FinTech
Real-Time Fraud Detection for a Digital Bank
Key Result$8M saved annually
Engineered an ML pipeline processing 50K transactions/second with 95% fraud detection accuracy, reducing false positives by 60% and saving $8M annually.
The Problem
What Was Broken
A digital neobank was losing $12M/year to fraud while their rule-based system generated 40% false positives — blocking legitimate customers and damaging trust.
Our Solution
How We Solved It
We built a real-time ML fraud detection pipeline with ensemble models, behavioral biometrics, and adaptive thresholds that learn from each decision.
Technology Stack
Technology Used
XGBoost + Neural NetworksApache KafkaKubernetesFeature Store (Feast)Grafana monitoringPython + Go
Results
Measurable Outcomes
95% fraud detection accuracy
False positives reduced by 60%
$8M annual savings
50K transactions/second throughput
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