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Does credit scoring produce a disparate impact?

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Abstract
The widespread use of credit scoring in the underwriting and pricing of mortgage and consumer credit has raised concerns that the use of these scores may unfairly disadvantage minority populations. A specific concern has been that the independent variables that comprise these models may have a disparate impact on these demographic groups. By \"disparate impact\" we mean that a variable's predictive power might arise not from its ability to predict future performance within any demographic group, but rather from acting as a surrogate for group membership. Using a unique source of data that combines a nationally representative sample of credit bureau records with demographic information from the Social Security Administration and a demographic information company, we examine the extent to which credit history scores may have such a disparate impact. Our examination yields no evidence of disparate impact by race (or ethnicity) or gender. However, we do find evidence of limited disparate impact by age, in which the use of variables related to an individual's credit history appear to lower the credit scores of older individuals and increase them for the young.

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  • Robert B. Avery & Kenneth P. Brevoort & Glenn B. Canner, 2010. "Does credit scoring produce a disparate impact?," Finance and Economics Discussion Series 2010-58, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2010-58
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    References listed on IDEAS

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    1. Mitchell Stengel & Dennis Glennon, 1999. "Evaluating Statistical Models of Mortgage Lending Discrimination: A Bank‐Specific Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 27(2), pages 299-334, June.
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    5. Marsha J. Courchane, 2007. "The Pricing of Home Mortgage Loans to Minority Borrowers: How Much of the APR Differential Can We Explain?," Journal of Real Estate Research, American Real Estate Society, vol. 29(4), pages 399-440.
    6. Stephen L. Ross & John Yinger, 2002. "The Color of Credit: Mortgage Discrimination, Research Methodology, and Fair-Lending Enforcement," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262182289, April.
    7. Munnell, Alicia H. & Geoffrey M. B. Tootell & Lynn E. Browne & James McEneaney, 1996. "Mortgage Lending in Boston: Interpreting HMDA Data," American Economic Review, American Economic Association, vol. 86(1), pages 25-53, March.
    8. James A. Berkovec & Glenn B. Canner & Stuart A. Gabriel & Timothy H. Hannan, 1994. "Race, redlining, and residential mortgage loan performance," Proceedings, Federal Reserve Bank of Philadelphia, pages 263-298.
    9. Black, Harold A. & Boehm, Thomas P. & DeGennaro, Ramon P., 2003. "Is there discrimination in mortgage pricing? The case of overages," Journal of Banking & Finance, Elsevier, vol. 27(6), pages 1139-1165, June.
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    Cited by:

    1. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    2. Patrick Bayer & Fernando Ferreira & Stephen L. Ross, 2016. "The Vulnerability of Minority Homeowners in the Housing Boom and Bust," American Economic Journal: Economic Policy, American Economic Association, vol. 8(1), pages 1-27, February.
    3. Dubravka Ritter & David Skanderson, 2014. "Fair lending analysis of credit cards," Consumer Finance Institute discussion papers 14-2, Federal Reserve Bank of Philadelphia.
    4. Ulbricht, Lena, 2020. "Algorithmen und Politisierung [Algorithms and politicization]," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 0, pages 255-278.
    5. Patrick Bayer & Fernando Ferreira & Stephen L. Ross, 2016. "The Vulnerability of Minority Homeowners in the Housing Boom and Bust," American Economic Journal: Economic Policy, American Economic Association, vol. 8(1), pages 1-27, February.
    6. David Nickerson & Robert Jones, 2017. "Collateral Risk and Demographic Discrimination in Mortgage Market Equilibria," Review of Economics & Finance, Better Advances Press, Canada, vol. 9, pages 13-28, August.
    7. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    8. Liming Brotcke, 2022. "Time to Assess Bias in Machine Learning Models for Credit Decisions," JRFM, MDPI, vol. 15(4), pages 1-10, April.
    9. Fumiko Hayashi & Joanna Stavins, 2012. "Effects of credit scores on consumer payment choice," Research Working Paper RWP 12-03, Federal Reserve Bank of Kansas City.
    10. Vitaly Meursault & Daniel Moulton & Larry Santucci & Nathan Schor, 2022. "One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas," Working Papers 22-39, Federal Reserve Bank of Philadelphia.
    11. Ryan M. Goodstein & Alicia Lloro & Sherrie L.W. Rhine & Jeffrey M. Weinstein, 2021. "What accounts for racial and ethnic differences in credit use?," Journal of Consumer Affairs, Wiley Blackwell, vol. 55(2), pages 389-416, June.
    12. Robert Clifford & Daniel Shoag, 2016. "“No more credit score”: employer credit check bans and signal substitution," Working Papers 16-10, Federal Reserve Bank of Boston.
    13. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.
    14. Ballance, Joshua & Clifford, Robert & Shoag, Daniel, 2020. "“No more credit score”: Employer credit check bans and signal substitution," Labour Economics, Elsevier, vol. 63(C).
    15. Ha Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," Working Papers hal-04141336, HAL.
    16. Laura Blattner & Scott Nelson, 2021. "How Costly is Noise? Data and Disparities in Consumer Credit," Papers 2105.07554, arXiv.org.

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    Keywords

    Credit scoring systems; Mortgage loans; Discrimination in consumer credit;
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