Real-time Fraud Detection Model
Case Study

The challenge

A digital payments platform processing 2M+ transactions per month was using rule-based fraud detection that was generating too many false positives (blocking legitimate transactions) while missing 8-12% of actual fraud. The static rules couldn't keep up with evolving fraud patterns.

What we built

• Custom ML transaction monitoring trained on 18 months of historical fraud and legitimate transaction data
• Sub-millisecond decision latency — fraud scoring runs in <1ms, invisible to transaction flow
• Adaptive model weights updated weekly using confirmed fraud labels from the investigations team
• Explainable AI outputs providing human-readable fraud signal summaries for the review team
• Velocity checks, device fingerprinting, and behavioural biometrics integrated into the feature pipeline

The results

The solution delivered measurable results within weeks of go-live. The client achieved 99.2% Detection accuracy and <1ms Decision latency, with full ROI achieved within 5 months Full ROI.