Financial services

Fraud and AML triage.
Analyst capacity back.

Nearly every financial institution already detects more than it can investigate. The constraint is not the model that raises alerts — it is the analyst capacity behind it, and that is where an AI system actually earns its place.

All Financial Services →

The real bottleneck

Transaction monitoring systems are typically tuned conservatively, because the regulatory consequence of missing a suspicious activity report is severe and the consequence of raising a false one is merely expensive. It is widely reported across the industry that the overwhelming majority of alerts close as no further action, and each one has consumed analyst time to get there.

So the valuable intervention is rarely a better detection model. It is compressing the investigation. An analyst opening an alert spends most of their time assembling context that already exists somewhere — prior alerts on the same party, KYC file, transaction history, adverse media, related accounts — before making a judgement that is often quick once the picture is complete.

That assembly is what we automate. The agent gathers the evidence, structures it against your institution's own typologies, and presents a case file with every element linked to its source. The decision stays with the analyst, and the time to reach it falls substantially.

What we build

ALERT TRIAGE PATH

01

Alert

from your monitoring system
02

Assemble

KYC · history · prior alerts
03

Screen

sanctions · adverse media
04

Structure

against your typologies
05

Analyst decision

human, always
06 · Branch / escalation

SAR drafting support

analyst-reviewed

Solid: evidence assemblyDashed: drafting support, never submission

The system assembles and structures. It does not close alerts, and it does not file reports.

What decides whether it works

The model never closes an alert.

Auto-closure is where these projects go wrong with a regulator, and it is not a capability question — it is an accountability one. A named human disposes of every alert. The system changes how long that takes and how good the information in front of them is.
Every element is traceable.

A case file assembled by an AI system has to show, per element, which record it came from. An examiner will ask, and “the model determined” is not an answer that survives the follow-up question.
Model risk management applies.

If your institution operates under a model risk framework, this system is in scope: documented, validated, monitored and periodically reviewed. Building it as though that will be added later is how a pilot fails to reach production.
Measure investigation time, not detection.

The success metric is time to disposition and consistency of outcome across analysts, not the alert volume — which the AI system does not change and should not be credited for.

Questions

Questions about Fraud & AML Triage.

Will this reduce our false-positive rate?

Not directly, and we would rather be clear about that. False positives come from your monitoring rules, which we do not replace. What changes is the cost of processing them. If reducing alert volume is the goal, that is a rules-tuning engagement with a model risk process attached, and a different piece of work.

Can it draft the suspicious activity report?

It can assemble a draft from the evidence, which analysts generally find useful. The analyst writes, reviews and files — the narrative in a report is a professional judgement with a name attached and should stay that way.

Which rules apply to this?

Fraud detection is carved out of the EU AI Act’s high-risk credit category, but the EU anti-money-laundering package and GDPR still apply in full — including the Article 6 and Article 10 basis and the retention position for adverse-media screening of individuals, which is the part most often left undefined. Scope is for your compliance function to set; we build to it. This is not legal advice.

Where does the data live?

Wherever your risk position requires. Financial crime data is among the strongest reasons to choose on-premise or private data centre hosting, and an open-weight model self-hosted handles this workload well. The system does not need a frontier API.