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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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    2. Linhui Wang & Jianping Zhu & Chenlu Zheng & Zhiyuan Zhang, 2024. "Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging," Mathematics, MDPI, vol. 12(18), pages 1-15, September.

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