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

Lars Ole Hjelkrem () and Petter Eilif de Lange
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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

JRFM, 2023, vol. 16, issue 4, 1-19

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.

Keywords: open banking; credit scoring; deep learning; textual data; XAI; SHAP (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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