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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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    1. Rajkamal Iyer & Asim Ijaz Khwaja & Erzo F. P. Luttmer & Kelly Shue, 2016. "Screening Peers Softly: Inferring the Quality of Small Borrowers," Management Science, INFORMS, vol. 62(6), pages 1554-1577, June.
    2. William Adams & Liran Einav & Jonathan Levin, 2009. "Liquidity Constraints and Imperfect Information in Subprime Lending," American Economic Review, American Economic Association, vol. 99(1), pages 49-84, March.
    3. Loretta J. Mester & Leonard I. Nakamura & Micheline Renault, 2007. "Transactions Accounts and Loan Monitoring," Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 529-556.
    4. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    5. Wei Jiang & Ashlyn Aiko Nelson & Edward Vytlacil, 2014. "Liar's Loan? Effects of Origination Channel and Information Falsification on Mortgage Delinquency," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 1-18, March.
    6. Marshall, Andrew & Tang, Leilei & Milne, Alistair, 2010. "Variable reduction, sample selection bias and bank retail credit scoring," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 501-512, June.
    7. Mark A Chen & Qinxi Wu & Baozhong Yang, 2019. "How Valuable Is FinTech Innovation?," Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2062-2106.
    8. Christina Zhu, 2019. "Big Data as a Governance Mechanism," Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2021-2061.
    9. Lars Norden & Martin Weber, 2010. "Credit Line Usage, Checking Account Activity, and Default Risk of Bank Borrowers," Review of Financial Studies, Society for Financial Studies, vol. 23(10), pages 3665-3699, October.
    10. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 71(3), pages 393-410, June.
    11. Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2020. "On the Rise of FinTechs: Credit Scoring Using Digital Footprints," Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2845-2897.
    12. Robert Order & Peter Zorn, 2000. "Income, Location and Default: Some Implications for Community Lending," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 28(3), pages 385-404.
    13. 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.
    14. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    15. Mark J. Garmaise, 2015. "Borrower Misreporting and Loan Performance," Journal of Finance, American Finance Association, vol. 70(1), pages 449-484, February.
    16. Tullio Jappelli, 1990. "Who is Credit Constrained in the U. S. Economy?," The Quarterly Journal of Economics, Oxford University Press, vol. 105(1), pages 219-234.
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    Cited by:

    1. 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.
    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.

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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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