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SHAP Stability in Credit Risk Management: A Case Study in Credit Card Default Model

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  • Luyun Lin
  • Yiqing Wang

Abstract

The increasing development in the consumer credit card market brings substantial regulatory and risk management challenges. The advanced machine learning models applications bring concerns about model transparency and fairness for both financial institutions and regulatory departments. In this study, we evaluate the consistency of one commonly used Explainable AI (XAI) technology, SHAP, for variable explanation in credit card probability of default models via a case study about credit card default prediction. The study shows the consistency is related to the variable importance level and hence provides practical recommendation for credit risk management

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  • Luyun Lin & Yiqing Wang, 2025. "SHAP Stability in Credit Risk Management: A Case Study in Credit Card Default Model," Papers 2508.01851, arXiv.org.
    • Handle: RePEc:arx:papers:2508.01851
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    References listed on IDEAS

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    1. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    2. Andrés Alonso & José Manuel Carbó, 2020. "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers 2032, Banco de España.
    3. Rakshith Bhandary & Bidyut Kumar Ghosh, 2025. "Credit Card Default Prediction: An Empirical Analysis on Predictive Performance Using Statistical and Machine Learning Methods," JRFM, MDPI, vol. 18(1), pages 1-20, January.
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    Cited by:

    1. Shengkun Xie & Tara Shingadia, 2025. "Explainable Machine Learning Framework for Predicting Auto Loan Defaults," Risks, MDPI, vol. 13(9), pages 1-18, September.

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