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A framework for evaluating the business deployability of digital footprint based models for consumer credit

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  • Loutfi, Ahmad Amine

Abstract

Every time we interact with online digital services, we generate large amounts of data that reveal our shopping habits, social interactions, and much more. We refer to these data collectively as the user-generated digital footprint (UGDF). Today, there is growing interest in using UGDF data as an alternative to conventional financial data in building consumer credit models—UGDF models. Unfortunately, we also observe a hype where the models’ business deployability is reduced to simplistic technical metrics, namely, the model’s prediction accuracy. This study argues that this is a misleading oversimplification of the financial sector’s business realities as it ignores vital dimensions such as the model’s economic viability. Therefore, we develop a framework for evaluating the business deployability of UGDF models for consumer credit using a design science research methodology. The framework is composed of seven criteria: Data accessibility, data coverage, data timeliness, data authenticity, cost of deployment, interpretability, and compliance.

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  • Loutfi, Ahmad Amine, 2022. "A framework for evaluating the business deployability of digital footprint based models for consumer credit," Journal of Business Research, Elsevier, vol. 152(C), pages 473-486.
    • Handle: RePEc:eee:jbrese:v:152:y:2022:i:c:p:473-486
    DOI: 10.1016/j.jbusres.2022.07.057
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    1. Yinqiao Li & Renée Spigt & Laurens Swinkels, 2017. "The impact of FinTech start-ups on incumbent retail banks’ share prices," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-16, December.
    2. Eric Rosenberg & Alan Gleit, 1994. "Quantitative Methods in Credit Management: A Survey," Operations Research, INFORMS, vol. 42(4), pages 589-613, August.
    3. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    4. Asli Demirgüč-Kunt & Luc Laeven & Ross Levine, 2004. "Regulations, market structure, institutions, and the cost of financial intermediation," Proceedings, Federal Reserve Bank of Cleveland, pages 593-626.
    5. Hughes, Joseph P. & Mester, Loretta J., 2013. "Who said large banks don’t experience scale economies? Evidence from a risk-return-driven cost function," Journal of Financial Intermediation, Elsevier, vol. 22(4), pages 559-585.
    6. Jagtiani, Julapa & Lemieux, Catharine, 2018. "Do fintech lenders penetrate areas that are underserved by traditional banks?," Journal of Economics and Business, Elsevier, vol. 100(C), pages 43-54.
    7. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    8. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, December.
    9. Bernardo Nicoletti, 2017. "The Future of FinTech," Palgrave Studies in Financial Services Technology, Palgrave Macmillan, number 978-3-319-51415-4, December.
    10. Arráiz, Irani & Bruhn, Miriam & Stucchi, Rodolfo, 2015. "Psychometrics as a Tool to Improve Screening and Access to Credit," IDB Publications (Working Papers) 7266, Inter-American Development Bank.
    11. Arráiz,Irani & Bruhn,Miriam & Stucchi,Rodolfo Mario, 2015. "Psychometrics as a tool to improve screening and access to credit," Policy Research Working Paper Series 7506, The World Bank.
    12. Vanhala, Mika & Lu, Chien & Peltonen, Jaakko & Sundqvist, Sanna & Nummenmaa, Jyrki & Järvelin, Kalervo, 2020. "The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research," Journal of Business Research, Elsevier, vol. 106(C), pages 46-59.
    13. Geerts, Guido L., 2011. "A design science research methodology and its application to accounting information systems research," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 142-151.
    14. Julapa Jagtiani & Catharine Lemieux, 2019. "The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform," Financial Management, Financial Management Association International, vol. 48(4), pages 1009-1029, December.
    15. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    16. Tokunaga, Howard, 1993. "The use and abuse of consumer credit: Application of psychological theory and research," Journal of Economic Psychology, Elsevier, vol. 14(2), pages 285-316, June.
    17. Pamela Meadows & Paul Ormerod & William Cook, 2004. "Social Networks: Their Role in Access to Financial Services in Britain," National Institute Economic Review, National Institute of Economic and Social Research, vol. 189(1), pages 99-109, July.
    18. Bernstein, David, 1996. "Asset quality and scale economies in banking," Journal of Economics and Business, Elsevier, vol. 48(2), pages 157-166, May.
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