The Use of Machine Learning Combined with Data Mining Technology in Financial Risk Prevention

Bo Gao ()
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Bo Gao: Communication University of China

Computational Economics, 2022, vol. 59, issue 4, No 5, 1385-1405

Abstract: Abstract In order to improve the ability of enterprises to deal with financial risks, reduce labor costs, reduce financial losses, increase investors' trust in enterprise finance, and establish a comprehensive enterprise financial risk evaluation index system, the deep learning technology and data mining method under the artificial intelligence environment are applied to the financial risk analysis of listed companies. Under this background, an analysis method of financial risk prevention based on interactive mining is put forward. Around the various financial risks faced by listed companies, a special risk analysis model is established to analyze the key factors. Through the empirical analysis of 21 listed companies, rules with high trust are found, and the financial crisis of listed companies is forewarned in time. The results show that the financial risk evaluation index system of four dimensions of solvency, operation ability, profitability, growth ability and cash flow ability can affect the financial risk of enterprises. Compared with the traditional data mining algorithm, the algorithm of financial risk index evaluation model constructed in this exploration has the best performance, and the average detection accuracy is 90.27%. The accuracy of the model can be improved by 30%. The results show that the weight of each variable is good, and all of them pass the consistency test. The evaluation effect is high, and the relative error is 1.55%, which proves the rationality and accuracy of the model. The financial risk prevention model based on deep learning and data mining technology can provide a theoretical basis for the research of enterprise financial risk prevention.

Keywords: Artificial intelligence; Deep learning; Data mining; Financial risk; Evaluation index system (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-021-10101-0

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