Real-Time Fraud Detection for a Regional Bank
EvoTech replaced a noisy rules-based fraud process with a real-time machine-learning detection layer that reduced false positives and cut losses.
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Challenge
The bank’s existing fraud detection model produced a high false-positive rate, frustrated legitimate customers, and consumed fraud operations capacity. Card-not-present fraud was increasing and the team needed a more adaptive model without losing auditability.
Approach
EvoTech designed an ensemble fraud-detection stack using boosting models, graph-based relationship analysis, and streaming feature engineering tied to transaction events. The implementation also included explainability support and API-based integration into the bank’s core environment.
Results
- False positives reduced by 72 percent.
- Fraud losses cut by 58 percent in the first 12 months.
- Operations team capacity improved by 40 percent.
- The platform created a stronger foundation for model governance and tuning.
Solution structure
Advanced fraud detection system
ML Scoring Layer
Replaced legacy rule engine with real-time ML scoring layer using transaction patterns and customer behavior.
Adaptive Thresholds
Built adaptive threshold system that learns from analyst decisions and reduces false positives over time.
System Integration
Integrated with existing banking systems and provided analyst dashboard for case review and override.
Outcome metrics
Measurable business impact
Building a real-time fraud detection system?
EvoTech can design and implement ML-powered fraud detection that reduces false positives and protects your customers.