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Graph Neural Networks Compared to Chained-Join Features for Payroll Fraud Detection

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv details a comparative study of methods for detecting payroll fraud within synthetic HR/ERP graphs. The study evaluated traditional analyst rules, explicit path features, and graph neural networks (GNNs), finding that a tuned gradient-boosted model utilizing chained-join features achieved the highest overall link average precision. This approach outperformed a GraIL-style GNN, demonstrating that significant multi-hop gains are not exclusive to GNNs. The research also explored the impact of failed identifier joins and the effectiveness of matched twins in reducing false positives. AI

IMPACT This research offers insights into optimizing fraud detection methods, potentially improving accuracy and efficiency in financial and enterprise systems.

RANK_REASON The cluster contains a research paper detailing a comparative study of machine learning models for fraud detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Graph Neural Networks Compared to Chained-Join Features for Payroll Fraud Detection

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The cluster contains a research paper detailing a comparative study of machine learning models for fraud detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiaobo Chen, Genliang Zhu, Ziyuan Wang, Chu Wang ·

    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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