AI-Powered Fraud Detection Agent

The problem

Fraud review in retail banking is a queue problem before it is a modelling problem. Rules-based systems flag far more transactions than analysts can work through, so genuinely suspicious activity waits behind false positives while legitimate customers get blocked mid-purchase. Tuning the rules tighter cuts the noise but lets real fraud through.

How it works

The agent scores transactions continuously rather than in nightly batches. Behavioural models built in TensorFlow learn what normal looks like per customer instead of applying one global threshold, so an unusual amount for one account is unremarkable for another. Snowflake holds the transaction history and alternate data the models draw on, which keeps feature lookups fast enough to score in the payment window rather than after settlement.

Scores feed into NICE Actimize rather than replacing it. That matters: the bank's existing case management, audit trail and regulatory reporting stay the system of record, and the model becomes another signal feeding decisions the compliance team already knows how to defend.

What shaped it

Anything touching payments inherits the bank's regulatory surface. A score that cannot be explained to an auditor is not usable no matter how accurate it is, so the design favoured signals a reviewer could reason about over marginal gains from opacity. The system also had to degrade safely — a scoring outage cannot become a payments outage.

Outcome

Manual fraud review volume dropped substantially while regulatory compliance improved, with analysts spending their time on the cases most likely to be genuine.