Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops (Label-Free)

Fraud detection models can suddenly fail when criminal patterns change, a problem known as concept drift that often goes unnoticed until performance plummets. A novel neuro-symbolic system provides an early warning by monitoring the IF-THEN rules the model learns internally, rather than waiting for prediction accuracy to drop. The system's success hinges on a new metric, the FIDI Z-Score, which detects when a feature's importance to these rules changes by an anomalous amount compared to its recent history. In experiments, this label-free method successfully flagged concept drift in every trial, sometimes providing an alert even before traditional performance metrics showed any sign of a problem.