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Interpretable AI for financial risk perception: A machine learning approach to corporate crisis prediction

Author

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  • Geng, Xiaoyuan
  • Wang, Haonan
  • Yan, Liangyu

Abstract

Predicting financial crises is important for maintaining stable markets and helping people make smart choices. Machine learning has made better predictions; however, many models are still difficult to understand and use. This study created an easy-to-understand AI method to predict financial crises using an improved LightGBM model. This study uses data on Chinese companies covering 2007 to 2023. This study examined 1293 indicators, such as financial ratios, company reports, and media attention. To fix the data issues, the model used random oversampling and Bayesian tuning. Tests showed that the model worked well, reducing the investment risk by >60 %. They found 15 key predictors, including ROE and market type. The SHAP analysis shows how these factors affect crisis chances, providing clear insights into the risks. This study shows that understandable AI can help investors and managers to see risks better and act early. These findings help to turn complex predictions into clear and useful insights for financial decisions.

Suggested Citation

  • Geng, Xiaoyuan & Wang, Haonan & Yan, Liangyu, 2026. "Interpretable AI for financial risk perception: A machine learning approach to corporate crisis prediction," Finance Research Letters, Elsevier, vol. 89(C).
    • Handle: RePEc:eee:finlet:v:89:y:2026:i:c:s1544612325025668
    DOI: 10.1016/j.frl.2025.109317
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    References listed on IDEAS

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    1. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    2. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 199-235, March.
    3. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03529325, HAL.
    4. Jiandong Chen & Douglas Cumming & Wenxuan Hou & Edward Lee, 2016. "Does the External Monitoring Effect of Financial Analysts Deter Corporate Fraud in China?," Journal of Business Ethics, Springer, vol. 134(4), pages 727-742, April.
    5. Kedia, Simi & Rajgopal, Shiva, 2011. "Do the SEC's enforcement preferences affect corporate misconduct?," Journal of Accounting and Economics, Elsevier, vol. 51(3), pages 259-278, April.
    6. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    7. Scott, James, 1981. "The probability of bankruptcy: A comparison of empirical predictions and theoretical models," Journal of Banking & Finance, Elsevier, vol. 5(3), pages 317-344, September.
    8. repec:eme:mfppss:mf-06-2020-0332 is not listed on IDEAS
    9. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03380580, HAL.
    10. Jiaxing You & Bohui Zhang & Le Zhang, 2018. "Who Captures the Power of the Pen?," The Review of Financial Studies, Society for Financial Studies, vol. 31(1), pages 43-96.
    11. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    12. Kong, Dongmin & Yang, Yiwei & Wang, Qin, 2023. "Innovative efficiency and firm value: Evidence from China," Finance Research Letters, Elsevier, vol. 52(C).
    13. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    14. Chih‐Chun Chen & Chun‐Da Chen & Donald Lien, 2020. "Financial distress prediction model: The effects of corporate governance indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1238-1252, December.
    15. Alexander Dyck & Adair Morse & Luigi Zingales, 2010. "Who Blows the Whistle on Corporate Fraud?," Journal of Finance, American Finance Association, vol. 65(6), pages 2213-2253, December.
    16. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 237-291, March.
    17. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
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