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Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?

Author

Listed:
  • Iyer, Rajkamal

    (MIT)

  • Khwaja, Asim Ijaz

    (Harvard University)

  • Luttmer, Erzo F. P.

    (Harvard University)

  • Shue, Kelly

    (Harvard University)

Abstract

The current banking crisis highlights the challenges faced in the traditional lending model, particularly in terms of screening smaller borrowers. The recent growth in online peer-to-peer lending marketplaces offers opportunities to examine different lending models that rely on screening by multiple peers. This paper evaluates the screening ability of lenders in such peer-to-peer markets. Our methodology takes advantage of the fact that lenders do not observe a borrower's true credit score but only see an aggregate credit category. We find that lenders are able to use available information to infer a third of the variation in creditworthiness that is captured by a borrower's credit score. This inference is economically significant and allows lenders to lend at a 140-basis-points lower rate for borrowers with (unobserved to lenders) better credit scores within a credit category. While lenders infer the most from standard banking "hard" information, they also use non-standard (subjective) information. Our methodology shows, without needing to code subjective information that lenders learn even from such "softer" information, particularly when it is likely to provide credible signals regarding borrower creditworthiness. Our findings highlight the screening ability of peer-to-peer markets and suggest that these emerging markets may provide a viable complement to traditional lending markets, especially for smaller borrowers.

Suggested Citation

  • Iyer, Rajkamal & Khwaja, Asim Ijaz & Luttmer, Erzo F. P. & Shue, Kelly, 2009. "Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?," Working Paper Series rwp09-031, Harvard University, John F. Kennedy School of Government.
  • Handle: RePEc:ecl:harjfk:rwp09-031
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Xiaojiao Yu, 2017. "Machine learning application in online lending risk prediction," Papers 1707.04831, arXiv.org.
    2. Jenq, Christina & Pan, Jessica & Theseira, Walter, 2015. "Beauty, weight, and skin color in charitable giving," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 234-253.
    3. Mingfeng Lin & Nagpurnanand R. Prabhala & Siva Viswanathan, 2013. "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 59(1), pages 17-35, August.
    4. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    5. Qizhi Tao & Yizhe Dong & Ziming Lin, 0. "Who can get money? Evidence from the Chinese peer-to-peer lending platform," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    6. Seth M. Freedman & Ginger Zhe Jin, 2011. "Learning by Doing with Asymmetric Information: Evidence from Prosper.com," NBER Working Papers 16855, National Bureau of Economic Research, Inc.
    7. repec:taf:apeclt:v:24:y:2017:i:19:p:1358-1362 is not listed on IDEAS
    8. Dezső, Linda & Loewenstein, George, 2012. "Lenders’ blind trust and borrowers’ blind spots: A descriptive investigation of personal loans," Journal of Economic Psychology, Elsevier, vol. 33(5), pages 996-1011.
    9. Efraim Berkovich, 2011. "Search and herding effects in peer-to-peer lending: evidence from prosper.com," Annals of Finance, Springer, vol. 7(3), pages 389-405, August.
    10. repec:spr:infosf:v:19:y:2017:i:3:d:10.1007_s10796-017-9751-5 is not listed on IDEAS
    11. Jagtiani, Julapa & Lemieux, Catharine, 2017. "Fintech Lending: Financial Inclusion, Risk Pricing, and Alternative Information," Working Papers 17-17, Federal Reserve Bank of Philadelphia.

    More about this item

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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