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Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data

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  • Lars Ole Hjelkrem

    (Department of International Business, Faculty of Economics, Norwegian University of Science and Technology (NTNU), Larsgårdsvegen 2, 6025 Ålesund, Norway)

  • Petter Eilif de Lange

    (Department of International Business, Faculty of Economics, Norwegian University of Science and Technology (NTNU), Larsgårdsvegen 2, 6025 Ålesund, Norway)

Abstract

Predicting creditworthiness is an important task in the banking industry, as it allows banks to make informed lending decisions and manage risk. In this paper, we investigate the performance of two different deep learning credit scoring models developed on the textual descriptions of customer transactions available from open banking APIs. The first model is a deep learning model trained from scratch, while the second model uses transfer learning with a multilingual BERT model. We evaluate the predictive performance of these models using the area under the receiver operating characteristic curve (AUC) and Brier score. We find that a deep learning model trained from scratch outperforms a BERT transformer model finetuned on the same data. Furthermore, we find that SHAP can be used to explain such models both on a global level and for explaining rejections of actual applications.

Suggested Citation

  • Lars Ole Hjelkrem & Petter Eilif de Lange, 2023. "Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data," JRFM, MDPI, vol. 16(4), pages 1-19, April.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:4:p:221-:d:1114264
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    References listed on IDEAS

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