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Explainable models of credit losses

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  • João A. Bastos
  • Sara M. Matos

Abstract

Credit risk management is an area where regulators expect banks to have trans-parent and auditable risk models, which would preclude the use of more accurate black-box models. Furthermore, the opaqueness of these models may hide unknownbiases that may lead to unfair lending decisions. In this study, we show that banksdo not have to sacrifice prediction accuracy at the cost of model transparency tobe compliant with regulatory requirements. We illustrate this by showing that the predictions of credit losses given by a black-box model can be easily explained in terms of their inputs. Because black-box models are better at uncovering complex patterns in the data, banks should consider the determinants of credit losses suggested by these models in lending decisions and pricing of credit exposures.

Suggested Citation

  • João A. Bastos & Sara M. Matos, 2021. "Explainable models of credit losses," Working Papers REM 2021/0161, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  • Handle: RePEc:ise:remwps:wp01612021
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    2. Julia Brasse & Hanna Rebecca Broder & Maximilian Förster & Mathias Klier & Irina Sigler, 2023. "Explainable artificial intelligence in information systems: A review of the status quo and future research directions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-30, December.
    3. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Sun, Weixin & Zhang, Xuantao & Li, Minghao & Wang, Yong, 2023. "Interpretable high-stakes decision support system for credit default forecasting," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    5. Xiong Xiong & Fan Yang & Li Su, 2023. "Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques," Papers 2310.19200, arXiv.org.
    6. Kraus, Mathias & Tschernutter, Daniel & Weinzierl, Sven & Zschech, Patrick, 2024. "Interpretable generalized additive neural networks," European Journal of Operational Research, Elsevier, vol. 317(2), pages 303-316.
    7. Petter Eilif de Lange & Borger Melsom & Christian Bakke Vennerød & Sjur Westgaard, 2022. "Explainable AI for Credit Assessment in Banks," JRFM, MDPI, vol. 15(12), pages 1-23, November.
    8. M. K. Nallakaruppan & Himakshi Chaturvedi & Veena Grover & Balamurugan Balusamy & Praveen Jaraut & Jitendra Bahadur & V. P. Meena & Ibrahim A. Hameed, 2024. "Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence," Risks, MDPI, vol. 12(10), pages 1-18, October.
    9. Ahmed, Abdulaziz & Topuz, Kazim & Moqbel, Murad & Abdulrashid, Ismail, 2024. "What makes accidents severe! explainable analytics framework with parameter optimization," European Journal of Operational Research, Elsevier, vol. 317(2), pages 425-436.
    10. Piccialli, Veronica & Romero Morales, Dolores & Salvatore, Cecilia, 2024. "Supervised feature compression based on counterfactual analysis," European Journal of Operational Research, Elsevier, vol. 317(2), pages 273-285.
    11. Thuy, Arthur & Benoit, Dries F., 2024. "Explainability through uncertainty: Trustworthy decision-making with neural networks," European Journal of Operational Research, Elsevier, vol. 317(2), pages 330-340.
    12. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
    13. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    14. Janssens, Bram & Schetgen, Lisa & Bogaert, Matthias & Meire, Matthijs & Van den Poel, Dirk, 2024. "360 Degrees rumor detection: When explanations got some explaining to do," European Journal of Operational Research, Elsevier, vol. 317(2), pages 366-381.

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    More about this item

    Keywords

    Credit risk; Loss given default; Recovery rates; Explainable machine learning; Forecasting;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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