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Credit scoring and loan default

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Listed:
  • Geetesh Bhardwaj
  • Rajdeep Sengupta

Abstract

This paper introduces a measure of credit score performance that abstracts from the influence of ?situational factors.? Using this measure, we study the role and effectiveness of credit scoring that underlied subprime securities during the mortgage boom of 2000-2006. Parametric and nonparametric measures of credit score performance reveal different trends, especially on originations with low credit scores. The paper demonstrates an increasing trend of reliance on credit scoring not only as a measure of credit risk but also as a means to offset other riskier attributes of the origination. This reliance led to deterioration in loan performance even though average credit quality?as measured in terms of credit scores? actually improved over the years.

Suggested Citation

  • Geetesh Bhardwaj & Rajdeep Sengupta, 2011. "Credit scoring and loan default," Working Papers 2011-040, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:2011-040
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    References listed on IDEAS

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    1. Foote, Christopher L. & Gerardi, Kristopher & Goette, Lorenz & Willen, Paul S., 2008. "Just the facts: An initial analysis of subprime's role in the housing crisis," Journal of Housing Economics, Elsevier, vol. 17(4), pages 291-305, December.
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Why more bad mortgages? Too much reliance on credit scores
      by Economic Logician in Economic Logic on 2011-12-01 21:29:00

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    Cited by:

    1. Andrew Grant & Luke Deer, 2020. "Consumer marketplace lending in Australia: Credit scores and loan funding success," Australian Journal of Management, Australian School of Business, vol. 45(4), pages 607-623, November.
    2. David Byrne & Robert Kelly & Conor O'Toole, 2022. "How Does Monetary Policy Pass‐Through Affect Mortgage Default? Evidence from the Irish Mortgage Market," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(7), pages 2081-2101, October.
    3. Joanna Stavins, 2020. "Credit Card Debt and Consumer Payment Choice: What Can We Learn from Credit Bureau Data?," Journal of Financial Services Research, Springer;Western Finance Association, vol. 58(1), pages 59-90, August.
    4. Brad S. Trinkle & Amelia A. Baldwin, 2016. "Research Opportunities for Neural Networks: The Case for Credit," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 240-254, July.
    5. Chandrasekhar Valluri & Sudhakar Raju & Vivek H. Patil, 2022. "Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 279-296, September.
    6. Yushu Zhu, 2017. "Call it good, bad or no news? The valuation effect of debt issues," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(4), pages 1203-1229, December.
    7. David Pla-Santamaria & Mila Bravo & Javier Reig-Mullor & Francisco Salas-Molina, 2021. "A multicriteria approach to manage credit risk under strict uncertainty," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 494-523, July.
    8. Bhardwaj, Geetesh & Sengupta, Rajdeep, 2014. "Subprime cohorts and loan performance," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 236-252.
    9. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.

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    More about this item

    Keywords

    Credit scoring systems; Mortgage loans;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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