A neuro-symbolic model reduces fraud explanation latency from 30ms to 0.9ms while maintaining identical fraud detection performance. This breakthrough addresses a critical real-time limitation in explainable AI systems. The model generates deterministic, human-readable explanations as a by-product of its forward pass, eliminating the need for separate post-hoc computation.

On the Kaggle Credit Card Fraud dataset, the neuro-symbolic approach achieves a fraud recall rate of 0.8469 with only a minimal AUC drop, demonstrating practical viability. The 33x speedup represents a transformative improvement for fraud detection systems requiring instant explainability.