monitoring (ODD)](https://strise.ai/product/ongoing-due-diligence) - [UBO discovery](https://strise.ai/product/ubo-discovery) - [Remediation](https://strise.ai/product/remediation) - [Risk scoring](https://strise.ai/product/risk-engine) - [Screening](https://strise.ai/product/screening-and-alert-triage) - [Document analysis](https://strise.ai/product/document-analysis) ##### More clients verified. No extra headcount. See how Strise collects and verifies documents, and what your team’s workload looks like when document collection runs itself. [Book a demo](https://strise.ai/book-a-meeting-with-strise) ##### Things we get asked. Answered. Can ID verification and declaration forms be sent together? Yes — one portal request can include ID verification, a declaration form, and any other document collection in a single flow. Your client submits everything in one place. Your team gets one complete submission, not a sequence of separate requests and separate follow-up chains. Which document types does Strise accept and verify? Passports, driving licences, national IDs, and residence permits, across every major issuing jurisdiction. Documents are checked for authenticity and expiry automatically. Liveness checks confirm the document matches the person submitting it. No manual document review from your team. What happens when a client doesn’t submit? Automated follow-up fires based on submission status, not fixed timers. A client who opened the portal but didn’t submit gets a different message to one who hasn’t opened it at all. Escalation to your analyst only triggers when something is genuinely stuck, not on a fixed schedule. Do the client-facing forms work on mobile? Yes. The client portal is built to be completed on a phone, document photo, liveness check, and declaration signing in a few minutes without switching devices or downloading anything. Less friction means faster submission and fewer drop-offs at the point of collection. Who needs to be verified, the UBO, the director, or the person filling in the form? You configure it. Strise can send verification requests to the beneficial owners in the ownership structure, the directors, the form filler, or any combination your policy requires. The right people get verified based on your rules, not a one-size-fits-all flow applied to every case. What does the audit trail look like for submitted documents? Every submission is logged with a timestamp, when it was requested, when it was submitted, what was extracted, and what action it triggered. Declarations are digitally signed and stored with full chain of custody. When your regulator asks, the complete record is already there. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### UBO discovery and beneficial ownership mapping — Strise URL: https://strise.ai/product/ubo-discovery Strise uses AI and entity resolution to map complex ownership structures, surfacing ultimate beneficial owners across layers and jurisdictions automatically. - [Home](https://strise.ai)/ - Product / - UBO discovery #### Know who really owns it. Before anyone asks. Ownership mapped before your analyst opens the case. Nothing left to trace. [Book a demo](https://strise.ai/book-a-meeting-with-strise) UBO mapping icon - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Sound familiar? 01 Registry data is incomplete more often than not When data is missing, your analyst fills the gaps with fragments, filings, and direct client requests. It’s slower, less reliable, and leaves questions in the audit trail a regulator will eventually ask. 02 You verified the structure. That was eighteen months ago Ownership changes, new shareholders, restructured holding chains, updated allocations. None of it surfaces automatically once onboarding is done. What you documented can be out of date before the next review. 03 Ownership chains that cross borders don’t trace themselves Each jurisdiction means a different registry, a different search session, and an analyst who knows where to look. Most don’t have time for all of them, so the chain gets traced partially, or not at all. 04 The investigation is done. The data entry hasn’t started Once the ownership chain is traced, someone still manually re-enters every individual into your case management system. The data exists, moving it into the right place is where the time goes. 05 Individual thresholds miss aggregate control Three shareholders each holding 18% don’t individually trigger a 25% UBO flag, but together they control 54%. Without cumulative ownership logic, your system approves the entity and misses the people behind it. 06 Some ownership chains end with a locked door Trusts and foundations aren’t required to declare their controllers. When the chain ends at one, your analyst starts playing email tennis, and the case sits open until someone replies. ##### The difference Strise makes. 80% of routine cases can be automated. 30 MIN per complex case, down from 3 hours. 6× more complex cases handled per analyst. Same team. No new hires. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## Complex Ownership chains resolved. - 1 ###### Entity in. Mapping begins Provide a company name. Strise starts mapping the ownership chain immediately. - 2 ###### Every layer. Every jurisdiction Parent companies, holding entities, and investment vehicles traced across every jurisdiction. - 3 ###### Owners surfaced. Checks run Beneficial owners identified at each threshold. PEP and sanctions checks run automatically. - 4 ###### Graph delivered. Audit-ready Navigable ownership graph, risk scores applied, source citations included, exceptions flagged. ##### Automate UBO Discovery. ###### The complete ownership chain, from one company number. Strise traces upstream to ultimate natural persons and downstream to subsidiaries, across jurisdictions, through holding layers, with diluted percentages calculated and every data source cited at each node. Ownership diagram showing Bribery Barnes owns 6% (politically exposed person), Corruption Carlson as ultimate beneficial owner with 64%, affecting ownership in Mammal Money Laundering Ltd., Octopus Offshore Holdings, Llama Tax Havens Inc., and Shady Sharks Ltd. ###### Beyond percentages. Who actually controls. Family members with sub-threshold stakes are aggregated and flagged automatically. Share class structures are mapped by voting control, not just headline ownership, so real control is visible. Ownership chart for Shady Sharks Ltd. showing Finn Shark as beneficial owner with 28.93% personal shares, and Shelly Shark as alternative beneficial owner with 6.24% personal shares plus 33.77% family shares. ###### When the registry hits a wall, documents take over. When the ownership chain ends at an opaque entity, Strise flags the gap and generates a targeted document request. Upload the response and AI extracts and maps the ownership data. No re-entry. Audit trail complete. Screen showing extracted entities from PDF Meridian_Trust_Deed.pdf: Finn Shark Director, Shady Sharks AS Shareholder 10%, Shelly Shark Ultimate beneficial owner 49%, with an Add to ownership button. ###### Your analysts know things registries don’t. When your team has better information than a registry, they can update the ownership record directly with a source and timestamp. Every edit tracked. Original data preserved alongside it. Comparison of shareholder and verified ultimate beneficial owner details for Bambu Bandit, each holding 31% shares, with the verified UBO showing additional warning and verification notes. ###### Ownership changes after onboarding. Strise catches them. A director added, a shareholding transferred, a new entity appearing in the chain, all picked up automatically. Changes that affect risk classification trigger a review with everything your analyst needs to act already attached. List of four high risk companies with warnings: Murky Tide Investments with new sanctions on beneficial owner, Crocodile Capital Pty Ltd. with new sanctions on beneficial owner Sleazy Steve, Bear Market Manipulation Inc. with new beneficial owner above 25% threshold, and one unnamed entry. ###### Every source cited. Every gap explained. Every check, every closure, every analyst decision, logged with source, timestamp, and rationale. Your audit trail is complete before anyone asks for it. Audit trail list showing recent changes including address change to Rådhusgata 9, industry change to rice growing, new and updated sanctions on owners Sleazy Steve and Bambu Bandit, and additions/removals of beneficial owners by user @luna.lionfish. ##### Explore other products. - [KYB onboarding](https://strise.ai/product/kyb-onboarding) - [KYC onboarding](https://strise.ai/product/kyc-onboarding) - [Monitoring (ODD)](https://strise.ai/product/ongoing-due-diligence) - [Identity verification](https://strise.ai/product/id-verification) - [Remediation](https://strise.ai/product/remediation) - [Risk scoring](https://strise.ai/product/risk-engine) - [Screening](https://strise.ai/product/screening-and-alert-triage) - [Document analysis](https://strise.ai/product/document-analysis) ##### More structures mapped. No ownership rabbit holes. See how Strise traces multi-layer ownership automatically, and what your team’s workload looks like when UBO research runs itself. [Book a demo](https://strise.ai/book-a-meeting-with-strise) ##### Things we get asked. Answered. Is Strise built for AMLR’s verification requirements? Yes. From 2027, AMLR raises the bar on beneficial ownership verification, a central register is no longer treated as a sufficient source on its own, and ownership must be confirmed across multiple independent sources. That is how Strise already works: we cross-reference registries, AI-read documents, and analyst-verified data into one ownership record, every source cited and every change timestamped. Multi-source, traceable, current, built in now, not retrofitted in 2027. Which countries and registries are covered? Strise integrates data from over 250 jurisdictions globally. Coverage includes 10B+ corporate records, 500M+ company profiles, and 600M+ individual profiles. Where registries don’t offer digital access, Strise uses enrichment providers, and targeted client forms to close any remaining gap. How are ownership percentages calculated across multiple layers? Strise calculates both direct and cumulative ownership at every level — so a 60% stake in a company that owns 50% of another shows as 30%, not 60%. Any threshold you configure (25%, 10%, whatever your policy requires) is applied consistently to every entity, every time. Can Strise identify beneficial owners hidden through nominees or trusts? Nominee structures, trusts, and foundations are flagged automatically where indicators are present. Strise surfaces what the data shows and highlights what needs further verification. Where a trust or foundation is the end of the trail, your analyst sees that clearly, rather than an unexplained dead end. What happens when ownership changes after onboarding? Strise monitors for changes and flags them as they happen. A director added, an ownership transfer filed, a new entity appearing in the structure — all picked up automatically. Your team gets a review triggered when something material changes, not at the next scheduled review cycle. How deep does Strise trace ownership chains? As deep as the structure goes. Strise traces through holding companies, investment vehicles, and intermediaries across multiple layers until it reaches the ultimate natural persons. Circular ownership patterns and dissolved intermediaries are flagged automatically rather than creating dead ends. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### AML remediation software and compliance file remediation — Strise URL: https://strise.ai/product/remediation Strise uses AI to automate AML remediation, prioritising files by risk, collecting missing data, and helping teams clear backlogs without adding headcount. - [Home](https://strise.ai)/ - Product / - Remediation #### Remediation. Days, not months. Strise processes your entire portfolio simultaneously, gaps identified, outreach triggered, cases ranked, before your team opens a single file. [Book a demo](https://strise.ai/book-a-meeting-with-strise) Remediation icon - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Sound familiar? 01 Full remediation. Thousands of clients. Few months to complete A remediation at portfolio scale is not a compliance task. It is a project. One that your existing team and tools were never built to absorb without significant disruption. 02 You know the data is stale. You do not know where Without systematic gap analysis, prioritisation is guesswork. Teams start with the most visible cases and discover the scope of the problem only as they work through it. 03 Every client needs re-KYC. Relationship managers handle it manually Remediation outreach falls to whoever owns the client relationship. That means senior people spending weeks on administrative follow-up instead of the cases that need judgment. 04 The team prioritised the visible cases. The rest are in a queue Without portfolio-wide ranking by risk and gap severity, effort concentrates where it is easiest, not where the regulatory exposure is highest. The most urgent cases wait longest. 05 Three months in. Thirty percent complete. The deadline is in four weeks Manual remediation velocity is fixed. The team works as fast as the process allows, and the process almost always runs slower than the timeline requires. 