Why SHAP Falls Short in Explaining Agentic AI Fraud Detection Systems

Traditional explainability tools like SHAP (Shapley Additive exPlanations) are widely used in fraud detection to identify why a transaction appears risky, but they were designed for static models rather than autonomous AI agents. As agentic AI systems gain traction in financial ecosystems, they introduce complex behaviors such as multi-step tool calls, real-time policy updates, and cross-agent decision chains that SHAP cannot trace. This explainability gap poses regulatory and operational challenges, since analysts cannot fully audit how a fraud alert was generated. Researchers and practitioners are now exploring complementary approaches, including action-level audit logs, Explain-Then-Act patterns, and human-in-the-loop summaries, to address what SHAP leaves invisible. Closing this gap is considered essential for organizations seeking to maintain transparency and compliance as agentic AI becomes more prevalent in fraud detection workflows.