AML transaction-monitoring analytics for a bank

The client is a UK bank whose AML transaction-monitoring relied on crude rules that flooded investigators with false positives. Effective monitoring is both a regulatory obligation and a genuine defence against financial crime.

Investigators spent most of their time clearing noise, which is expensive and risks real cases hiding in the flood.

Crude transaction-monitoring rules drowned analysts in false positives while still missing subtle laundering patterns. The system was simultaneously too noisy and not sharp enough.

Investigators spent their days clearing noise, which is both expensive and a genuine financial-crime risk when real cases hide in the flood. Wasted effort on false positives is also risk on true ones.

The bank needed sharper detection and less noise at the same time, without weakening its regulatory defensibility. Every improvement had to remain provable to the regulator.

We rebuilt monitoring on governed data with risk-scored alerts, so investigators see ranked risk rather than an undifferentiated queue. Ranking by risk focuses scarce investigator time where it matters.

Network features surfaced relationship patterns that flat rules miss entirely, catching the subtler cases the old system let through. Looking at relationships, not just transactions, is what improves detection.

We tuned the models against confirmed outcomes, so the system learned from real case dispositions, and every alert carries its evidence, keeping the whole approach defensible to the regulator. Sharper detection and defensibility came together.