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

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  • Bo Gao

    (Communication University of China)

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.

Suggested Citation

  • Bo Gao, 2022. "The Use of Machine Learning Combined with Data Mining Technology in Financial Risk Prevention," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1385-1405, April.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-021-10101-0
    DOI: 10.1007/s10614-021-10101-0
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    References listed on IDEAS

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    1. Bin Li & Geng Peng & Benfu Lv, 2019. "Financial Structure Reform and Enterprise Debt Risk Prevention," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(4), pages 507-516, April.
    2. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    3. Olga Sosnovska & Maksym Zhytar, 2018. "Financial Architecture As The Base Of The Financial Safety Of The Enterprise," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", vol. 4(4).
    4. Sihem Khemakhem & Younes Boujelbene, 2018. "Predicting credit risk on the basis of financial and non-financial variables and data mining," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 17(3), pages 316-340, August.
    5. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    6. Andreas Ch. Hadjichambis & Demetra Paraskeva-Hadjichambi, 2020. "Environmental Citizenship Questionnaire (ECQ): The Development and Validation of an Evaluation Instrument for Secondary School Students," Sustainability, MDPI, vol. 12(3), pages 1-12, January.
    7. Judit Oláh & Sándor Kovács & Zuzana Virglerova & Zoltán Lakner & Maria Kovacova & József Popp, 2019. "Analysis and Comparison of Economic and Financial Risk Sources in SMEs of the Visegrad Group and Serbia," Sustainability, MDPI, vol. 11(7), pages 1-19, March.
    8. Sangjung Ha & Thomas Hale & Peter Ogden, 2016. "Climate Finance in and between Developing Countries: An Emerging Opportunity to Build On," Global Policy, London School of Economics and Political Science, vol. 7(1), pages 102-108, February.
    9. Stelios Bekiros & Syed Jawad Hussain Shahzad & Rania Jammazi & Chaker Aloui, 2020. "Spillovers across European sovereign credit markets and role of surprise and uncertainty," Applied Economics, Taylor & Francis Journals, vol. 52(8), pages 851-865, February.
    10. Bin Li & Geng Peng & Benfu Lv, 2019. "Financial Structure Reform and Enterprise Debt Risk Prevention," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(4), pages 507-516.
    11. Chu V. Nguyen, 2018. "The Mexican Interest Rate Pass-Through in the Post-U.S. Subprime Mortgage Crisis Era," The International Trade Journal, Taylor & Francis Journals, vol. 32(1), pages 100-115, January.
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