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Addressing Traditional Credit Scores as a Barrier to Accessing Affordable Credit

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  • Ying Lei Toh

Abstract

Affordable credit enables consumers to better manage their finances, cope with unexpected emergencies, and pursue opportunities such as entrepreneurship or higher education. However, many consumers face difficulties obtaining the credit they need. A major impediment is lenders’ reliance on traditional credit scores to assess consumers’ creditworthiness. These credit scores affect not only loan approval decisions but also the interest rates consumers pay on their loans. While credit scores are intended to help lenders make informed decisions about consumers’ risk of default, they do not always accurately reflect a borrower’s ability to repay. Traditional credit scores may also disproportionately punish consumers from economically disadvantaged groups. Ying Lei Toh examines the barrier traditional credit scores pose to obtaining affordable credit in the United States and discusses efforts to address this barrier. Using data from the 2019 Survey of Consumer Finances, she finds that traditional credit scores may indeed hinder a sizeable share of consumers from obtaining the credit they desire. Further, disparities in credit access across several sociodemographic groups match the disparities in their likelihood of having high traditional credit scores, suggesting lenders’ reliance on traditional credit scores may drive disparities in credit access.

Suggested Citation

  • Ying Lei Toh, 2023. "Addressing Traditional Credit Scores as a Barrier to Accessing Affordable Credit," Economic Review, Federal Reserve Bank of Kansas City, vol. 0(no. 3), pages 1-22, June.
  • Handle: RePEc:fip:fedker:96375
    DOI: 10.18651/ER/v108n3Toh
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    References listed on IDEAS

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    1. 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.
    2. Charles B. Perkins & J. Christina Wang, 2019. "How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data," Working Papers 19-16, Federal Reserve Bank of Boston.
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    Cited by:

    1. Hayashi, Fumiko & Routh, Aditi & Toh, Ying Lei, 2024. "Heterogeneous unbanked households: Which types of households are more (or less) likely to open a bank account?," Journal of Economics and Business, Elsevier, vol. 129(C).

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    More about this item

    Keywords

    credit scores; payments; credit availability;
    All these keywords.

    JEL classification:

    • E59 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Other
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • J33 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Compensation Packages; Payment Methods

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