06 Remediation done. Regulator asks for documentation. None exists The work was done. But without a complete audit trail, what was checked, when, and by whom, proving it to a regulator means reconstructing the project from memory and emails. ##### The difference Strise makes. 10,000+ entities processed in a single remediation run. 100% audit trail from day one. Regulator-ready before the project closes. Days not months for large-scale remediation projects. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## Portfolio remediated. Automatically. - 1 ###### Portfolio ranked. Prioritised Entities ordered by risk severity and data gap. Most critical cases surfaced first. - 2 ###### Files compared. Gaps found Each entity compared against current registry data. Discrepancies identified automatically. - 3 ###### Data missing. Forms sent Missing data triggers the right collection automatically. No manual chasing required. - 4 ###### Monitoring starts Every cleared entity rolls into continuous monitoring. No second project, no manual handoff. ##### Automate remediation. ###### Every gap found before your team starts. Strise compares your entire portfolio against your current data standards and live registry data simultaneously. Missing documents, stale ownership, outdated risk scores, all surfaced before a single case is opened. Risk distribution showing 1,247 entities processed: 47 high risk, 178 medium risk, and 1,022 low risk; with 623 hours saved and 1,022 cases automated. ###### Old data vs new. Every discrepancy shown. Each entity’s file compared against current registry data. Where something’s changed, a director, an address, a beneficial owner, the discrepancy view shows the old value and the new value side by side. Your analyst decides. Strise does the finding. Comparison of shareholder and verified ultimate beneficial owner details for Bambu Bandit, each holding 31% shares, with the verified UBO showing additional warning and verification notes. ###### Outreach triggered. No manual chasing. When data is missing, the right form goes out automatically, ID request, UBO declaration, source of wealth, or whatever your policy requires. Submission tracked in real time. Follow-up runs automatically until it’s complete. ORCABANK business information form requesting company ID, name, industry with example entries, and legal form field with a submit button. ###### The project ends, the protection doesn’t. Every cleared entity rolls straight into Strise’s monitoring layer. No second project. No manual handoff. The same system keeps watching your portfolio, surfacing ownership changes, flagging sanctions hits, closing low-risk cases automatically. Dashboard showing portfolio risk factors with counts for PEPs 27, Sanctions 3, RCAs 0, and PEPs in high risk countries 15, plus review overview with Reviews 435, Rending Reviews 12, Pending Priority Reviews 421, and Upcoming reviews 56. ###### Regulator-ready from the first run. Not the last. Every remediation action logged from day one, what was checked, what was found, what was updated, and by whom. Progress reporting shows completion rates and outstanding cases by risk tier, exportable in the format your regulator expects. The audit trail is complete before anyone asks for it. Audit trail list showing recent changes including address change to Rådhusgata 9, industry change to rice growing, new and updated sanctions on owners Sleazy Steve and Bambu Bandit, and additions/removals of beneficial owners by user @luna.lionfish. ##### Explore other products. - [KYB onboarding](https://strise.ai/product/kyb-onboarding) - [KYC onboarding](https://strise.ai/product/kyc-onboarding) - [Monitoring (ODD)](https://strise.ai/product/ongoing-due-diligence) - [Identity verification](https://strise.ai/product/id-verification) - [UBO discovery](https://strise.ai/product/ubo-discovery) - [Risk scoring](https://strise.ai/product/risk-engine) - [Screening](https://strise.ai/product/screening-and-alert-triage) - [Document analysis](https://strise.ai/product/document-analysis) ##### A remediation backlog isn’t a headcount problem. See how Strise processes thousands of entities simultaneously, and what your team’s workload looks like when remediation velocity doesn’t depend on how many people you have. [Book a demo](https://strise.ai/book-a-meeting-with-strise) ##### Things we get asked. Answered. How do we know what actually needs remediating? Strise compares every entity in your portfolio against your current data standard and live registry data. Gaps are surfaced automatically, missing documents, outdated ownership information, stale risk scores — so the scope of the project is defined by data, not by what your team has time to manually check. How long does a remediation project typically take? That depends on portfolio size and gap depth, but the comparison and case preparation work that typically takes months runs in days with Strise. The bottleneck shifts from data gathering to analyst decisions, and since cases arrive pre-prepared and ranked by priority, your team works through the backlog faster than a traditional remediation program allows. How does the discrepancy detection actually work? Strise pulls current data from registries and compares it against what’s on file for each entity. Where something has changed, a director, a shareholder, an address, a beneficial owner, the discrepancy view shows the old value and the new value side by side. Your analyst sees exactly what’s different and decides whether to update the record, request clarification, or flag for review. How does client outreach work when data is missing? When Strise identifies a gap that requires client input, the right collection form goes out automatically — ID request, UBO declaration, source of wealth, or whatever your policy requires. Submission is tracked in real time. Follow-up runs automatically until the form is completed. Your team doesn’t chase, they review what comes back. How many entities can Strise process in a single remediation run? There is no practical ceiling. Strise processes entities in parallel, the comparison, gap analysis, and risk recalculation runs across your entire portfolio simultaneously. Ten thousand entities takes the same elapsed time as one hundred. The work doesn’t queue up. Your team’s capacity is the only constraint on how fast the remediation closes. What does the audit trail look like for our regulator? Every remediation action is logged from the first run — what was checked, what was found, what was updated, and by whom. The full audit trail is available before the project closes, not assembled afterwards. Progress reporting shows completion rates, outstanding cases by risk tier, and gap resolution status — exportable and shareable in the format your regulator expects. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### AML risk scoring and automated risk assessment — Strise URL: https://strise.ai/product/risk-engine Strise scores customer risk automatically against your policy, giving every analyst a clear, consistent view before they open a case. - [Home](https://strise.ai)/ - Product / - Risk scoring #### Your policy. Applied automatically. Every entity scored against the same rules, and recalculated the moment data says it should be. [Book a demo](https://strise.ai/book-a-meeting-with-strise) Risk scoring icon - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Sound familiar? 01 The risk model lives in a spreadsheet Policy changes, new jurisdictions, updated thresholds, all requiring manual edits to a shared file that may or may not reflect what your analysts are actually applying. 02 A director changed last month. The risk score did not Risk scores calculated at onboarding and never updated. Material events, director changes, ownership shifts, sanctions hits, sit undetected until the next scheduled review. 03 High, medium, low, defined differently by each analyst on your team Without a configured policy applied consistently, risk classification reflects who reviewed the case as much as what the case actually contains. That’s an audit problem. 04 Thousands of clients reviewed last quarter. Risk scores calculated by hand Manual risk scoring does not scale. As the portfolio grows, so does the review backlog, and the margin for inconsistency grows with it. 05 Something material changed three weeks ago. The risk score didn’t Your risk policy lists the events that should trigger recalculation. Your system can’t read the policy, so recalculation happens when someone remembers to run it, not when the data says it should. 06 You can explain the decision. Documenting it is a different problem Risk scores without factor breakdowns, source citations, or change history are not defensible. The judgment was sound. Proving it after the fact is where things fall apart. ##### The difference Strise makes. Instant risk recalculation when data changes. 80% of cases auto-approved. No analyst required. 100% of risk decisions documented with rationale. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## Policy applied automatically. - 1 ###### Policy set. Engine ready Set risk weights for every factor in plain language. No developers needed. - 2 ###### Entity assessed. Scored Risk calculated at onboarding, then recalculated automatically as new data arrives. - 3 ###### Data changes. Score updates A director change, sanctions hit, or jurisdiction shift triggers automatic recalculation. - 4 ###### Decision made. All recorded Full score timeline per entity, what changed, when, and why. Every factor cited. ##### Automate risk scoring. ###### Your risk policy. Your team. No IT department. Assign risk weight to any factor, jurisdiction, industry code, PEP status, ownership structure, in plain language, without writing a line of code. Your compliance team owns the configuration. Your IT team isn’t involved. User interface displaying risk scoring settings with high risk level for PEPs and sanctions, alerts on beneficial owner changes at 10% ownership threshold, and a periodic review cycle with a 12-month validity period and high risk class. ###### Event-driven reviews, not calendar ones. Reviews triggered by actual risk changes, not by the date. A PEP designation, a sanctions update, a director change, each triggers an immediate review with everything your analyst needs already attached. High-risk entities are reviewed when it matters, not just when the annual cycle says so. List of four high risk companies with warnings: Murky Tide Investments with new sanctions on beneficial owner, Crocodile Capital Pty Ltd. with new sanctions on beneficial owner Sleazy Steve, Bear Market Manipulation Inc. with new beneficial owner above 25% threshold, and one unnamed entry. ###### Your entire portfolio. Risk-ranked. No reports to build. Segment your portfolio by risk tier, industry, jurisdiction, or any custom attribute. See exactly where your exposure sits, what’s changed, what’s overdue, what needs attention, without building a report or running a query. Dashboard showing portfolio risk factors with counts for PEPs 27, Sanctions 3, RCAs 0, and PEPs in high risk countries 15, plus review overview with Reviews 435, Rending Reviews 12, Pending Priority Reviews 421, and Upcoming reviews 56. ###### Risk changes. Strise acts instantly. When new information comes in, Strise automatically updates the risk assessment and routes material changes directly to the right person for review. No material change in risk goes unaddressed. Comparison of two profiles named Bambu Bandit, showing board membership at Panda Payoffs Ltd., country China, and roles including Minister of Natural Resources. ###### Every score explained and documented. Every risk score broken down by factor, every factor cited to its source. Your analysts can explain and justify any decision, in any context, to any regulator. Not because they remember the case, but because the system recorded it. Audit trail list showing recent changes including address change to Rådhusgata 9, industry change to rice growing, new and updated sanctions on owners Sleazy Steve and Bambu Bandit, and additions/removals of beneficial owners by user @luna.lionfish. ###### Your internal signals. Inside the engine. One score. Connect signals from your existing systems via API. Internal risk data sits alongside Strise data in the engine, so scores reflect everything your operation knows, not just what external registries say. The score isn’t incomplete. It’s the full picture. Diagram showing integration of Strise’s risk output and audit trail with your existing CRM and CLM stack via API. ##### Explore other products. - [KYB onboarding](https://strise.ai/product/kyb-onboarding) - [KYC onboarding](https://strise.ai/product/kyc-onboarding) - [Monitoring (ODD)](https://strise.ai/product/ongoing-due-diligence) - [Identity verification](https://strise.ai/product/id-verification) - [UBO discovery](https://strise.ai/product/ubo-discovery) - [Remediation](https://strise.ai/product/remediation) - [Screening](https://strise.ai/product/screening-and-alert-triage) - [Document analysis](https://strise.ai/product/document-analysis) ##### Risk scores you can explain. And prove. See how Strise scores and documents risk, and what your team’s workload looks like when every decision is backed by evidence, not memory. [Book a demo](https://strise.ai/book-a-meeting-with-strise) ##### Things we get asked. Answered. How are risk scores actually calculated? Each entity is scored across multiple risk categories, jurisdiction, PEP status, adverse media, sanctions exposure, ownership structure, industry, each weighted according to your policy. The score is the sum of those weighted factors, not a black box. Every point comes from somewhere your team can see and explain. Can we configure the risk policy ourselves, without developers? Yes. You set the weight for each risk factor, how many points a PEP hit adds, how much a high-risk jurisdiction contributes, which adverse media categories matter. Configuration is done in plain language, no code required. Your policy, not a generic one applied to everyone. How many risk tiers can we have, and can we name them ourselves? Up to seven risk levels, though most customers use three or four. Tier names and thresholds are yours to define. The system shows both the calculated score and any user-assessed override side by side, so there is always a clear record of what the engine produced and what your team decided. What triggers a risk recalculation? Any material data change: a director added, a sanctions list updated, a PEP designation made, adverse media flagged, an ownership structure changed, or a jurisdiction reclassified. The score updates automatically when the data changes, not when the next review cycle comes around. Can analysts override a calculated risk score? Yes. An analyst can move a score up or down, with a rationale attached. The calculated score is preserved in the audit trail alongside the override, so there is always a record of what the system produced and what your team decided, and why. Both are visible, neither is lost. What does the audit trail look like for risk decisions? Every score change is logged automatically, what changed, when, who made the decision, and why. If a risk rating was overridden, the original calculated score sits in the record alongside it. When your regulator asks how a risk decision was reached, you show them the complete trail. Nothing to reconstruct. