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Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information

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  • Shi, Feifen
  • Zhao, Chuanjun

Abstract

In the highly competitive business environment, financial fraud detection plays a crucial role in safeguarding companies and investors. However, traditional methods face challenges when assessing the complex and multidimensional nature of financial data. Cutting-edge machine learning techniques, particularly the hierarchical graph attention network (HGAT), emerge as a promising approach for financial fraud detection. The proposed approach includes encoding adjacency matrices for capturing local relationships and utilizing multi-head self-attention to propagate structural attributes across multiple layers. Node embeddings are generated by the HGAT model, which integrates both local and extensive structural information through multihead self-attention. Through learning intricate inter-entity relationships, the HGAT model can effectively identify potential financial risks. Experiments performed on a publicly available financial report dataset demonstrate the superior performance of our model compared to the existing methods in detecting financial risks.

Suggested Citation

  • Shi, Feifen & Zhao, Chuanjun, 2023. "Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information," Finance Research Letters, Elsevier, vol. 58(PB).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pb:s1544612323008309
    DOI: 10.1016/j.frl.2023.104458
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    References listed on IDEAS

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    Cited by:

    1. Dawei Cheng & Yao Zou & Sheng Xiang & Changjun Jiang, 2024. "Graph Neural Networks for Financial Fraud Detection: A Review," Papers 2411.05815, arXiv.org, revised Nov 2024.

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