A survey on deep learning for financial risk prediction

Kuashuai Peng; Guofeng Yan; Kuashuai Peng; Guofeng Yan

doi:10.3934/QFE.2021032

Quantitative Finance and Economics

2021, Volume 5, Issue 4: 716-737. doi: 10.3934/QFE.2021032

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Review

A survey on deep learning for financial risk prediction

Kuashuai Peng ,
Guofeng Yan ^,

School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China

Received: 09 December 2021 Accepted: 20 December 2021 Published: 23 December 2021
JEL Codes: C45, C5, C1, C38

The rapid development of financial technology not only provides a lot of convenience to people's production and life, but also brings a lot of risks to financial security. To prevent financial risks, a better way is to build an accurate warning model before the financial risk occurs, not to find a solution after the outbreak of the risk. In the past decade, deep learning has made amazing achievements in the fields, such as image recognition, natural language processing. Therefore, some researchers try to apply deep learning methods to financial risk prediction and most of the results are satisfactory. The main work of this paper is to review the predecessors' work of deep learning for financial risk prediction according to three prominent characteristics of financial data: heterogeneity, multi-source, and imbalance. We first briefly introduced some classical deep learning models as the model basis of financial risk prediction. Then we analyzed the reasons for these characteristics of financial data. Meanwhile, we studied the differences of commonly used deep learning models according to different data characteristics. Finally, we pointed out some open issues with research significance in this field and suggested the future implementations that might be feasible.
- risk prediction,
- deep learning,
- financial data,
- risk assessment,
- bankruptcy prediction,
- financial distress prediction,
- financial security
Citation: Kuashuai Peng, Guofeng Yan. A survey on deep learning for financial risk prediction[J]. Quantitative Finance and Economics, 2021, 5(4): 716-737. doi: 10.3934/QFE.2021032

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Abstract

The rapid development of financial technology not only provides a lot of convenience to people's production and life, but also brings a lot of risks to financial security. To prevent financial risks, a better way is to build an accurate warning model before the financial risk occurs, not to find a solution after the outbreak of the risk. In the past decade, deep learning has made amazing achievements in the fields, such as image recognition, natural language processing. Therefore, some researchers try to apply deep learning methods to financial risk prediction and most of the results are satisfactory. The main work of this paper is to review the predecessors' work of deep learning for financial risk prediction according to three prominent characteristics of financial data: heterogeneity, multi-source, and imbalance. We first briefly introduced some classical deep learning models as the model basis of financial risk prediction. Then we analyzed the reasons for these characteristics of financial data. Meanwhile, we studied the differences of commonly used deep learning models according to different data characteristics. Finally, we pointed out some open issues with research significance in this field and suggested the future implementations that might be feasible.

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