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Deciphering big data in consumer credit evaluation

Author

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  • Jiang, Jinglin
  • Liao, Li
  • Lu, Xi
  • Wang, Zhengwei
  • Xiang, Hongyu

Abstract

This paper examines the impact of large-scale alternative data on predicting consumer delinquency. Using a proprietary double-blinded test from a traditional lender, we find that the big data credit score predicts an individual’s likelihood of defaulting on a loan with 18.4% greater accuracy than the lender’s internal score. Moreover, the impact of the big data credit score is more significant when evaluating borrowers without public credit records. We also provide evidence that big data have the potential to correct financial misreporting.

Suggested Citation

  • Jiang, Jinglin & Liao, Li & Lu, Xi & Wang, Zhengwei & Xiang, Hongyu, 2021. "Deciphering big data in consumer credit evaluation," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 28-45.
    • Handle: RePEc:eee:empfin:v:62:y:2021:i:c:p:28-45
    DOI: 10.1016/j.jempfin.2021.01.009
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    References listed on IDEAS

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    Cited by:

    1. Yidi Liu & Xin Li & Zhiqiang (Eric) Zheng, 2024. "Smart Natural Disaster Relief: Assisting Victims with Artificial Intelligence in Lending," Information Systems Research, INFORMS, vol. 35(2), pages 489-504, June.
    2. Ho, Anson T.Y. & Morin, Lealand & Paarsch, Harry J. & Huynh, Kim P., 2022. "A flexible framework for intervention analysis applied to credit-card usage during the coronavirus pandemic," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1129-1157.
    3. Osama Wagdi & Yasmeen Tarek, 2022. "The Integration of Big Data and Artificial Neural Networks for Enhancing Credit Risk Scoring in Emerging Markets: Evidence from Egypt," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 14(2), pages 1-32, February.
    4. Choudhary, Priya & Thenmozhi, M., 2024. "Fintech and financial sector: ADO analysis and future research agenda," International Review of Financial Analysis, Elsevier, vol. 93(C).

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    More about this item

    Keywords

    Big data; FinTech; Personal credit; Large-scale alternative data; Income exaggeration;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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