Payroll Fraud Detection in Synthetic HR/ERP Graphs: An Inductive Comparison of Rules, Path Features, and Graph Neural Networks

arXiv cs.AINovember 2, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2610.41085v1 Announce Type: new Abstract: Payroll-fraud screening requires finding hidden employee--payee links while distinguishing suspicious relationships from legitimate ones. We formulate this problem as inductive link prediction and entity ranking on synthetic human-resources and enterprise resource planning (HR/ERP) graphs, with unseen organizations and a matched benign twin for every planted case. An original study on 24 held-out organizations and a prospectively frozen extension compare analyst rules, explicit path features and graph neural networks (GNNs). A tuned gradient-boosted model on chained-join features achieved the highest overall link average precision (AP; 0.823),

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