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Sanctions and PEP screening software — AML Screening — Strise URL: https://strise.ai/product/screening-and-alert-triage Screen against global sanctions lists, PEP databases, and adverse media. AI-powered filtering cuts false positives and keeps alerts actionable. - [Home](https://strise.ai)/ - Product / - Screening and alert triage #### Real risk only. No noise. Real risk reaches your analyst. Everything else is cleared before it arrives. [Book a demo](https://strise.ai/book-a-meeting-with-strise) Screening icon - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Sound familiar? 01 Nine in ten alerts are false positives. Your team confirms every one before moving on The genuine risk that screening is designed to catch is buried under a flood of irrelevant matches. The ratio gets worse with common names and global client bases. 02 The sanctions list updated overnight. Re-screening the whole portfolio starts now. Manually List updates should trigger automatic re-screening. Instead they trigger a team briefing, a task allocation meeting, and a queue that takes weeks to clear. 03 Adverse media from seven years ago still fires every quarter. Someone checks it every time Screening without date and relevance filters treats a decade-old minor press mention the same as a recent financial crime conviction. The volume is unmanageable. 04 Your analyst cleared the same false positive on three different clients this week No institutional memory. The same name match that was investigated and dismissed last month is investigated and dismissed again — by a different analyst who has no record of it. 05 Re-screening was supposed to happen quarterly. The backlog is already three months behind Periodic re-screening at scale only works if it’s automated. When it is manual, it is always the thing that slips when something more urgent arrives. 06 The name matched. Is it actually your client? Someone has to confirm every single one Name matching without context is a noise machine. Date of birth, nationality, entity type, none of it checked automatically, so every hit lands on a human to investigate. ##### The difference Strise makes. 30% fewer false positives reaching your analysts. 100% of alerts reach analysts pre-assessed. 11.5M entities screened by a single European payments customer. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## Separate real risk from noise. - 1 ###### Entity in. Screening begins The screening check begins the moment an entity enters the workflow. Nothing waits. - 2 ###### Context matched. Not noise Date of birth, nationality, jurisdiction, and entity type used together to reduce false positives. - 3 ###### All signals. At once Sanctions lists, PEP databases, and adverse media checked and scored at the same time. - 4 ###### Risk confirmed. Alert raised Confirmed PEP, sanctions, and adverse media hits escalated with full context and recommended action. ##### Automate screening. ###### Alerts triaged. Only real matches reach your analyst. For every PEP, sanctions, and adverse media alert, AI assesses match confidence and separates confirmed hits from false positives. Analysts confirm, dismiss, or escalate, they don’t investigate from scratch. False positives AI agent interface showing screening results for 4 individuals with categories: 1 likely true match, 2 likely false matches, and 1 needing analyst review, and options to confirm match, review evidence, or defer review. ###### Your screening policy applied automatically. Define how PEP, sanctions, and adverse media matches should be treated. Set risk weights, escalation rules, and review thresholds once, then the same policy applies consistently across every entity and every alert, without anyone needing to enforce it. User interface displaying risk scoring settings with high risk level for PEPs and sanctions, alerts on beneficial owner changes at 10% ownership threshold, and a periodic review cycle with a 12-month validity period and high risk class. ###### Re-screening runs without anyone starting it. When a sanctions list updates or new adverse media appears, re-screening triggers automatically across your portfolio. No manual trigger. No briefing to get it started. Your portfolio is always screened against current data, not last month’s. List of four high risk companies with warnings: Murky Tide Investments with new sanctions on beneficial owner, Crocodile Capital Pty Ltd. with new sanctions on beneficial owner Sleazy Steve, Bear Market Manipulation Inc. with new beneficial owner above 25% threshold, and one unnamed entry. ###### Alerts arrive with the investigation already prepared. Each alert includes the matched watchlist record, the contextual attributes used for verification, the source list, and the reason the match triggered. Your analyst reviews the evidence and makes the decision, they don’t assemble it. Comparison of two profiles named Bambu Bandit, showing board membership at Panda Payoffs Ltd., country China, and roles including Minister of Natural Resources. ###### Screening results in your case system. No context switching. Alerts and review outcomes sync directly with your CRM, TMS, or CLM. Your team reviews screening results without switching systems, re-entering data, or rebuilding context from scratch. Diagram showing integration of Strise’s risk output and audit trail with your existing CRM and CLM stack via API. ###### Every screening decision logged. Including the ones that said no. Every check, every closure, every alert dismissed as a false positive, logged automatically with source, timestamp, and rationale. When your regulator asks why an entity was cleared, the answer is already written. Nothing to recall. Nothing to reconstruct. Audit trail list showing recent changes including address change to Rådhusgata 9, industry change to rice growing, new and updated sanctions on owners Sleazy Steve and Bambu Bandit, and additions/removals of beneficial owners by user @luna.lionfish. ##### Explore other products. - [KYB onboarding](https://strise.ai/product/kyb-onboarding) - [KYC onboarding](https://strise.ai/product/kyc-onboarding) - [Monitoring (ODD)](https://strise.ai/product/ongoing-due-diligence) - [Identity verification](https://strise.ai/product/id-verification) - [UBO discovery](https://strise.ai/product/ubo-discovery) - [Remediation](https://strise.ai/product/remediation) - [Risk scoring](https://strise.ai/product/risk-engine) - [Document analysis](https://strise.ai/product/document-analysis) ##### See how much of your alert queue disappears. Most alert volume is noise. Let us show you what your team’s workload looks like when false positives are cleared before they arrive. [Book a demo](https://strise.ai/book-a-meeting-with-strise) ##### Things we get asked. Answered. How does Strise identify PEPs, and what counts as one? Strise screens against major global PEP datasets. Coverage includes family members and close associates according to your policy. What qualifies as a PEP is configurable. Your policy defines the scope. Strise applies it consistently across every entity. Which screening lists and data sources does Strise use? Dow Jones is the primary data source — covering OFAC, EU, UN, HMT, and dozens of other sanctions and watchlists. Lists are updated in real time, so your team never screens against yesterday’s data. When a list updates, re-screening triggers automatically across your portfolio. How does the AI decide what’s a false positive? It does not guess. Each match is verified against additional attributes such as date of birth, nationality, jurisdiction, and entity type. When those attributes do not align with the watchlist record, the match is cleared automatically and the rationale is recorded in the audit trail. Only matches where the attributes remain consistent are escalated to your team for review. How often does Strise re-screen our portfolio? Sanctions and PEP lists are monitored continuously. When a list updates or a new designation is added, re-screening triggers automatically — no manual trigger, no team briefing required. Your portfolio is always screened against current data, not a snapshot from last quarter. Does Strise include adverse media screening? Yes. Global news sources are scanned and scored for date, severity, and topic relevance. A recent financial crime conviction surfaces. An unrelated article from seven years ago doesn’t. The result is a curated set of genuinely material hits — not a flood of noise to sift through. What does the audit trail look like for screening decisions? Every match — cleared or escalated — is logged automatically with its rationale, data source, and outcome. When your regulator asks how screening decisions were made, you show them the trail. You don’t reconstruct it from emails or case notes. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Document analysis for KYC and KYB | AI-powered entity enrichment — Strise URL: https://strise.ai/product/document-analysis Turn documents into structured entity data in minutes. AI extracts UBOs, directors and ownership, reconciles them with the registry, audit-ready by default. - [Home](https://strise.ai)/ - Product / - Document analysis #### Stop reading docs. Start making decisions. Turn any document into structured entity data, reconciled against the registry, in minutes. No manual entry. No missed risk. [Book a demo](https://strise.ai/book-a-meeting-with-strise) Risk scoring icon - British Land - KLP - BDO - Zevoy - Givestar - Sparebanken Norge - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Sound familiar? 01 The registry goes dark the moment you cross a border Luxembourg locked its register behind legitimate-interest checks. Cayman, Angola, BVI, opaque by design. Even where data exists, access runs out, and the case goes back to manual. 02 The document arrived last Tuesday. It’s still unread The trust deed sat in a shared inbox. The customer questionnaire response is buried in a thread. The case is open because nobody’s had time to read a PDF. 03 Your analysts are re-keying percentages from PDFs Directors, shareholdings, registered addresses. Transcribed by hand, one line at a time. Mistakes creep in. And the real work, the judgment, hasn’t started yet. 04 Email, Salesforce, compliance, no version of the truth The document lives in four places. Nobody has both sides open at once, so nobody actually compares. The field says whatever the last person typed. 05 You’re constantly re-finding what the registry should have Ownership moves. Directors change. Registries update when they update. Your team keeps going back out to confirm what’s current, then entering it by hand, because the filing hasn’t caught up yet. 06 A thousand entities to re-review. The plan is spreadsheets Remediation means working through five, seven, ten thousand entities, the same compare-and-update exercise, one at a time. Today it lives in spreadsheets thrown between systems, with no way to scale beyond more people and a longer week. ##### The difference Strise makes. 70% less time on complex entity entries. Minutes from document drop to reconciled entity. Down from a half-day. 100% of enriched fields cite a source and a timestamp. Regulator-ready by default. