Real-time Fraud Detection for Digital Payments
A digital payments company was experiencing $900K monthly fraud losses with a rule-based system generating 40% false positives.
Team
6 ML engineers + 2 security specialists
Timeline
14 weeks end-to-end
Client
Digital Payments Company (Southeast Asia)
Outcomes Delivered
89%
Fraud Loss Reduction
< 80ms
Transaction Scoring Latency
12%
False Positive Rate (from 40%)
How we delivered it
Analysed 18 months of transaction data to identify fraud patterns across 40+ feature dimensions including device fingerprinting, velocity, and network graph signals.
Built a real-time scoring engine using a gradient boosting model served via FastAPI with Redis caching for sub-80ms latency at peak load.
Implemented a feedback loop where confirmed fraud cases are automatically added to the training dataset for weekly model retraining.
Designed a case management interface for the fraud operations team to review flagged transactions and submit ground truth labels.
Ran a 4-week parallel operation alongside the legacy rule engine before full cutover, validating the 89% fraud loss reduction.
What we built
Replaced rule-based system with a real-time ML fraud scoring engine processing transactions in under 80ms.
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