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Institutionalizing explainability: On credit scoring, AI, and consumer agency

Author

Listed:
  • Bauer, Kevin
  • Gill, Andrej
  • Langenbucher, Katja
  • Franke, Lucia

Abstract

The paper starts from a situation of information asymmetry on credit markets and zooms in on AIenhanced credit scoring as an institutional response. It assumes the potential for expanding access to credit as well as the risk of discriminatory treatment of historically disadvantaged communities. Against this background, the paper explores legal requirements of "explainability", using two recent European Court of Justice decisions as illustrations. The paper gives an overview of XAI methods along with their socio-technical and legal limits. It contributes to the discussion by suggesting to treat explanations as a public good and designing an intermediary institution which would act as a go-between connecting consumer data subjects and scoring companies.

Suggested Citation

  • Bauer, Kevin & Gill, Andrej & Langenbucher, Katja & Franke, Lucia, 2025. "Institutionalizing explainability: On credit scoring, AI, and consumer agency," SAFE White Paper Series 116, Leibniz Institute for Financial Research SAFE.
    • Handle: RePEc:zbw:safewh:334497
    as

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    References listed on IDEAS

    as
    1. Christa Gibbs & Benedict Guttman-Kenney & Donghoon Lee & Scott Nelson & Wilbert van der Klaauw & Jialan Wang, 2025. "Consumer Credit Reporting Data," Journal of Economic Literature, American Economic Association, vol. 63(2), pages 598-636, June.
    2. Hong Wang & Qingsong Xu & Lifeng Zhou, 2015. "Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-20, February.
    3. Stiglitz, Joseph E & Weiss, Andrew, 1992. "Asymmetric Information in Credit Markets and Its Implications for Macro-economics," Oxford Economic Papers, Oxford University Press, vol. 44(4), pages 694-724, October.
    4. Kevin Bauer & Moritz von Zahn & Oliver Hinz, 2023. "Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing," Information Systems Research, INFORMS, vol. 34(4), pages 1582-1602, December.
    5. Will Dobbie & Paige Marta Skiba, 2013. "Information Asymmetries in Consumer Credit Markets: Evidence from Payday Lending," American Economic Journal: Applied Economics, American Economic Association, vol. 5(4), pages 256-282, October.
    6. Jon Frost & Leonardo Gambacorta & Yi Huang & Hyun Song Shin & Pablo Zbinden, 2019. "BigTech and the changing structure of financial intermediation," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 761-799.
    7. 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.
    8. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    9. Petter Eilif de Lange & Borger Melsom & Christian Bakke Vennerød & Sjur Westgaard, 2022. "Explainable AI for Credit Assessment in Banks," JRFM, MDPI, vol. 15(12), pages 1-23, November.
    10. Taylor A. Begley & Amiyatosh Purnanandam & Kuncheng Zheng, 2017. "The Strategic Underreporting of Bank Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(10), pages 3376-3415.
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