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## From document to decision, automatically. - 1 ###### Drop the document in PDF, CSV, JSON, text. Straight onto the entity. - 2 ###### AI extracts. Fields mapped Names, roles, percentages, addresses. Parsed to the same fields Strise uses everywhere. - 3 ###### Compared with registry Side-by-side. Real differences flagged, formatting noise filtered, hidden risk exposed. - 4 ###### Analyst decides Accept, override, or edit. Every change lands with source and timestamp attached. ##### Automate Document analysis. ###### Closes the registry blind spots. Jersey, Cayman, Luxembourg, Guernsey, BVI, Angola. Where public registries fall short, a customer document closes the gap on day one. No waiting on a new data-provider integration. User interface for document analysis with options to upload, request, or compare documents; lists data sources including PDFs, CSV, and JSON files with verification and processing statuses. ###### Only real differences surfaced. New shareholder. Changed address. Missing UBO. Sub-threshold aggregation that adds up to control. Comparison showing Bambu Bandit’s 18% ordinary shares as shareholder from registry versus 49% preference voting control as ultimate beneficial owner, verified with documents and flagged with 2 warnings. ###### AI proposes. You decide. Nothing updates without a human decision. Full control, aligned with AMLA and EU AI Act expectations. List showing discrepancies found including mismatched shareholders count, family ownership differences, undisclosed preference share class, and detected PEP exposure related to Shelly Shark’s spouse on a bank board. ###### Audit-ready by default. Every change stamped with source, timestamp, and the person who approved it. When a regulator asks how you reached a decision, you open one file, not five. Audit trail showing recent actions: PDF reviewed with 3 attachments 3 minutes ago, beneficial owner Bambu Bandit added 1 day ago, new sanction hit for Sleazy Steve on OFAC SDN list dated 1 Feb 2026, AI extraction complete with 11 fields extracted on 24 Jan 2026, and beneficial owner Sleazy Steve removed on 24 Jan 2026. ##### Explore other products. - [KYB onboarding](https://strise.ai/product/kyb-onboarding) - [KYC onboarding](https://strise.ai/product/kyc-onboarding) - [Monitoring (ODD)](https://strise.ai/product/ongoing-due-diligence) - [Identity verification](https://strise.ai/product/id-verification) - [UBO discovery](https://strise.ai/product/ubo-discovery) - [Remediation](https://strise.ai/product/remediation) - [Risk scoring](https://strise.ai/product/risk-engine) - [Screening](https://strise.ai/product/screening-and-alert-triage) ##### Fewer tabs. More documents read. See what your team’s day looks like when every document lands as reviewed, reconciled data on the entity. [Book a demo](https://strise.ai/book-a-meeting-with-strise) ##### Things we get asked. Answered. What document types does Strise actually read? PDF, CSV, JSON, and plain text — what your clients actually send. Trust deeds, articles of association, incorporation certificates, registry extracts, questionnaire responses, structure charts, annual reports, ownership registers. If it’s computer-readable, Strise parses it. How does Strise handle documents in other languages? Multilingual by default. The extraction model reads the document in its native language and maps every field to the working language in your Strise instance. A Luxembourg trust deed in French, an Estonian registry extract, a German questionnaire response, the entity data lands in the same structured fields either way. How do you stop the AI from hallucinating? Every extracted value is grounded in the source: the field, the page, the exact text it came from. Low-confidence extractions are surfaced for review, not auto-applied. And nothing, high-confidence or low, is written to the entity without an analyst’s click. Accuracy isn’t trusted; it’s verified, row by row. What about privacy? Documents stay isolated to your Strise instance. Nothing is shared across customers. Nothing trains a shared model. Your enriched data sharpens your own intelligence layer, not anyone else’s. GDPR-compliant by default, with DPAs and data-processing documentation available at any point during evaluation. How does this hold up under the EU AI Act? Every AI step is labelled and logged. Extraction is proposed, not committed, the analyst accepts, overrides, or edits before anything touches the entity. Every automated decision maps to a human review. When an examiner asks what the model did and what the human decided, the audit trail answers both. What happens when the registry later publishes something that contradicts a document-sourced edit? It triggers a monitoring alert. Nothing silently overwrites an analyst decision. The registry update and the original document-sourced edit sit side-by-side in the same compare view your team used to create the record. You reconcile the conflict once, and the audit trail keeps both versions. ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ## Resources ### The Laundry URL: https://strise.ai/the-laundry The Laundry is our podcast connecting AML, compliance, and financial crime to the real world. Where the financial crime compliance community thinks out loud. #### the laundry podcast The Laundry is our podcast connecting AML, compliance, and financial crime to the real world. Where the financial crime compliance community thinks out loud. 10,000+ monthly listeners — and the conversations here also shape how we build Strise. Join us!* I agree to receive other communications from Strise, including the Laundry Community. You can find more details in our [Privacy Policy](https://strise.ai/privacy-policy).* Website Sign me up People socializing in a dimly lit venue with a large illuminated ‘Laundry’ sign in the background. Smiling woman with light skin and red hair tied back wearing a blue ribbed turtleneck sweater against a neutral background. Listen on Apple Podcasts Listen on Spotify Watch on YouTube Spotify sets its own cookies, so this player stays off until you allow functional cookies. You can change that in your [cookie preferences](https://strise.ai/cookie-policy). Open cookie preferences ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Meet the Team — Strise AI-Powered AML Compliance Platform URL: https://strise.ai/about-us/meet-the-team Meet the people building the autonomous FinCrime department. Backed by Atomico, trusted by 70% of Nordic tier-1 banks and S&P 500 companies. Three skiers wearing helmets and goggles sitting on a ski lift above a snowy forest. Group of twelve skiers posing cheerfully on a snowy mountain slope with ski poles raised, scenic mountainous landscape in the background. Group of five skiers standing on snow-covered slope with ski gear under a clear blue sky. Group of four people in a cozy living room working on laptops and using a smartphone with coffee mugs on a table. Group of people sitting and standing on wooden steps outside, many wearing hats and smiling, with a small colorful piñata hanging under a white canopy. People wait in line at night near a red London double-decker bus in front of a bright red advertisement for The Laundry podcast. Man in black sweater holding a white mug, engaged in conversation with another person whose hands are visible. Two colleagues working at desks facing computers in a bright office; a woman in the foreground smiles at the camera. Group of people sitting and listening to a man standing and speaking in a cozy wooden room with large windows and a projector. #### Building the end of manual fincrime work. With Strise, manual FinCrime work ends. New customers onboard in hours, not days. Risk is monitored continuously and surfaced upfront. Ownership is mapped. False positives resolved. Audit trail built throughout. What’s left is judgement work, and that stays with your team. That’s what we’re building. This is the team building it. ##### Backed by investors who get the scale of the problem Atomico Strise is backed by globally respected investors including Atomico , Maki.vc , and Curiosity . Because automating financial crime compliance isn’t a nice-to-have. It’s infrastructure. ###### Trusted where the stakes are highest 70% of Nordic tier-1 banks run on Strise, alongside companies listed on the S&P 500. When regulation is non-negotiable, teams choose a platform they can rely on. ###### Oslo (HQ) · London Built in Norway, operating across Europe. We’re small enough to move fast and senior enough to know what we’re doing. Two men working at desks with computers in a bright office space. Press ##### Strise and shine Where we’ve been featured, what we’ve said, and why financial crime compliance keeps coming up. - Four people standing together in a dimly lit setting with a Ministry of Sound logo illuminated in the background. ###### How Strise redefines AML automation Forbes logo Read article > - Woman with red hair speaking on stage holding a remote, with a blurred background text starting with ‘Strise’. ###### Strise raises $11M in funding round Bloomberg logo Read article > - Marit Rødevand, CEO and co-founder of Strise, speaking into a microphone while wearing headphones in a studio with warm lighting. ###### Interview with Strise CEO on BBC BBC logo - Abstract black gear icon inside concentric circles on a gradient yellow to red background with circled text ‘MIC 2024’ and ‘STRISE’. ###### One of the world’s most innovative Fast Company logo Read article > - Woman with light skin and red hair braided, wearing a dark green turtleneck sweater, smiling softly against a gradient blue-green background. ###### 16 Norwegian startups to watch Sifted logo Read article > - ## Team Strise The people behind the autonomous operation. - Jørgen Hoff Amundsen Jørgen Hoff Amundsen UX Designer - Mia Myking Bjørnsen Mia Myking Bjørnsen Interim Head of CS - Marit Rødevand Marit Rødevand Founder and Chief Executive Officer - Edward Bragg Edward Bragg Strategic Partnerships Manager - Tom Potter Tom Potter Senior Software Engineer - Eric Börjesson Eric Börjesson Senior Software Engineer - Andrew Kessler Andrew Kessler Customer Success Manager - Magnus Hagem Magnus Hagem Head Of Legal - Miguel Ochoa Miguel Ochoa Digital Marketing Specialist - Kim Kvaase Kim Kvaase Head Of People - Kim Liam Kim Liam Office Manager - Robin Lycka Robin Lycka Solution Architect - Fredrik Riiser Fredrik Riiser Head Of Strategy & Business Development - Lars Lunde Birkeland Lars Lunde Birkeland Chief Marketing Officer - Benjamin Brown Benjamin Brown Account Executive - Alex Street Alex Street Strategic Account Executive - Synne Marie Sollie Synne Marie Sollie Product Manager - Ana Vizcarrondo Ana Vizcarrondo Product Manager - Torkjel Hongve Torkjel Hongve Senior Software Engineer - Thilo Schmidt Thilo Schmidt Head Of Product - Marit Røkenes Marit Røkenes Lead Designer - Stein-Erik Bjørnnes Stein-Erik Bjørnnes Staff Engineer - Adrian Trzeciak Adrian Trzeciak Head Of Engineering - Martin Brandhaug Martin Brandhaug Staff Engineer - Henrik Nårstad Henrik Nårstad Senior Software Engineer - Sigurd Berglann Sigurd Berglann Staff Engineer - Sivert Randem Sivert Randem Senior Software Engineer - Bjørn Håvard Hoffman Bjørn Håvard Hoffman Senior Software Engineer - Mathias Møller Mathias Møller Senior Software Engineer ##### Strise was built on a simple belief: compliance teams shouldn’t spend their days on work AI can do. The Autonomous FinCrime Department handles routine onboarding, continuous monitoring, ownership mapping, and alert triage, automatically. Your analysts focus on the cases that actually need them. Everything else runs. ##### See what your team could stop doing Most compliance teams are one platform away from cutting 80% of routine work. Let us show you what that looks like for your operation. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Yes, Chef! Compliance is judgement work — Strise® URL: https://strise.ai/yes-chef Strise automates the routine in onboarding, monitoring, ownership mapping, and alert triage, so analysts spend their time on the cases that actually matter. #### Compliance is judgement work. Strise automates the routine in onboarding, monitoring, ownership mapping, and alert triage — so analysts spend their time on the cases that actually matter. Play full videoShare the film ##### Made-up names. Real pain. The work that matters gets buried under repetitive reviews, false positives, fragmented ownership checks, manual monitoring, and copy-pasting. Six chefs you’ve never met. Six pains you know too well. - Woman with curly hair in black shirt, thoughtful pose with hand on chin, papers on wall behind her. Dee Legence Head of Financial Crime (MLRO) “I sign for every decision this team makes. The team makes more than I can read.” [What Strise does for Dee](https://strise.ai/product/risk-engine) - Man in white chef uniform and tall hat standing in front of a wall covered with papers. Charlie Eck Enhanced Due Diligence Analyst “Cyprus holding. BVI trust. Cayman nominee. A corporate trustee. This was supposed to be one onboarding.” [What Strise does for Charlie](https://strise.ai/product/ubo-discovery) - Chef in white uniform and hat stands against a wall covered with pinned papers. Carlos Alerta Sanctions Screening Analyst “Eighteen years investigating financial crime. Today I clicked ‘false positive’ three hundred times. My CV needs an update.” [What Strise does for Carlos](https://strise.ai/product/screening-and-alert-triage) - Male chef in white uniform and hat stands with arms crossed against wall covered in papers. Remy Diation Senior Corporate Onboarding Analyst “My team grew by four people this year. The backlog grew by four thousand. None of us trained for this.” [What Strise does for Remy](https://strise.ai/product/kyb-onboarding) - Woman chef with arms crossed wearing white uniform and hat looks up thoughtfully against receipt-covered wall. Polly Cee CDD Analyst “The customer changed ownership in March. We learned in October. The new owner had been on a list since January.” [What Strise does for Polly](https://strise.ai/product/ongoing-due-diligence) - Worried chef in white uniform touching his forehead with many papers pinned on the wall behind him. Bjørn Tucheck Periodic Review Analyst “I finished last year’s periodic reviews in March. It is now April. This year’s are already due.” [What Strise does for Bjørn](https://strise.ai/product/remediation) ##### Manual FinCrime work. ends now. - ### Onboarding moves in hours, not weeks Corporate onboarding stalls when registries, ownership structures, screening, and documents live across disconnected systems. Strise pulls them together and resolves the entity before the first review starts. - ### Only material risk reaches your analysts Most monitoring alerts are noise. Strise continuously evaluates ownership changes, sanctions exposure, and risk signals against your risk model so analysts focus on what changed, not what did not. - ### Ownership structures resolved Strise uses AI to cross-reference registers, documents, and customer data into one ownership structure, because no single register is enough. Verified across sources, updated as entities change. - ### Every decision stays traceable Every source, risk evaluation, analyst action, escalation, and override is captured automatically inside the case history. When regulators ask how a decision was made, the reasoning chain already exists. Woman chef in black uniform stands in a busy modern kitchen with chefs working behind her. ##### Let analysts do analyst work. The cases that need judgement are waiting. Everything else, Strise handles. