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Explainable AI for Credit Assessment in Banks

Author

Listed:
  • Petter Eilif de Lange

    (Department of International Business, Norwegian University of Science and Technology, 6025 Ålesund, Norway)

  • Borger Melsom

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7034 Trondheim, Norway)

  • Christian Bakke Vennerød

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7034 Trondheim, Norway)

  • Sjur Westgaard

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7034 Trondheim, Norway)

Abstract

Banks’ credit scoring models are required by financial authorities to be explainable. This paper proposes an explainable artificial intelligence (XAI) model for predicting credit default on a unique dataset of unsecured consumer loans provided by a Norwegian bank. We combined a LightGBM model with SHAP, which enables the interpretation of explanatory variables affecting the predictions. The LightGBM model clearly outperforms the bank’s actual credit scoring model (Logistic Regression). We found that the most important explanatory variables for predicting default in the LightGBM model are the volatility of utilized credit balance, remaining credit in percentage of total credit and the duration of the customer relationship. Our main contribution is the implementation of XAI methods in banking, exploring how these methods can be applied to improve the interpretability and reliability of state-of-the-art AI models. We also suggest a method for analyzing the potential economic value of an improved credit scoring model.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:12:p:556-:d:986356
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    References listed on IDEAS

    as
    1. Branka Hadji Misheva & Joerg Osterrieder & Ali Hirsa & Onkar Kulkarni & Stephen Fung Lin, 2021. "Explainable AI in Credit Risk Management," Papers 2103.00949, arXiv.org.
    2. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
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    Cited by:

    1. Song, Cen & Ma, Xiaoqian & Ardizzone, Catherine & Zhuang, Jun, 2024. "The adverse impact of flight delays on passenger satisfaction: An innovative prediction model utilizing wide & deep learning," Journal of Air Transport Management, Elsevier, vol. 114(C).
    2. Nils-Gunnar Birkeland Abrahamsen & Emil Nylén-Forthun & Mats Møller & Petter Eilif de Lange & Morten Risstad, 2024. "Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models," JRFM, MDPI, vol. 17(10), pages 1-23, September.

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