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In a controlled experiment with injected multi-account fraud rings, engineered structural features recover all injected test transactions, while the tabular baseline misses roughly a quarter of th","title":"Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation","url":"https://arxiv.org/abs/2607.19266","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.19266v1 Announce Type: cross \nAbstract: 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 inflate baseline performance. 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