Global Bank Prevents $47M in Fraud with Real-Time AI Detection
Key Results
The Challenge
The bank's legacy fraud detection system had a 23% false positive rate, causing customer friction and costing $8M annually in manual reviews. Meanwhile, sophisticated fraud attacks were bypassing rule-based systems, resulting in $12M annual losses.
The Solution
Stratafy's assessment scored the bank at 58% AI readiness, identifying weaknesses in real-time data processing and ML operations. We designed a transformation roadmap: (1) Migrated to cloud-based streaming architecture, (2) Implemented ensemble ML models for fraud detection, (3) Built MLOps pipeline for continuous model improvement, (4) Established AI risk management framework compliant with Basel III.
Technologies & Approaches
The Results
Total fraud prevented in first year of operation
Reduced from 23% to 4.4% false positive rate
Real-time fraud detection in under 200 milliseconds
Improved NPS score due to reduced friction
The AI readiness assessment was eye-opening. We thought we were ready for AI, but Stratafy showed us critical gaps in our infrastructure and governance. Their roadmap gave us confidence to invest $15M in AI transformation, which has already paid for itself 3x over.