Thought SHAP made your fraud model explainable enough? Turns out there's a much bigger hole underneath. Title: What SHAP Can't Explain About Agentic AI Fraud URL: ❓ How does fraud detection spot "human-ness" in the first place? 💡 Typing dynamics, device switching, timing quirks: signals of human stress or deviation from a personal baseline. That assumption has underpinned fraud detection for a decade. ❓ What does SHAP actually give you? 💡 Ranked feature attribution over amount, timing, device, and velocity, explaining why a transaction looked suspicious in a way compliance analysts can audit and defend. ❓ What changes when the actor is an autonomous agent? 💡 No circadian rhythm, no panic-driven mistakes, superhuman consistency. The whole premise that "deviation from a human baseline signals fraud" simply stops applying. ❓ So what's the fix? 💡 The author argues for shifting from transaction-level scoring to "trajectory auditing", tracking an agent's decision path and tool calls, though no implemented solution exists yet. #FraudDetection# #ExplainableAI#