The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank

Lars Ole Hjelkrem (), Petter Eilif de Lange and Erik Nesset
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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
Erik Nesset: Department of International Business, Faculty of Economics, Norwegian University of Science and Technology (NTNU), Larsgårdsvegen 2, 6025 Ålesund, Norway

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

Abstract: Banks generally use credit scoring models to assess the creditworthiness of customers when they apply for loans or credit. These models perform significantly worse when used on potential new customers than existing customers, due to the lack of financial behavioral data for new bank customers. Access to such data could therefore increase banks’ profitability when recruiting new customers. If allowed by the customer, Open Banking APIs can provide access to balances and transactions from the past 90 days before the score date. In this study, we compare the performance of conventional application credit scoring models currently in use by a Norwegian bank with a deep learning model trained solely on transaction data available through Open Banking APIs. We evaluate the performance in terms of the AUC and Brier score and find that the models based on Open Banking data alone are surprisingly effective in predicting default compared to the conventional credit scoring models. Furthermore, an ensemble model trained on both traditional credit scoring data and features extracted from the deep learning model further outperforms the conventional application credit scoring model for new customers and narrows the performance gap between application credit scoring models for existing and new customers. Therefore, we argue that banks can increase their profitability by utilizing data available through Open Banking APIs when recruiting new customers.

Keywords: Open Banking; credit scoring; deep learning; transaction data (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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