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Payroll Fraud Detection in Synthetic HR/ERP Graphs: An Inductive Comparison of Rules, Path Features, and Graph Neural Networks — arXiv:2610.41085

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41085
License (as archived): CC0-1.0
Archived: 2026-11-03T05:22:24.359Z
SHA-256 of the archived text: 7759e52dc108833c5092e841ac81f539081039d3f891a939f556e4f283214f5c
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How to cite

Payroll Fraud Detection in Synthetic HR/ERP Graphs: An Inductive Comparison of Rules, Path Features, and Graph Neural Networks — arXiv:2610.41085 — arXiv (en), https://arxiv.org/abs/2610.41085 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c1ecd951fa02 (Metraipolis archive dc_c1ecd951fa02, sha256 7759e52dc108833c5092e841ac81f539081039d3f891a939f556e4f283214f5c)

BibTeX

@misc{dcc1ecd951fa02,
  title = {Payroll Fraud Detection in Synthetic HR/ERP Graphs: An Inductive Comparison of Rules, Path Features, and Graph Neural Networks — arXiv:2610.41085},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41085},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c1ecd951fa02; sha256 7759e52dc108833c5092e841ac81f539081039d3f891a939f556e4f283214f5c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41085. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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), above the GraIL-style GNN (0.663); its 4-hop AP was 0.646 versus 0.272. Thus, the observed multi-hop gain is not specific to GNNs. On a new synthetic cohort with 2.0% positive entities, chained-join boosting again led (AP 0.798); the relational entity GNN also exceeded record-aware rules (0.707 versus 0.504). At a 2% review budget, their precision was 0.695 versus 0.602. Under 40% failed identifier joins, the entity GNN ranked above chained-join boosting (AP 0.712 versus 0.683, descriptively). Structure-only auditing exposed a shortcut in the initial generator; matched twins reduced the audited twin-pool AUROC to 0.493--0.514. These results separate multi-hop candidate discovery, record-based discrimination and review prioritization within an explicit synthetic evaluation design. Code and data are available from the authors on request.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41085 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T05:18:52.087Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).