IDEAS home Printed from https://ideas.repec.org/a/fip/fedker/96375.html
   My bibliography  Save this article

Addressing Traditional Credit Scores as a Barrier to Accessing Affordable Credit

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.kansascityfed.org/Economic%20Review/documents/9602/EconomicReviewV108N3Toh.pdf
    File Function: Full Text
    Download Restriction: no

    File URL: https://libkey.io/10.18651/ER/v108n3Toh?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 479-508.
    2. Rishabh, Kumar & Schäublin, Jorma, 2021. "Payment Fintechs and Debt Enforcement," Working papers 2021/02, Faculty of Business and Economics - University of Basel.
    3. Tang, Yunfeng & Zhang, Xuan & Lu, Shibao & Taghizadeh-Hesary, Farhad, 2023. "Digital finance and air pollution in China: Evolution characteristics, impact mechanism and regional differences," Resources Policy, Elsevier, vol. 86(PA).
    4. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
    5. Guillaume Edou Tchidi & Wei Zhang, 2025. "Mediating effect of financial inclusion on FinTech innovations and economic development in West Africa: Evidence from the Benin Republic," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(2), pages 1032-1048, April.
    6. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    7. Sumin Hu & Qi Zhu & Xia Zhao & Ziyue Xu, 2023. "Digital Finance and Corporate Sustainability Performance: Promoting or Restricting? Evidence from China’s Listed Companies," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    8. Liu, Shiang & Yang, Changyu, 2024. "The role of FinTech lenders in mortgage market: Evidence from corporate relocations," Finance Research Letters, Elsevier, vol. 63(C).
    9. Shota Ichihashi & Alex Smolin, 2023. "Buyer-Optimal Algorithmic Recommendations," Papers 2309.12122, arXiv.org, revised Jun 2025.
    10. Doerr, Sebastian & Frost, Jon & Gambacorta, Leonardo & Shreeti, Vatsala, 2023. "Big techs in finance," CEPR Discussion Papers 18665, C.E.P.R. Discussion Papers.
    11. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    12. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    13. Erik Dolson & