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Loan default predictability with explainable machine learning

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  • Li, Huan
  • Wu, Weixing

Abstract

This paper studies loan defaults with data disclosed by a lending institution. We comprehensively compare the prediction performance of nine commonly used machine learning models and find that the random forest model has an efficient and stable prediction ability. Then, we apply an explainable machine learning method, i.e., SHapley Additive exPlanations (SHAP), to analyze the important factors affecting loan defaults. Moreover, we conduct an empirical study and find that the significant influencing factors are clearly consistent with those suggested by SHAP: the older the lender and the longer their working experience, the lower the risk of loan default.

Suggested Citation

  • Li, Huan & Wu, Weixing, 2024. "Loan default predictability with explainable machine learning," Finance Research Letters, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:finlet:v:60:y:2024:i:c:s1544612323012394
    DOI: 10.1016/j.frl.2023.104867
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    References listed on IDEAS

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    1. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
    2. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    3. David B. Gross, 2002. "An Empirical Analysis of Personal Bankruptcy and Delinquency," The Review of Financial Studies, Society for Financial Studies, vol. 15(1), pages 319-347, March.
    4. Lane, Sylvia, 1972. "Submarginal Credit Risk Classification," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(1), pages 1379-1385, January.
    5. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    6. Bauer, Julian & Agarwal, Vineet, 2014. "Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 432-442.
    7. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    8. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    9. Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
    10. Rosett, Richard N & Nelson, Forrest D, 1975. "Estimation of the Two-Limit Probit Regression Model," Econometrica, Econometric Society, vol. 43(1), pages 141-146, January.
    11. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1.
    12. Gao, Wei & Ju, Ming & Yang, Tongyang, 2023. "Severe weather and peer-to-peer farmers’ loan default predictions: Evidence from machine learning analysis," Finance Research Letters, Elsevier, vol. 58(PA).
    13. Sigrist, Fabio & Hirnschall, Christoph, 2019. "Grabit: Gradient tree-boosted Tobit models for default prediction," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 177-192.
    14. Richard H. Thaler, 2016. "Behavioral Economics: Past, Present, and Future," American Economic Review, American Economic Association, vol. 106(7), pages 1577-1600, July.
    15. David Durand, 1941. "Risk Elements in Consumer Instalment Financing, Technical Edition," NBER Books, National Bureau of Economic Research, Inc, number dura41-2.
    16. Ma, Yuanyuan & Zhang, Pingping & Duan, Shaodong & Zhang, Tianjie, 2023. "Credit default prediction of Chinese real estate listed companies based on explainable machine learning," Finance Research Letters, Elsevier, vol. 58(PA).
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    Cited by:

    1. Xia Li & Hanghang Zheng & Xiao Chen & Hong Liu & Mao Mao, 2025. "Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring," Papers 2501.10677, arXiv.org, revised Jan 2025.

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