Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection

Traditional explainability methods like SHAP are often too slow and inconsistent for real-time fraud detection, creating a bottleneck when instant, reliable explanations are required. A novel neuro-symbolic model solves this by embedding a layer of human-readable, differentiable rules directly into its architecture. This design generates explanations as a natural byproduct of the model's forward pass, producing both a prediction and its reasoning in a single, instantaneous step. The new model achieves a 33x speedup in generating explanations compared to a standard model using SHAP, all while maintaining identical fraud recall and delivering deterministic results.