Client Context
A mid-tier UK financial services firm processing over 2 million transactions daily was struggling with its legacy rule-based fraud detection system. The system generated thousands of false positive alerts each day, overwhelming the compliance team and causing legitimate transactions to be delayed.
The Challenge
The existing system’s rigid rules couldn’t adapt to evolving fraud patterns. Analysts spent 80% of their time reviewing false positives, while genuine fraud occasionally slipped through during peak alert volumes. The FCA had flagged concerns about alert fatigue risk in their last supervisory visit.
Our Solution
We designed a multi-layered ML approach that replaced the binary rule-based system with probabilistic risk scoring. The architecture uses real-time feature engineering, ensemble models for initial scoring, and a deep learning model for complex pattern detection. A human-in-the-loop workflow ensures analysts focus only on genuinely ambiguous cases.
Results
The platform went live after 12 weeks of development and 4 weeks of parallel running. Within the first month of full production, false positives dropped by 73% and fraud detection accuracy reached 94.7%. The client reallocated 6 analysts to higher-value investigation work.