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### Strise — AML Automation & Compliance Blog URL: https://strise.ai/blog Sharp takes on AML, KYC and financial crime. Built for compliance teams who’d rather act than read 2,000-word think pieces. #### Straight talk, fresh updates, and a peek at what we’re building Follow our journey as we build smarter AML automation, share product updates, and highlight what’s next at Strise. ###### BDO automates the fight against financial crime with Strise BDO, one of Norway's leading advisory and audit firms, has selected Strise as its technology provider for customer due diligence and anti-money laundering (AML). The agreement makes Strise's AI platform a central part of how BDO meets its anti-money laundering obligations. July 22, 2026• Blog ###### How to clear your KYC remediation backlog before AMLA supervision begins Most KYC remediation projects stall because they treat a data problem as a headcount problem. Here's how to clear your back book in weeks, not quarters. April 27, 2026• Blog ###### Best AML software for banks and payment companies, and what good looks like in 2026 Banks are rethinking AML foundations as AI-native, data-driven software enables real-time detection, explainable automation, and lasting compliance trust. October 17, 2025• Blog ###### The hidden owner. The clear UBO map Strise centralises UBO discovery in one structured view so teams spend less time verifying ownership and more time assessing risk. No backtracking. June 11, 2025• Blog ###### Your KYB shouldn’t break when data does Create and review companies without registry data. Strise keeps KYB structured, audit-ready, and traceable even when public records are unavailable. May 14, 2025• Blog ###### Best KYC and KYB software tools in 2026: A practitioner’s guide The best KYC and KYB software tools in 2026, compared by category: data providers, AI platforms, identity verification and lifecycle tools, plus what good looks like for perpetual KYC. September 16, 2025• Blog ###### Top AML software for banks in 2026: AI‑native platforms comparison Comparing six AI-native AML platforms for banks in 2026: Strise, Silent Eight, Arva, Duna, Spektr, and Greenlite AI. Features, strengths, and trade-offs. June 12, 2026• Blog ###### Best AML monitoring tools for 2026: AI‑native platforms comparison Compare AI-native AML platforms in 2026. See how Strise, Duna, Greenlite, Spektr, and Bits Technology handle automation, risk, and compliance scale. August 21, 2025• Blog ###### New: Enhanced editing capabilities Strise adds comment functionality to UBO map edits, improving collaboration and reporting while streamlining AML workflows for compliance teams. December 5, 2024• Blog ###### Are low-risk KYB onboarding cases clogging your pipeline? Automate onboarding screening with risk-based prioritisation. Strise sorts KYB and AML queues so high-risk cases get reviewed first, automatically. May 28, 2025• Blog ###### More control, less noise: Customise your sanctions matching Strise now lets compliance teams customize sanctions matching criteria, aligning flagged results with company risk policy for better workflow control. December 5, 2024• Blog ###### Formue chooses Strise to end manual KYB Formue partners with Strise to automate KYC and AML workflows, speed up onboarding, and improve risk assessment for complex wealth management clients. December 1, 2025• Blog ###### The problem no one talks about in KYB Strise transforms KYB from slow manual checks into real-time verification, connecting to global registries and flagging discrepancies automatically. March 5, 2025• Blog ###### Cut the noise: Smarter Adverse Media Screening (AMS) Strise links entities to official registry records, adds precise AMS filtering, and streamlines navigation to make adverse media screening faster. August 6, 2025• Blog ###### PSA Solutions taps Strise to tackle AML for law firms PSA and Strise partner to give law firms AML automation within Custodian, unifying due diligence, monitoring, and compliance in one platform. August 20, 2025• Blog ###### PEP and sanctions screening and dispositioning, done right Structured, auditable PEP and sanctions review, with Agentic AI making autonomous, explainable decisions. Faster reviews, smaller backlog, more time for high-value cases. April 30, 2025• Blog ###### Real-time continuous monitoring at portfolio level The Strise Portfolio Health Dashboard gives compliance leaders a real-time view of portfolio risk with automatic AML updates and centralised monitoring. March 19, 2025• Blog ###### The reality of identity verification in compliance Strise connects IDV directly to KYB, automating verification, reducing drop-offs, and centralising compliance workflows in one secure platform. April 2, 2025• Blog ###### Faster sanction insights, without the clicks Get faster access to detailed sanction information. Strise's latest update shows exactly why a company or person has been sanctioned, fewer clicks needed. November 18, 2024• Blog ###### Strise partners with Kyckr to automate KYB beyond document retrieval Strise and Kyckr automate KYB with real-time data from 300+ registries, enabling instant verification and auto-flagging discrepancies globally. March 6, 2025• Blog ###### Why AML and CTF compliance really matter right now AML compliance is stricter than ever. Learn how automation, AI, and Strise help financial institutions meet EU/UK rules, reduce risk, and build regulator trust. October 13, 2025• Blog ###### Real-time ownership editing is here Edit UBO structures directly in Strise with real-time updates when ownership changes. Cleaner data, faster decisions, and audit-ready KYB from day one. November 18, 2024• Blog ###### Signicat and Strise launch unified platform for Digital Identity Verification and KYB automation Signicat and Strise partner to deliver modular KYC and AML compliance. Streamline onboarding, identity verification, and monitoring with no-code setup. February 26, 2025• Blog ###### Strise and Stacc join forces to replace manual AML with scalable automation Strise and Stacc join forces to replace manual AML with scalable automation, combining advanced data intelligence with user-focused compliance tools. December 5, 2024• Blog ###### PEP & Sanctions Matching: Fewer false positives Two reasons why a Strise search yields fewer false positives than any other PEP and sanctions screening tool. Precision matching that actually works. November 18, 2024• Blog ###### KLP joins the growing list of financial institutions connecting with Strise's AML Automation Cloud KLP joins Strise’s AML Automation Cloud with Stacc to automate AML processes, cut manual work, and unify compliance data. December 5, 2024• Blog ###### Fremtind chooses Strise to streamline customer case handling and enhance compliance efficiency Fremtind adopts Strise's AML automation platform to streamline Enhanced Due Diligence and standardise compliance in Norway's insurance sector. December 5, 2024• Blog ###### Storebrand partners with Strise to transform AML operations Storebrand partners with Strise to integrate AML Automation Cloud across its business units, serving 55,000+ corporate and 2.2M retail customers. March 11, 2025• Blog ###### UI: New Settings modal November 18, 2024• Blog ##### Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ### AML Automation FAQ –– Strise URL: https://strise.ai/faq Common AML compliance, KYC/KYB onboarding, and risk management questions answered. Learn how AI automates screening and reduces false positives. #### AML automation FAQ This FAQ answers common questions related to AML compliance, customer onboarding, and risk management. How is AI transforming AML software? AI is transforming AML software by shifting compliance teams from reviewing every case manually to reviewing only the cases that genuinely need human judgment. Traditional AML platforms collect and present data. Modern AI platforms like Strise make decisions on routine cases, onboarding low-risk businesses, completing unchanged periodic reviews, clearing screening alerts with no material match, and route only the exceptions to analysts. The result is a compliance function that processes far higher volumes without proportionally more headcount. The specific capabilities driving this shift: Automated entity resolution. AI reconciles inconsistent company names, ownership records, and cross-border data into a single trusted profile, removing the manual effort of assembling a customer picture from multiple sources. Event-driven risk monitoring. Rather than batch re-screening on a fixed schedule, AI-powered platforms detect changes, a new sanctions hit, a change in ownership, an adverse media mention, and trigger an alert the moment they occur. Auto-approval. Low-risk customers who meet configurable thresholds are approved without analyst review. The analyst queue contains only the cases where a decision is genuinely required. Explainable audit trails. Every automated decision is logged with a timestamped rationale, meeting FCA and EU AML directive requirements for demonstrating what was done and why. The compliance teams seeing the most impact are those that have stopped trying to accelerate the existing process and have instead changed which cases reach a human at all. How do you automate onboarding and due diligence? Automating onboarding and due diligence means connecting to live data sources, assembling a complete risk picture automatically, and approving low-risk applicants without manual analyst involvement. How Strise automates the onboarding workflow: - Data assembly. Company registration data, ownership filings, sanctions lists, PEP databases, and adverse media are pulled and harmonised into a single customer profile. No manual searches across multiple sources. - Entity resolution. AI and entity resolution links individuals and companies across variant names and jurisdictions into one reliable profile, supporting UBO discovery and international verification. - Real-time screening. Sanctions, PEP, UBO, and adverse media checks run automatically as data is assembled. Results are ranked by risk, genuine concerns surface to analysts, obvious non-matches are cleared. - Risk scoring. A configurable risk engine generates a score based on entity type, jurisdiction, ownership structure, screening results and more. - Automated routing. Applicants below the risk threshold are approved without analyst review. Higher-risk applicants route to the queue with all relevant context already built. - Audit logging. Every step is timestamped and recorded, creating a defensible record for regulatory review. The outcome for most customers: low-risk corporate onboardings complete in hours rather than days, and analyst capacity shifts from routine reviews to the cases that actually require judgment. Strise is used by compliance teams at banks, fintechs, payment firms, and professional services firms across the Nordics, UK, and Europe. How do you streamline KYC and KYB processes for financial institutions? Streamlining KYC and KYB for financial institutions means reducing the manual work at each stage of the process, data collection, identity verification, ownership mapping, screening, and risk decisioning, without reducing compliance quality or audit defensibility. The most effective approach involves three changes to how the process is structured. First, centralise data. Most compliance teams run separate tools for registry checks, screening, document verification, and case management. Switching between systems adds time and creates gaps in the audit trail. A unified platform that pulls all data into one workflow cuts this overhead significantly. Second, change