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

Feifen Shi and Chuanjun Zhao

Finance Research Letters, 2023, vol. 58, issue PB

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.

Keywords: Financial risk identification; Graph structure; Multi-head self-attention; Hierarchical graph attention network (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:58:y:2023:i:pb:s1544612323008309

DOI: 10.1016/j.frl.2023.104458

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