← Back to all news
arXiv AI By 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

Read the original on arXiv AI →

The study introduces an inductive link‑prediction framework for detecting payroll fraud on synthetic HR/ERP graphs, comparing analyst rules, path‑feature models, and graph neural networks (GNNs). A gradient‑boosted model using chained‑join features achieved the highest link average precision (AP 0.823), outperforming a GraIL‑style GNN (AP 0.663) and demonstrating that multi‑hop gains are not exclusive to GNNs. In additional experiments, the chained‑join model again led on a new cohort (AP 0.798), while the relational GNN surpassed record‑aware rules (AP 0.707 vs. 0.504) under various conditions, highlighting the distinct roles of multi‑hop discovery, record‑based discrimination, and review prioritization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Oct 27

LLM-Enhanced Heterogeneous Graph Learning with Business Semantics for Financial Fraud Detection

SemHGT is a new framework that enhances heterogeneous graph learning for financial fraud detection by explicitly incorporating business semantics of structured attributes. It uses role‑aware prompts to generate risk‑related descriptions, maps these to semantic vectors, and then guides multi‑round sampling of transaction paths based on semantic similarity. The framework combines local path evidence and global neighbor summaries in a unified transformer, achieving superior performance on three benchmark datasets compared to existing baselines.

By Yixuan Chen, Hongyu Zhan, Jie Sheng, Weiyu Han, Shuai Chen, Tianyi Zhang, Xiao Tan, Jun Xia
llmsbenchmarks
More like this →
arXiv Machine Learning
Oct 9

Evi-VN: Hard Region Guided Virtual Node Evidence Injection for GNN-Based Fraud Detection

Evi-VN is a new framework for graph neural network (GNN) fraud detection that injects evidence from diverse modalities—structured records, text, images, and audio—into virtual node representations. It targets shared hard regions where standard GNNs fail to distinguish well‑disguised fraudsters from legitimate users, correcting blind spots without disrupting reliable predictions. Experiments on bot, fake‑review, refund‑evidence, and telecom‑fraud datasets demonstrate its effectiveness across generic, fraud‑specific, and unseen GNNs.

By Jiran Tao, Yifan Wu, Binyan Jiang
multimodal
More like this →