Explainable AI for Credit Assessment in Banks

Petter Eilif de Lange (), Borger Melsom, Christian Bakke Vennerød and Sjur Westgaard
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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

JRFM, 2022, vol. 15, issue 12, 1-23

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.

Keywords: credit risk modelling; credit default prediction; explainable artificial intelligence (XAI); Light Gradient Boosting Machine (LightGBM) (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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