Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation

Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable. We study a layered pipeline on the PaySim dataset that combines a gradient-boosted classifier, graph-derived structural features, an autoencoder-based anomaly signal, TreeSHAP explanations, and a bounded LLM investigation agent applied to cases the classifier scores uncertainly. Before any model comparison, we identify and remove a simulator-specific balance shortcut that would otherwise

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ethics.ai (21 July 2026), “Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation,” evidence record 12592, https://ethics.ai/record/12592 (originally published by arXiv cs.AI).

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