IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v59y2022i4d10.1007_s10614-021-10101-0.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-021-10101-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-021-10101-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lihui Xiong & Ximiao Dong & Jiaqi Fang, 2023. "Interdisciplinary Teaching Reform of Financial Engineering Majors Based on the Analytic Hierarchy Process in the Post-Pandemic Era," Sustainability, MDPI, vol. 15(11), pages 1-17, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lianjie Zhou & Yuhui Dai, 2023. "Green Production Management and Innovation Nexus: Evidence from Technology-Based SMEs of China," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    2. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    3. Xi Liu & Yugang He & Renhong Wu, 2024. "Revolutionizing Environmental Sustainability: The Role of Renewable Energy Consumption and Environmental Technologies in OECD Countries," Energies, MDPI, vol. 17(2), pages 1-21, January.
    4. Gianluca MISURACA & Colin van Noordt, 2020. "AI Watch - Artificial Intelligence in public services: Overview of the use and impact of AI in public services in the EU," JRC Research Reports JRC120399, Joint Research Centre.
    5. Kissinger, Gabrielle & Gupta, Aarti & Mulder, Ivo & Unterstell, Natalie, 2019. "Climate financing needs in the land sector under the Paris Agreement: An assessment of developing country perspectives," Land Use Policy, Elsevier, vol. 83(C), pages 256-269.
    6. Fabian Dvorak & Regina Stumpf & Sebastian Fehrler & Urs Fischbacher, 2024. "Generative AI Triggers Welfare-Reducing Decisions in Humans," Papers 2401.12773, arXiv.org.
    7. Michael Hübler, 2017. "The Future of Foreign Aid in a Globalizing World with Climate Change," Global Policy, London School of Economics and Political Science, vol. 8(1), pages 41-51, February.
    8. Sergio Genovesi & Julia Maria Mönig, 2022. "Acknowledging Sustainability in the Framework of Ethical Certification for AI," Sustainability, MDPI, vol. 14(7), pages 1-10, March.
    9. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    10. Burka, Dávid & Puppe, Clemens & Szepesváry, László & Tasnádi, Attila, 2022. "Voting: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1003-1017.
    11. Ricardo Vinuesa & Soledad Le Clainche, 2022. "Machine-Learning Methods for Complex Flows," Energies, MDPI, vol. 15(4), pages 1-5, February.
    12. Qian, Yu & Xu, Zeshui & Qin, Yong & Gou, Xunjie & Skare, Marinko, 2023. "Measuring the varying relationships between sustainable development and oil booms in different contexts: An empirical study," Resources Policy, Elsevier, vol. 85(PB).
    13. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2022. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Papers 2201.07168, arXiv.org.
    14. Hanna Obracht-Prondzyńska & Ewa Duda & Helena Anacka & Jolanta Kowal, 2022. "Greencoin as an AI-Based Solution Shaping Climate Awareness," IJERPH, MDPI, vol. 19(18), pages 1-25, September.
    15. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    16. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    17. Guansan Du & Frank Elston, 2022. "RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models," Operations Management Research, Springer, vol. 15(3), pages 925-940, December.
    18. Minou Ella Mebane & Maura Benedetti & Daniela Barni & Donata Francescato, 2023. "Promoting Climate Change Awareness with High School Students for a Sustainable Community," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
    19. Betgilu Oshora & Goshu Desalegn & Eva Gorgenyi-Hegyes & Maria Fekete-Farkas & Zoltan Zeman, 2021. "Determinants of Financial Inclusion in Small and Medium Enterprises: Evidence from Ethiopia," JRFM, MDPI, vol. 14(7), pages 1-19, June.
    20. Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-021-10101-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.