Multi-View Heterogeneous Graph Neural Network Method for Enterprise Credit Risk Assessment

Wei Shaopeng; Liang Ting; Zhao Yu; Zhuang Fuzhen; Ren Fuji

doi:10.7544/issn1000-1239.202440126

Journal of Computer Research and Development > 2024 > 61(8): 1957-1967. > DOI: 10.7544/issn1000-1239.202440126 CSTR: 32373.14.issn1000-1239.202440126

Wei Shaopeng, Liang Ting, Zhao Yu, Zhuang Fuzhen, Ren Fuji. Multi-View Heterogeneous Graph Neural Network Method for Enterprise Credit Risk Assessment[J]. Journal of Computer Research and Development, 2024, 61(8): 1957-1967. DOI: 10.7544/issn1000-1239.202440126

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Multi-View Heterogeneous Graph Neural Network Method for Enterprise Credit Risk Assessment

1.
School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130
2.
School of Accounting, Southwestern University of Finance and Economics, Chengdu 611130
3.
Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province (Southwestern University of Finance and Economics), Chengdu 611130
4.
Institute of Artificial Intelligence, Beihang University, Beijing 100191
5.
School of Computer Science and Engineering (School of Cybersecurity), University of Electronic Science and Technology of China, Chengdu 611731

Funds: This work was supported by the National Natural Science Foundation of China (62376227).

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Author Bio:
Wei Shaopeng: born in 1997. PhD. His main research interest includes graph learning and its applications on Fintech

Liang Ting: born in 1979. PhD, associate professor. Her main research interests include financial accounting, corporate finance, and interculture communication

Zhao Yu: born in 1985. PhD, professor. His main research interests include machine learning, NLP, knowledge graph, and Fintech

Zhuang Fuzhen: born in 1983. PhD, professor. His main research interests include transfer learning, multi-task learning, multi-view learning, recommendation systems, and knowledge graph

Ren Fuji: born in 1959. PhD, professor. His main research interests include affective computing and artificial intelligence
Received Date: February 28, 2024
Revised Date: May 13, 2024
Available Online: July 04, 2024

Graphical Abstract

Abstract

Abstract

Credit risk assessment for enterprises is a critical issue, significantly impacting investor decisions. It also plays a crucial role in enabling government warnings and the handling of financial risks in a timely manner. Given the numerous heterogeneous relationships in the financial market, graph neural networks are naturally suitable for modeling enterprise credit risk. However, most existing research primarily focuses on modeling either the intra-risk of enterprises based on their financial information or the inter-enterprise contagion risk using simulation methods. Therefore, these approaches fail to fully capture the comprehensive credit risk of enterprises in complex financial networks. To address this limitation in current research, we propose a multi-perspective heterogeneous graph neural network method CRGNN for enterprise credit risk assessment. This method includes an enterprise intra-risk encoder and an enterprise contagion risk encoder, where the enterprise intra-risk encoder models the intra-risk based on enterprise feature information, and the enterprise contagion risk encoder consists of two sub-modules: a hierarchical heterogeneous graph Transformer network and a hierarchical heterogeneous graph feature attention network newly proposed in this paper. These two modules respectively explore contagion risks from the views of different neighbors and different feature dimensions. To fully utilize heterogeneous relationship information, both modules employ hierarchical mechanisms. With these designs, the model proposed in this study can adequately capture the comprehensive credit risk faced by enterprises. Extensive experiments are conducted on the SMEsD bankruptcy prediction dataset and the ECAD enterprise credit assessment dataset, resulting in an improvement of 3.98% and 3.47% in AUC compared with the best baseline model, respectively.
- graph neural networks,
- credit risk assessment,
- contagion risk,
- deep learning,
- FinTech

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References (40)

References

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