Risk assessment in real time
a top-20 US bank
fewer false positives
risk scoring on every transaction
drop in fraud caught
Fraud and risk rules fired on too many legitimate transactions, freezing good customers and flooding analysts with false alerts. Real threats hid in the noise. Tuning thresholds by hand could not keep pace with evolving fraud. The bank needed sharper detection that cut false positives without raising actual risk exposure.
How we approached it
Analyzed alert history to quantify how false positives drained analyst capacity.
Built models that score risk in real time using richer behavioral and transaction signals.
Layered models with existing rules so analysts see clear reasons behind each score.
Validated against known outcomes and monitored drift before raising the automation level.
“We cut the noise without missing real fraud. Analysts chase genuine threats now, and good customers stop getting frozen out.”
No. The models were validated against known outcomes to ensure detection of real fraud held steady while the volume of false alerts dropped substantially.
Yes. Risk scores layer on top of existing rules with clear contributing signals, so analysts see the reasoning behind each alert rather than an opaque number.
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