Fraud detection has long relied on the assumption that transactions reflect human behavior patterns—typing rhythms, device-switching habits, and contextual cues like urgency or anxiety. However, the rise of AI agents conducting transactions on behalf of users has disrupted this foundation. Experian's 2026 Future of Fraud Forecast identifies 'machine-to-machine mayhem' as a growing threat, where legitimate shopping agents and fraudulent bots appear nearly identical in transaction logs.

Traditional models, including Random Forest classifiers trained on datasets like Pay Sim, focus on transaction features such as amount, timing, and velocity, treating all users the same regardless of identity. While SHAP values helped explain model decisions by highlighting feature contributions, they cannot capture the absence of human context—such as a user's job, history, or intent—when the actor is an autonomous agent. This exposes a critical gap: explainability tools like SHAP clarify *what* the model used, but not *who* or *why* behind the action.

As agentic AI blurs the line between human and machine behavior, fraud detection must evolve beyond feature-based explanations to incorporate contextual and behavioral semantics that current XAI methods cannot provide.