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

A recent development in neuro-symbolic AI for fraud detection explores the use of symbolic rules embedded within neural networks to monitor concept drift at inference time without relying on labeled data. Specifically, the model encodes fraud detection rules, such as a V14 threshold indicating fraud, and investigates whether deviations in these rules can serve as early warning signalsacting as a "canary"to detect shifts in fraud patterns before a decline in model performance (e.g., F1 score) occurs. This approach leverages hybrid architectures that combine domain knowledge with neural learning, enabling real-time, label-free monitoring of model