Case study · Banking
Real-Time Fraud Detection
ML-powered fraud scoring with streaming event processing and feedback loops.
Key results
- Fraud loss -44%
- False-positive rate -38%
- Customer friction reduction measurable
Context
A regional bank's fraud-detection stack ran on rules-based logic that caught obvious fraud patterns but missed sophisticated attacks and produced high false-positive rates that frustrated customers. The analyst team spent most of its time on false-positive review.
Challenge
Replacing the rules with ML required real-time scoring at the transaction-authorization timescale (under 100ms), proper feedback loops so analyst decisions refined the model, and integration with the bank's existing fraud-operations workflow.
Approach
Thoughtwave delivered an 8-month ML fraud-detection platform: streaming event processing for real-time transaction scoring, supervised models trained on the bank's historical fraud outcomes, analyst-feedback integration for continuous model improvement, and integration with the existing case-management workflow. The engagement ran from discovery through two quarterly model-retraining cycles.
Outcomes
Fraud loss dropped 44% year-over-year after full deployment; false-positive rate dropped 38% which directly improved customer experience and reduced analyst workload; customer friction dropped measurably across the net-promoter metrics the bank tracks.
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