Loss and fraud analytics for a fintech

The client is a UK fintech whose fraud and loss reporting lagged two days behind reality. In fraud, the speed of detection is directly proportional to the losses avoided.

By the time trends surfaced in a report, the associated losses had already mounted: the reporting was always describing yesterday's problem.

Fraud and loss trends were two days stale by the time the report landed, so emerging patterns were caught late, after the losses had already mounted. The lag turned reporting into a post-mortem rather than a defence.

In fraud, that lag is expensive; a pattern spotted same-day can be shut down before it scales. Every hour of delay compounds the cost.

The fintech needed a live view it could actually act on. Timeliness was the whole point.

We modelled loss and fraud signals on a governed warehouse feeding live dashboards, refreshed daily so trends are current, not historical. Making the view live is what turns reporting into an early-warning system.

The analytics reconcile to the ledger, so the numbers are trusted for real decisions rather than treated as indicative. Reconciliation is what lets the fraud team act on the data with confidence.

Emerging patterns now surface the same day they appear, giving the fraud team a chance to intervene early, and we built it on governed, reusable models so the view extends as new signals emerge. Detection stays ahead as fraud evolves.