Research on optimization of an enterprise financial risk early warning method based on the DS-RF model

• Published in: International review of financial analysis Vol. 81; p. 102140
• Main Authors: Zhu, Weidong, Zhang, Tianjiao, Wu, Yong, Li, Shaorong, Li, Zhimin
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.05.2022
• Subjects: Evidence theory; Financial risk early warning; Machine learning; Random forest; Risks management; Risks management; Random forest; Evidence theory; Financial risk early warning; Machine learning
• ISSN: 1057-5219; 1873-8079
• DOI: 10.1016/j.irfa.2022.102140

The financial risk early warning process of enterprises faces problems such as uncertainty and complexity. In the big data environment, scholars and enterprises that continue to use traditional evaluation methods will face large challenges. It is essential for an enterprise's sustainable operation to combine artificial intelligence algorithms, dynamically monitor its financial risks, and carry out financial risk early warning processes accurately and effectively. This study proposes an early warning method for corporate financial risks based on the evidence theory-random forest (DS-RF) model. The classic algorithm of machine learning—random forest was introduced into the framework of evidence theory to construct a random forest model with four dimensions: profitability, asset quality, debt risk, and operating growth. While predicting the risk, the credibility of the evidence was determined, and then the D-S synthesis rule was used for information fusion. An example was analyzed, taking JS Reclamation Group as the study subject. The comparison with the early warning results of the random forest algorithm and the traditional model shows that the DS-RF model proposed in this paper has a higher early warning accuracy and the results are presented more comprehensively and systematically, which effectively improves the efficiency of enterprise financial risk early warning and helps managers to make relevant decisions efficiently and scientifically. •Random forest algorithm can obtain the basic probability assignment of evidence theory more objectively and intelligently.•This paper integrates evidence theory and random forest algorithm to establish an enterprise financial risk warning model.•The DS-RF model can not only objectively predict the financial risks, but also trace the causes of financial risks.•The validity and reliability of the model are proved by the empirical study of the JS Agricultural Reclamation Group.