IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v62y2021icp28-45.html
   My bibliography  Save this article

Deciphering big data in consumer credit evaluation

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927539821000098
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jempfin.2021.01.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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," The 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?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2062-2106.
    8. Christina Zhu, 2019. "Big Data as a Governance Mechanism," The 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," The 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," The 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, President and Fellows of Harvard College, vol. 105(1), pages 219-234.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Choudhary, Priya & Thenmozhi, M., 2024. "Fintech and financial sector: ADO analysis and future research agenda," International Review of Financial Analysis, Elsevier, vol. 93(C).
    3. 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.
    4. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tobias Berg & Andreas Fuster & Manju Puri, 2022. "FinTech Lending," Annual Review of Financial Economics, Annual Reviews, vol. 14(1), pages 187-207, November.
    2. Serena Gallo, 2021. "Fintech platforms: Lax or careful borrowers’ screening?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-33, December.
    3. D Dulani Jayasuriya & Mohamed Ayaz & Michael Williams, 2023. "The use of digital footprints in the US mortgage market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 353-401, March.
    4. Wu, Fei & Hu, Yan & Shen, Me, 2024. "The color of FinTech: FinTech and corporate green transformation in China," International Review of Financial Analysis, Elsevier, vol. 94(C).
    5. Tetyana Balyuk, 2023. "FinTech Lending and Bank Credit Access for Consumers," Management Science, INFORMS, vol. 69(1), pages 555-575, January.
    6. Eccles, Peter & Grout, Paul & Siciliani, Paolo & Zalewska, Anna, 2021. "The impact of machine learning and big data on credit markets," Bank of England working papers 930, Bank of England.
    7. Dong, Yingwei & Gou, Qin & Qiu, Han, 2023. "Big tech credit score and default risk ——Evidence from loan-level data of a representative microfinance company in China," China Economic Review, Elsevier, vol. 81(C).
    8. Ruyi Ge & Juan Feng & Bin Gu, 2016. "Borrower’s default and self-disclosure of social media information in P2P lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-6, December.
    9. Kowalewski, Oskar & Pisany, Paweł, 2022. "Banks' consumer lending reaction to fintech and bigtech credit emergence in the context of soft versus hard credit information processing," International Review of Financial Analysis, Elsevier, vol. 81(C).
    10. Berger, Allen N. & Cerqueiro, Geraldo & Penas, María F., 2011. "Does debtor protection really protect debtors? Evidence from the small business credit market," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1843-1857, July.
    11. Hibbeln, Martin & Norden, Lars & Usselmann, Piet & Gürtler, Marc, 2020. "Informational synergies in consumer credit," Journal of Financial Intermediation, Elsevier, vol. 44(C).
    12. Olena Havrylchyk, 2018. "Regulatory framework for the loan-based crowdfunding platforms," OECD Economics Department Working Papers 1513, OECD Publishing.
    13. Xia, Yanchun & Qiao, Zhilin & Xie, Guanghua, 2022. "Corporate resilience to the COVID-19 pandemic: The role of digital finance," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
    14. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    15. Wang, Xiaoting & Hou, Siyuan & Kyaw, Khine & Xue, Xupeng & Liu, Xueqin, 2023. "Exploring the determinants of Fintech Credit: A comprehensive analysis," Economic Modelling, Elsevier, vol. 126(C).
    16. Huang, Yiping & Li, Zhenhua & Qiu, Han & Tao, Sun & Wang, Xue & Zhang, Longmei, 2023. "BigTech credit risk assessment for SMEs," China Economic Review, Elsevier, vol. 81(C).
    17. Zhang, Xiaoyan & Li, Jinbao & Xiang, Dong & Worthington, Andrew C., 2023. "Digitalization, financial inclusion, and small and medium-sized enterprise financing: Evidence from China," Economic Modelling, Elsevier, vol. 126(C).
    18. 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.
    19. Miller, Sarah, 2015. "Information and default in consumer credit markets: Evidence from a natural experiment," Journal of Financial Intermediation, Elsevier, vol. 24(1), pages 45-70.
    20. Lei Xu & Qian Liu & Bin Li & Chen Ma, 2022. "Fintech business and firm access to bank loans," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(4), pages 4381-4421, December.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:empfin:v:62:y:2021:i:c:p:28-45. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jempfin .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.