What SHAP Can't Explain About Agentic AI Fraud

Traditional fraud detection relies on explainability methods like SHAP to interpret human behavioral anomalies within transactions. The emergence of autonomous AI agents, which lack human-like behavioral patterns, fundamentally challenges this paradigm. These agents operate with a speed and consistency that breaks the assumptions underlying current fraud models, making feature-level explanations less effective. The critical question is shifting from why a single transaction appears risky to why an agent took a specific action, requiring an audit of the agent's entire decision-making process.