Fraud detection in financial transactions

The problem

Transaction monitoring at scale creates two failures at once: too many false positives for a review team to work through, and a decision process nobody can explain when a regulator asks why a particular payment was held. Buying a model solves the first and worsens the second.

What we built

A monitoring layer that scores transactions in real time, with the reasoning behind each score retained and reviewable. Analysts see why a case surfaced, not merely that it did, and every judgement is reconstructable from the evidence that produced it.

• Feature pipeline over existing payment infrastructure, inside the client tenancy
• Scoring with retained explanations, written to an immutable audit store
• Review queue prioritised by risk and reviewer capacity
• Drift monitoring, with retraining gated on evaluation rather than schedule

Governance was the constraint, not accuracy. A model nobody can explain cannot go near a regulated payment flow, however good its numbers look.

Outcome

PLACEHOLDER — NQ TO SUPPLY VERIFIED OUTCOME DATA

Measured results, review-time reduction and false-positive movement will be published here once the client has approved the figures. Nothing is claimed until then.