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

图神经网络与链式连接特征在工资欺诈检测中的比较

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

一篇新发表在arXiv上的研究论文详细介绍了在合成HR/ERP图表中检测工资欺诈方法的比较研究。该研究评估了传统的分析师规则、显式路径特征和图神经网络（GNNs），发现利用链式连接特征的梯度提升模型在整体链接平均精度方面表现最佳。这种方法优于GraIL风格的GNN，表明显著的多跳增益并非GNN独有。研究还探讨了标识符连接失败的影响以及匹配双胞胎在减少误报方面的有效性。 AI

影响这项研究为优化欺诈检测方法提供了见解，有望提高金融和企业系统的准确性和效率。

排序理由该集群包含一篇研究论文，详细介绍了用于欺诈检测的机器学习模型的比较研究。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Xiaobo Chen, Genliang Zhu, Ziyuan Wang, Chu Wang · 2026-11-02 05:00

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

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-r…

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

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