what reaches an analyst. KYB processes slow down when every case, regardless of complexity, requires a human to review it before it progresses. Configuring auto-approval rules for low-risk business profiles, and automated renewal for unchanged periodic reviews, removes the bottleneck without removing oversight. Third, build the audit record throughout. FCA-regulated firms and those operating under EU AML directives need to demonstrate that every compliance decision was made on a documented basis. A platform that logs every data source, screening result, and decision point automatically is faster to audit and faster to remediate if a finding arises. Strise implements all three. Customers report onboarding cycle reductions of up to 80% for low-risk corporate clients, with analyst time focused on complex structures, high-risk cases, and the work that actually requires professional judgment. What are the best tools for real-time compliance monitoring? The best real-time compliance monitoring tools deliver continuous, event-driven risk updates rather than periodic batch rescreening, and surface material changes to analysts the moment they occur rather than during a scheduled review cycle. What to look for: - Event-driven alerts over batch processing. A tool that only rescreens customers monthly will miss a sanctions designation that happens on day two. The best platforms monitor continuously and trigger alerts in real time when a relevant change occurs. - Ownership change detection. Sanctions and PEP checks on the original directors are not sufficient if the ownership structure changes. Look for platforms that monitor Companies House and equivalent registries for new filings and ownership transfers. - Ongoing due diligence that replaces periodic reviews. Platforms that handle event-driven monitoring can close periodic reviews automatically when no material changes have occurred, replacing a manual re-review with a logged, defensible automated check. - Full audit trail on every alert. Regulators do not just want to know that a change was detected. They want to know what action was taken, by whom, and on what basis. Strise’s monitoring runs continuously across registries, sanctions lists, PEP databases, and adverse media. When something changes, the relevant customer profile is rescored and the compliance team is alerted immediately. Periodic reviews that detect no material change close automatically with a full audit record. Used by enterprises including British Land, Nordea, Corpay, and PwC Norway. How do you reduce false positives in sanctions and PEP screening? False positives in sanctions and PEP screening are reduced by combining precise match configuration with AI agentic alert ranking, so analysts review genuine risks rather than spending their day dismissing obvious mismatches. The main levers for reducing false positives: Match threshold configuration. Setting fuzzy versus exact-match levels for each watchlist allows teams to balance sensitivity against noise. A common name in a low-risk jurisdiction warrants a different threshold than a high-risk entity in a sanctioned country. Metadata filters. Date of birth and nationality data eliminate the majority of name-coincidence false positives immediately. Without these filters, common names generate alert volumes that are unworkable at scale. List scope. Not every list is relevant to every business type or jurisdiction. Screening a domestic retail bank against every global sanctions list generates noise. Configuring which lists apply to which customer segments brings alert volume in line with actual risk exposure. AI alert ranking. AI models trained on resolved cases learn what genuine matches look like versus coincidental ones, prioritising likely true positives and filtering low-risk alerts before they reach analysts. Strise applies all of these, with each configuration layer independently adjustable per customer segment. The outcome in live production: up to 40% fewer false positives, with analysts focused on the alerts most likely to represent genuine financial crime risk. One important note: false positives matter, but they are rarely the primary driver of compliance team frustration. Backlog volume, periodic review capacity, and the cost of manual case handling come up far more frequently in conversations with MLROs and compliance operations leads. A screening tool that reduces false positives but does not address the structural backlog problem solves the easier part of the challenge. How do you automate AML checks and risk scoring? AML checks and risk scoring are automated by connecting to live data sources, applying screening and entity resolution continuously, and generating a real-time risk score based on configurable weighted criteria, without manual data entry or case-by-case analyst review for routine profiles. How the process works in Strise: - Continuous data ingestion. Strise connects to business registries, sanctions lists, PEP databases, adverse media feeds, and ownership filings. Data updates trigger immediate rescoring. - Entity resolution. AI maps individuals, companies, shareholdings, and ownership layers into a unified profile. Indirect relationships, a director who is also a beneficial owner of a connected entity, are surfaced automatically. - Automated screening. Every customer profile is screened against relevant watchlists in real time. New list updates trigger immediate rescreening across the entire portfolio. - Weighted risk matrix. A configurable scoring model applies weights to risk factors, jurisdiction, entity type, PEP exposure, adverse media, ownership complexity, generating a numeric risk score and tiered risk category. - Rule-based routing. Profiles below the risk threshold are auto-approved or auto-renewed. Profiles above it route to analysts. The threshold and rules are fully configurable. - Audit-ready output. Every score, data source, and rule applied is logged with a timestamp, producing a defensible record for regulators. The result: compliance teams process higher volumes with the same headcount, and analyst time concentrates on the cases where human judgment is actually required. How do you automate KYC and KYB? KYC (Know Your Customer) and KYB (Know Your Business) are automated by replacing manual data collection, identity verification, and risk assessment steps with connected, AI-driven workflows that handle low-risk cases end-to-end without analyst involvement. Strise automates KYC and KYB across five areas: Data collection. Registry data, company filings, and ownership information are pulled automatically from live sources. For individuals, integrated identity verification handles document checks and biometric verification inside the same workflow. Entity resolution. Duplicate records, name variants, and cross-border data inconsistencies are reconciled by entity resolution and AI into a single trusted profile, supporting accurate UBO discovery across jurisdictions. Screening. Sanctions, PEP, adverse media, and UBO screening runs in real time as the profile is assembled, with results ranked and routed based on configurable risk rules. Risk decisioning. A weighted risk matrix generates a score and category automatically. Low-risk profiles meeting configurable thresholds are approved without manual review. Ongoing compliance. After onboarding, profiles are monitored continuously. Ownership changes, new sanctions hits, and adverse media triggers prompt immediate rescoring and alert. For most financial institutions, automating KYC and KYB reduces onboarding cycle time by up to 90% for low-risk clients and eliminates a significant portion of the manual due diligence work that currently reaches compliance analysts. The cases that still require analyst judgment, complex ownership structures, high-risk jurisdictions, unusual beneficial ownership arrangements, receive the time they deserve because the routine work is no longer competing for the same resource. How do you automate UBO verification? UBO (Ultimate Beneficial Owner) verification is automated by using graph analytics and live registry data to map direct and indirect ownership, identify natural persons controlling 25% or more of an entity, and monitor that ownership structure for changes, without manual construction of ownership trees or spreadsheet-based tracing. How Strise automates UBO verification: Live data connections. Strise connects to business registries, filings, and ownership databases across the UK and international markets. Data is pulled automatically rather than manually searched. Graph-based ownership modelling. AI maps companies, individuals, share classes, voting rights, and jurisdictional structures into an ownership graph. Direct, indirect, and cross-border control is calculated automatically, including through layered holding structures. International entity verification. Profiles are enriched across borders, including screening for sanctions, PEP exposure, and adverse media for all persons identified in the ownership chain. Real-time ownership monitoring. When a new filing is made, a director change, a revised shareholding, a new holding structure, Strise detects it, updates the graph, rescores the entity, and alerts the compliance team if the change is material. Editable and auditable. For jurisdictions with limited public data, analysts can manually enrich the ownership record. Every addition or amendment is logged and attributed, maintaining a defensible audit trail. The most common challenge in UBO verification is not identifying the top level of ownership, it is tracing through multi-layered structures in jurisdictions where public registry data is incomplete. Strise handles the data-intensive parts of that process automatically, reducing the manual effort of building and maintaining accurate beneficial ownership records. How does AI improve financial compliance? AI improves financial compliance by handling the parts of the process that follow a predictable pattern, routine due diligence, unchanged periodic reviews, low-risk onboarding, standard screening, so that trained compliance professionals can focus on the parts that require actual judgment. The practical improvements fall into four areas: Volume handling. A compliance team’s capacity is bounded by how many cases analysts can process per day. AI removes a large proportion of routine cases from the analyst queue entirely, increasing effective capacity without increasing headcount. Speed. Automated data assembly, real-time screening, and rule-based decisioning reduce onboarding cycle times significantly for low-risk customers. A process that took days of manual work completes in hours. Consistency. Human review is subject to variation, different analysts apply thresholds differently, particularly under pressure. Rule-based and AI decisioning applies the same criteria to every case, reducing inconsistency in risk outcomes and improving the defensibility of the audit record. Monitoring coverage. Manual compliance cannot continuously monitor thousands of customers for new sanctions hits, ownership changes, or adverse media. AI-powered event-driven monitoring does this automatically, alerting teams when something material changes rather than waiting for the next scheduled review. The important caveat: AI improves financial compliance when it is applied to the right parts of the process. Compliance decisions that require contextual judgment, regulatory interpretation, or investigative reasoning still need a human. The value of AI is in separating the cases that need that judgment from the cases that do not, and handling the latter without involving the former. How do you evaluate and compare AML automation vendors? Evaluating AML automation vendors effectively means going beyond feature checklists to understand what each platform genuinely automates versus what it streamlines while still requiring significant analyst time. A practical evaluation framework: 1. Clarify what is actually automated. Ask each vendor: after implementation, which case types close without analyst review, and what percentage of your current volume would that represent? A platform that automates data gathering but still routes every case to an analyst for decision is not solving the backlog problem, it is making the same bottleneck slightly faster. 2. Test the periodic review workflow specifically. Periodic reviews, mandatory re-assessment of the existing client portfolio on a fixed schedule, are one of the highest-volume, most time-consuming parts of compliance operations. Ask vendors how they handle a periodic review on a client whose profile has not changed. The answer reveals how much automation is real versus aspirational. 3. Evaluate the audit trail depth. Regulated firms need to demonstrate not just that a check was run, but what data was used, what rule was applied, and what decision was made. Test whether the platform produces a regulator-ready record for both automated decisions and analyst overrides. 4. Run a proof of concept with your own data. Vendor-provided demo data is optimised to show the platform well. A POC using your actual case types, your volume, and your risk rules will reveal whether the automation rates quoted apply to your situation. Measure analyst time per case before and after, not just headline feature counts. 