Real risk only. No noise

Real risk reaches your analyst. Everything else is cleared before it arrives.

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 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.
"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.
"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.
"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.
"Strise has helped us move away from periodic review. That's fundamental."
Rebecca Robinson
Chief Risk and Compliance Officer at Tenora
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"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.
"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.
"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.

How screening runs itself

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.
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.
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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.
sales@strise.ai