Custom ML fraud detection model processing 2 million transactions daily in real-time with sub-50ms latency, reducing fraud losses by 72% within 3 months of deployment.
A payment processor handling PKR 50 billion in annual transactions was losing 1.8% of revenue to fraud despite a rule-based fraud engine with 600+ rules. Fraudsters had learned to game the rules. Legitimate transactions were also being blocked at a 4% false positive rate — creating customer complaints.
We trained an ensemble ML model combining gradient boosting for feature-based detection and an LSTM network for sequential pattern analysis. The model was deployed as a real-time API on Sibyl Compute with < 50ms inference latency — fast enough to be embedded in the payment authorisation flow.
In 3 months we recovered more in prevented fraud than the entire project cost. The model keeps getting better as it sees more data. It's genuinely remarkable.
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