Financial Services
Real-Time Transaction Fraud Detection
KafkaBigQuerydbtPython
The Challenge
A digital payments platform processing 500K daily transactions relied on rule-based fraud checks that ran in nightly batches. Fraudulent transactions were caught an average of 14 hours after they occurred, resulting in $2.3M in monthly chargebacks.
The Approach
Replaced the batch system with a streaming analytics pipeline that scored every transaction in under 200ms. Feature engineering combined transaction velocity, geolocation anomalies, device fingerprinting, and merchant category patterns to train a gradient-boosted model refreshed hourly.
The Impact
Fraud detection latency cut from 14 hours to 200ms
63% reduction in monthly chargebacks
False positive rate held under 0.4%
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