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Credit scoring and loan default

Author

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  • Geetesh Bhardwaj
  • Rajdeep Sengupta

Abstract

A metric of credit score performance is developed to study the usage and performance of credit scoring in the loan origination process. We examine the performance of origination FICO scores as measures of ex ante borrower creditworthiness using loan-level data on ex post performance of subprime mortgages. Parametric and nonparametric estimates of credit score performance reveal different trends, especially on originations with low credit scores. The data suggest a trend of increased emphasis on higher credit scores accompanying a trend of increased riskiness in other origination attributes. Over time, this increased emphasis on credit scoring coincided with deterioration in FICO performance largely due to the fact that higher credit score originations of later cohorts were more likely to have riskier attributes. However, controlling for other attributes on originations and changes in economic conditions, we find that, as measures of borrower ranking, FICO performance on subprime loans over the years remains fairly stable.

Suggested Citation

  • Geetesh Bhardwaj & Rajdeep Sengupta, 2015. "Credit scoring and loan default," Research Working Paper RWP 15-2, Federal Reserve Bank of Kansas City.
  • Handle: RePEc:fip:fedkrw:rwp15-02
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    File URL: https://www.kansascityfed.org/documents/7727/rwp15-02.pdf
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    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.
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    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; subprime mortgages;

    JEL classification:

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

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