Real-Time Fraud Detection Engine

Challenge

The credit union was losing an average of $320,000 annually to fraudulent transactions. Their rule-based detection system flagged only 34% of confirmed fraud cases and generated excessive false positives that frustrated legitimate customers.

Solution

We developed a machine-learning fraud detection engine that analyzes transaction patterns in real time. The system scores every transaction within 50ms, flags anomalies for human review, and continuously learns from analyst decisions to improve accuracy.

Results

Technology Stack