Financial Account Audit Early Warning Based on Fuzzy Comprehensive Evaluation and Random Forest Model

Yuting Zhao, Yupeng Fang and Miaochao Chen

Journal of Mathematics, 2022, vol. 2022, 1-10

Abstract: With the continuous and rapid development of China’s economy, the operating environment of listed companies has become more and more complex, and the increasing pressure of international competition among companies has made the issue of financial risks of listed companies more severe. If you do not pay attention to the financial risk status of the enterprise, it will cause the financial risk to accumulate and eventually cause a financial crisis, which will be marked by ST. Therefore, this paper proposes an early warning model of enterprise financial accounts combining fuzzy sets and random forest trees, which specifically includes the following steps. First, the dataset is analyzed, selected and initially constructed by the training prediction sample. It is further explained by the data labels, and is charged whether the label is marked by ST or not. Then, the method of fuzzy mathematics is used to fuzzify the training sample data, and the two-category label is converted into a multiclass label; then, the random forest model is used to train the above-mentioned fuzzified sample data. Obtain the trained random forest model. Finally, input the prediction sample data into the trained random forest model to make decisions on the scene application. At the same time, the invention is applied to the enterprise financial risk early warning, which demonstrates the practicability and effectiveness of the invention Sexuality and scientificity. The significant advantage of the present invention is that the two-class decision making is converted into the multiclass decision making by combining the fuzzy set and the random forest model, which greatly improves the prediction accuracy, efficiency, and data rationality.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:3090335

DOI: 10.1155/2022/3090335

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