5. Assess total cost of ownership. Include implementation time, configuration effort, and ongoing maintenance alongside licensing fees. A platform that requires three months of configuration to achieve the automation rates quoted has a different total cost than one that deploys in four weeks. Strise offers proof-of-concept implementations scoped to your volume and case types. If the platform is the right fit for your situation, that will be apparent from the results. If it is not, it is better to know before committing. --- ### KYC Remediation: Process, Backlogs & Software — Strise URL: https://strise.ai/guides/kyc-remediation What KYC remediation is, how the process works, and how banks clear back-book backlogs before AMLA — manual vs. automated approaches compared. #### KYC Remediation: The Complete Guide Author Miguel Ochoa Growth and GTM engineer — Strise® Table of contents Table of contents What is KYC remediation? Types of KYC remediation Why remediation backlogs build up The regulatory drivers behind remediation The KYC remediation process, step by step 1. Gap analysis 2. Risk-based prioritisation 3. Client outreach 4. Document processing 5. Decisioning and audit trail 6. Hand-off to ongoing monitoring How to scope and plan a remediation programme The risk-based approach: segmenting your portfolio Data and sources that power remediation Manual vs. automated remediation Metrics and KPIs to track Common pitfalls Choosing KYC remediation software A worked example KYC remediation and the AMLA deadline KYC remediation is the process of reviewing, updating, and correcting existing customer files so they meet your current data standard and regulatory requirements. In practice it means reopening cases you onboarded months or years ago, finding what’s missing or out of date, and getting the right documentation in place — before a regulator asks why it wasn’t done sooner. For most banks it isn’t one task. It’s a programme that spans thousands of customer files, and the back book grows staler every quarter it waits. This guide covers what remediation is, the different types, the regulations driving it, the process and how to plan one, the risk-based approach, the metrics that matter, the pitfalls that sink projects, and how to choose between doing it manually and automating it. ##### What is KYC remediation? KYC remediation brings already-onboarded customers back up to standard. A file needs remediating when its data has drifted from what your policy — or your regulator — now requires: an expired ID document, an ownership structure that has changed, a [missing UBO chain](https://strise.ai/product/kyb-onboarding), an unscreened related party, or a risk score that hasn’t been recalculated since the customer was first onboarded. It helps to place remediation against the things it is often confused with: - Onboarding happens once, at the start of a relationship. It establishes the baseline. - Periodic review is a scheduled re-check — every one, three, or five years depending on risk rating. - Perpetual (or ongoing) KYC replaces fixed schedules with continuous, event-driven monitoring, so a file updates when something changes rather than on a calendar. - Remediation is the corrective sweep that fixes a backlog of files that have already fallen behind standard — usually a large volume at once, and usually under time pressure. Put simply: onboarding and review keep individual files current; remediation fixes a population of files that aren’t. Definition KYC remediation is the process of reviewing, updating, and correcting existing customer files so they meet current data standards and regulatory requirements — clearing a backlog of already-onboarded customers whose information has gone stale or fallen below today’s compliance bar. ##### Types of KYC remediation Not every remediation programme looks the same. Knowing which type you’re running shapes the scope, the timeline, and the tooling. - Full back-book remediation. A complete re-papering of the existing customer base against a new standard. The largest and most resource-intensive type, usually triggered by a regulatory change or a supervisory finding. - Targeted remediation. A defined slice of the book — a single product line, jurisdiction, or risk tier — rather than the whole population. Often the smart way to de-risk the highest-exposure segment first. - Event-driven remediation. Files pulled for correction because something changed: a change in beneficial ownership, an adverse media hit, a sanctions designation, or a corporate restructuring. - Periodic-review-driven remediation. The backlog that accumulates when scheduled reviews fall behind. What should have been routine maintenance becomes a remediation project once the queue grows past the team’s capacity to clear it. ##### Why remediation backlogs build up Backlogs aren’t a sign that compliance teams don’t know their data is stale. They know. The problem is that the volume outpaces the capacity to clear it, for several compounding reasons: - Onboarding standards tighten over time. Files that passed three years ago no longer meet today’s policy. Every standard change retroactively creates a backlog. - Customers change. Ownership shifts, directors leave, businesses pivot, addresses move — and the file doesn’t keep up unless something forces it to. - Periodic review turns into perpetual backlog. When scheduled reviews slip, remediation candidates pile up faster than they’re cleared, and the queue compounds. - Regulatory change resets the bar. New rules force a re-assessment of the entire book against a new standard, often on a fixed deadline. - Manual capacity is fixed. The book grows and ages continuously; a manual team’s throughput does not. The gap widens structurally over time. ##### The regulatory drivers behind remediation Remediation is rarely optional for long. The current wave in Europe is driven by the new AML framework. The EU’s Anti-Money Laundering Regulation (AMLR) applies from 10 July 2027 . AMLA — the new EU anti-money laundering authority — is finalising guidelines on business-wide risk assessments through 2026, and a separate RTS on how supervisors score the risk profile of regulated firms applies from 31 December 2027 . Both pull from the same data points obliged entities are expected to assess themselves on. The practical translation: supervisors will score you on the quality of your customer data. The AMLR is built on a risk-based approach, and your business-wide risk assessment, your customer due diligence, and your ongoing monitoring all depend on the same foundation — customer data that is accurate, current, and defensible. A back book full of stale profiles, missing ownership chains, or risk scores that were never recalculated is exactly the kind of finding that triggers deeper supervisory scrutiny once direct supervision begins. This sits on top of long-standing expectations under 6AMLD and national supervisors (such as the FCA in the UK and Finanstilsynet in Norway), which have consistently treated poor data quality and unaddressed back-book risk as a governance failing in its own right. ##### The KYC remediation process, step by step A remediation programme runs through six stages. The difference between a project that drags for quarters and one that clears in weeks is how much of each stage is done by people versus by the platform. ###### 1. Gap analysis Compare every file in the portfolio against your current data standard and live registry data. Missing documents, outdated ownership information, stale risk scores, unscreened parties — all surfaced before a single analyst opens a case. This is the step that turns a remediation project from a multi-quarter slog into a structured, prioritised programme, because the scope is defined by data on day one rather than discovered case by case. ###### 2. Risk-based prioritisation Once gaps are identified, rank cases by regulatory exposure, [risk score](https://strise.ai/product/risk-engine), and gap severity. The team works the highest-risk cases first, not the ones that happen to sit at the top of a spreadsheet. ###### 3. Client outreach Where data is missing, the right request goes out: ID documents, UBO declarations, source-of-wealth evidence, whatever your policy requires. Submissions are tracked, and follow-up runs until the request is satisfied — so relationship managers aren’t spending weeks on administrative chasing. ###### 4. Document processing When documents arrive, they need to be [classified, extracted, and validated](https://strise.ai/product/document-analysis), then written back into the case file. Done manually, this is slow and error-prone; done automatically, the analyst opens a pre-prepared case rather than re-keying data. ###### 5. Decisioning and audit trail An analyst reviews the prepared case and makes the call. Every action — what was checked, what was found, what was updated, and by whom — is logged as it happens, so the audit trail is complete before anyone asks for it rather than assembled retrospectively. ###### 6. Hand-off to ongoing monitoring This is the stage most projects miss. Remediated files should flow directly into [continuous monitoring](https://strise.ai/product/ongoing-due-diligence), so the back book doesn’t quietly rebuild itself before the next review cycle. A remediation that ends with a clean file sitting untouched until the next periodic review is just building a future remediation project today. - 1 Gap analysis Compare every file against your current standard and live registry data to surface missing documents, stale risk scores, and outdated ownership. - 2 Risk-based prioritisation Rank cases by regulatory exposure and gap severity so the highest-risk files are worked first. - 3 Client outreach Request missing documents and track submissions, with follow-up running automatically. - 4 Document processing Classify, extract, and validate incoming documents, then write them back into the case file. - 5 Decisioning & audit trail An analyst reviews the prepared case; every action is logged as it happens. - 6 Hand-off to monitoring Remediated files flow into continuous monitoring so the back book doesn’t rebuild itself. ##### How to scope and plan a remediation programme Before any cases are worked, a remediation programme needs a plan. A workable framework runs in five moves: - Assess. Define the target standard. What does a complete, compliant file look like under your current policy and the incoming regulation? You can’t measure a gap without a benchmark. - Segment. Break the book into populations — by risk tier, product, jurisdiction, entity type — so the programme can be sequenced rather than tackled as one undifferentiated pile. - Prioritise. Rank segments and cases by exposure and gap severity. Decide what gets done first and what the acceptable timeline is for the rest. - Execute. Run gap analysis, outreach, processing, and decisioning — ideally in parallel across the portfolio rather than sequentially. - Monitor. Move remediated files into ongoing monitoring and report progress continuously, so completion is provable and the book stays current. ##### The risk-based approach: segmenting your portfolio A remediation programme that treats every file as equally urgent will always run out of time on the cases that matter most. The risk-based approach inverts that. In practice, you score each file on two axes: regulatory exposure (how much risk the customer carries — high-risk jurisdiction, complex ownership, PEP connections, adverse media) and gap severity (how far the file is from standard). A high-risk customer with a missing UBO chain is worked before a low-risk, fully documented file with a single expired address proof. For example: a portfolio of 10,000 entities might break down into roughly 800 high-exposure files with material gaps, 3,000 medium-exposure files with minor gaps, and 6,200 low-exposure files that are largely compliant. The risk-based approach puts the 800 first — retiring the bulk of the regulatory exposure long before the full population is cleared — and reserves lighter-touch handling for the long tail. Segment Approx. volume Handling High exposure, material gaps ~800 files Worked first — retires the bulk of regulatory exposure early Medium exposure, minor gaps ~3,000 files Second wave, standard remediation workflow Low exposure, largely compliant ~6,200 files Lighter-touch handling; the long tail ##### Data and sources that power remediation Remediation is only as good as the data it draws on. A complete file typically reconciles several sources: - Corporate registries for legal status, directors, and filings — the source of truth for whether a business still exists as recorded. - Beneficial ownership data to rebuild [UBO chains](https://strise.ai/product/ubo-discovery) that have shifted since onboarding. - Sanctions, PEP, and adverse media screening to catch designations and reputational risk that post-date the original check. - Identity verification for individuals where documents have expired or were never captured to current standard. The hard part isn’t any single source — it’s reconciling all of them against your standard, at scale, without an analyst doing it by hand for every file. ##### Manual vs. automated remediation Most remediation stalls for the same five reasons — and each one is a place automation changes the maths: - Scope. Manual programmes discover scope case by case, so it grows as the team works. Automated remediation defines scope by data on day one. - Outreach. Manual outreach falls on relationship managers chasing documents. Automated outreach and follow-up run on their own. - Prioritisation. Manual effort concentrates where it’s easiest. Automated ranking puts the highest-risk cases first. - Velocity. Manual velocity scales only with headcount. Automated processing runs in parallel — 10,000 files take roughly the same elapsed time as 100, because the bottleneck shifts from data gathering to analyst decisions. - Audit trail. Manual trails are assembled at the end, when whatever time is left determines their quality. Automated trails build from the first comparison run. Where manual programmes stall What automation changes Scope is discovered case by case, growing as the team works Scope is defined by data on day one Relationship managers chase missing documents Outreach and follow-up run automatically Effort concentrates where it’s easiest Cases are ranked by actual risk exposure Velocity scales only with headcount Processing runs in parallel — 10,000 files take roughly the same elapsed time as 100 The audit trail is assembled at the end The trail builds from the first comparison run The shift is simple in principle: instead of analysts finding and assembling data for every case, the platform does that work and analysts make decisions on pre-prepared, pre-ranked files. For teams that have run remediation with [Strise](https://strise.ai/product/remediation), portfolio-wide gap analysis completes in days rather than months, and the backlog clears faster than a manual programme allows because cases arrive ready to decide. ##### Metrics and KPIs to track A remediation programme you can’t measure is one you can’t prove to a supervisor. Track at least: - Completion rate overall and by risk tier — high-risk completion is the number a regulator cares about most. - Outstanding cases by risk tier , so exposure is visible at a glance. - Gap resolution rate — how quickly identified gaps are closed. - Outreach response rate and average time-to-document. - Throughput — cases cleared per week, and whether it’s keeping ahead of new candidates entering the queue. ##### Common pitfalls Most remediation programmes fail for predictable reasons. Watch for these: - Scope discovered too late. Without systematic gap analysis, teams start with the visible cases and only learn the true size three months in. - The wrong people doing outreach. Senior relationship managers spending weeks on document chasing instead of judgment work. - Backwards prioritisation. Effort going where it’s easiest rather than where exposure is highest. - Linear velocity. Throughput tied to headcount while the backlog grows on its own. - Audit trail as an afterthought. Documentation assembled after the fact, reflecting whatever time was left. - No hand-off to monitoring. Clean files left to go stale again, guaranteeing the next remediation project. ##### Choosing KYC remediation software When you evaluate tooling, weigh it against exactly where manual programmes break. A capable platform should offer: - Portfolio-wide gap analysis against your current standard and live data. - Risk-based ranking across the whole population, not just within batches. - Automated client outreach with tracking and follow-up. - Automated document classification and extraction . - An audit trail that builds automatically and progress reporting by risk tier. - A clean hand-off into ongoing monitoring , so remediation doesn’t repeat on a two-year cycle. Capability Why it matters Portfolio-wide gap analysis Defines scope by data on day one instead of discovering it mid-project Risk-based ranking across the whole population Highest-exposure cases get worked first, not batch by batch Automated client outreach Removes manual document chasing from relationship managers Document classification & extraction Analysts review pre-prepared cases rather than re-keying data Automatic audit trail & reporting by risk tier Completion is provable to a supervisor without retrospective assembly Hand-off into ongoing monitoring Stops the back book rebuilding itself on a two-year cycle For the wider view of how KYC and KYB tooling is shifting toward perpetual, event-driven monitoring, see our [practitioner’s guide to the best KYC and KYB software tools in 2026](https://strise.ai/blog/best-kyc-kyb-software-tools). ##### A worked example Consider a mid-sized bank with 10,000 corporate customers and a supervisor signalling interest in data quality ahead of the AMLR. A manual programme would staff a team, start with the most visible files, and discover the real scope over the first quarter — by which point the deadline pressure is acute. An automated programme runs gap analysis across all 10,000 entities in the first days, surfacing that 800 high-exposure files carry material gaps. Those are ranked and worked first; outreach for missing documents goes out automatically; arriving documents are processed without re-keying; and every action is logged. The bulk of the regulatory exposure is retired in weeks, the long tail follows, and remediated files move straight into monitoring so the book stays current. The bottleneck is analyst decisions — not data gathering. Days, not months Portfolio-wide gap analysis when automated 10,000 ≈ 100 Parallel processing — same elapsed time at any scale 10 Jul 2027 AMLR applies; supervisors score data quality ##### KYC remediation and the AMLA deadline The window to fix a stale back book before AMLA supervision begins is open now, and it’s closing. Firms that will be ready are the ones treating remediation as an infrastructure problem the platform handles continuously — not a project that gets staffed, run, and then repeated two years later. [Read the full guide to clearing your backlog before AMLA →](https://strise.ai/blog/how-to-clear-your-kyc-remediation-backlog-before-amla-supervision-begins) See what Strise processes in the first 48 hours of a remediation programme. [Book a meeting with Strise →](https://strise.ai/book-a-meeting-with-strise) ##### Find answers to common questions about KYC remediation What is KYC remediation? The process of reviewing and correcting existing customer files so they meet current data and regulatory standards — fixing what’s missing or out of date across a backlog of already-onboarded customers. What’s the difference between KYC remediation and periodic review? Periodic review is a scheduled re-check of a customer. Remediation is a corrective sweep that clears a backlog of files that have fallen behind standard, usually at volume and under time pressure. What is the difference between remediation and perpetual KYC? Remediation fixes a backlog that has already accumulated. Perpetual KYC prevents the backlog by monitoring continuously and updating files when something changes, rather than on a fixed schedule. How long does KYC remediation take? Manually, portfolio-scale programmes run for quarters. With automated gap analysis and parallel processing, portfolio-wide analysis completes in days and the backlog clears in weeks rather than months. What triggers a KYC remediation project? Tightened onboarding standards, regulatory change (such as the AMLR), a periodic-review backlog, a corporate or ownership change, or supervisory findings about data quality. How do you prioritise a remediation backlog? Rank cases by regulatory exposure and gap severity so high-risk files with material gaps are worked first, ahead of low-risk, largely compliant files. How does KYC remediation relate to AMLA? Under the AMLR, supervisors assess firms on customer-data quality from 2027. Remediating a stale back book before supervision begins reduces the risk of adverse findings. Can KYC remediation be automated? Yes. Gap analysis, risk-based ranking, client outreach, document processing, and audit-trail generation can all be automated, leaving analysts to make decisions on pre-prepared cases. ##### Success stories - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## Running a formal evaluation? Send us your RFP. We’ll come back with real numbers for your setup. [Book a demo](https://strise.ai/book-a-meeting-with-strise) --- ## About us ### Strise partners: Deliver autonomous FinCrime operations to your clients — Strise URL: https://strise.ai/partner Partner with Strise to deliver autonomous FinCrime operations to your clients. Built for tech providers, consultancies, and financial institutions. - [Home](https://strise.ai)/ - Partner with Strise #### Help your clients end manual FinCrime work. Whether you’re a technology provider, consultancy, or financial institution expanding your compliance stack, Strise gives you the infrastructure to bring this to your clients: manual FinCrime work ends, and judgement stays with their analysts. Schedule a partner call Moose illustration - British Land - KLP - Nordea - Storebrand - SpareBank 1 - Fremtind - Formue - Vipps MobilePay - ## Why partner with us? 01 Technology partners Build on top of or alongside Strise’s open API. Connect your product to the compliance infrastructure — onboarding, monitoring, ownership mapping, alert triage. Integration typically takes 7–10 days with a dedicated tech team. 02 Reseller partners Bundle Strise into your compliance offering and own the end-customer relationship. We provide the platform, the integrations, and the technical support. You bring the market access. 03 Referral partners You know the compliance teams. We build what they need. Refer a customer, and we handle everything from there, demo, implementation, support, contract. ##### The opportunity. 7–10 days Typical integration time for technology partners using our open API. 70% of top Nordic banks are already on the platform your clients are asking for. Recurring revenue Resellers and referral partners earn from every customer deployed on Strise. ##### What customers say. - Smiling woman with glasses and blonde hair in a bun, wearing hoop earrings and a black top indoors. “With Strise, AI can take on much of the time-consuming routine work.” Anne Reisch Bruset Head of KYC at BDO - Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window. Play video “Strise has revolutionised our approach to KYC.” Gavin Bergin Director of Governance at British Land - Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background. Play video “With Strise we use manual labor where it’s most worthwhile, and decrease costs.” Silke Oeverby Chief Risk & Compliance Officer at Vipps MobilePay - Man with light brown hair wearing a white shirt, smiling in front of a red curtain background. Play video “With Strise, we have reduced false positives by 30%.” Endre Jo Reite Director of personal markets - Smiling woman with long blonde hair wearing a black top against a wooden background. Play video “Strise has helped us move away from periodic review. That’s fundamental.” Rebecca Robinson Chief Risk and Compliance Officer at Tenora - Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background. Play video “With Strise, we get better accuracy and quality in our customer risk assessments.” Niri Kvammen Forberg AML Specialist at SpareBank 1 SMN - Smiling woman with long brown hair looking slightly upward against a dark background. Play video “Strise saves us a considerable amount of time per onboarding” Ragnhild Georgsen Head of AML & Sanctions at Sparebanken Norge - Man in white shirt wearing a lanyard with ‘Plenitude’ logo, speaking indoors with blurred windows in the background. Play video “Strise helps our clients realise the benefits of AI quickly.” Alan Paterson Founder and Chief Innovation Officer at Plenitude - ## Talk to our partnerships team. Tell us about your company and how you’d like to partner with Strise. Our partnerships team will follow up to explore referral, reseller, or technology collaboration. Schedule a partner call ##### Frequently asked questions. What partnership models does Strise offer? Three: referral, reseller, and technology. Referral partners introduce customers and earn a percentage of the year-one licence fee, Strise handles the contract and relationship. Reseller partners own the end-customer relationship and bundle Strise into their own offering. Technology partners integrate directly with the API to build on or alongside the platform. How long does API integration take? Typically 7–10 days with a dedicated development team. Some partners have done it in as little as 8 days. The API is always open, we don’t gate access or limit what you can build on top of it. Can we embed Strise in our own platform? Yes, the API lets you keep users inside your product while running the full Strise compliance workflow in the background. What data does Strise provide? AML, UBO, and entity data, global and local coverage. This includes ownership structures, PEP and sanctions data, adverse media, and risk signals. Partners can use this insights to enrich their own products or build new compliance workflows on top of it. Who owns the end-customer contract? In a referral arrangement, Strise owns the contract and manages the customer relationship. In a reseller arrangement, the partner owns it. We’re clear about this from the start so there’s no ambiguity down the line. What support do you provide to partners? A dedicated technical lead who can work directly with your development team, a partnership manager for commercial questions, and access to our full API documentation. Most partners are operational within a week. Can we co-market with Strise? Yes. We’re building a partner ecosystem, not just a reseller network. That means joint content, co-branded materials, and where relevant, access to The Laundry, our podcast with over 10,000 monthly listeners in the compliance community.