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

Author

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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)

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.

Suggested Citation

  • Lars Ole Hjelkrem & Petter Eilif de Lange & Erik Nesset, 2022. "The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank," JRFM, MDPI, vol. 15(12), pages 1-15, December.
  • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:12:p:597-:d:1000763
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    References listed on IDEAS

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    1. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    2. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    3. Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2020. "On the Rise of FinTechs: Credit Scoring Using Digital Footprints," The Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2845-2897.
    4. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    5. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
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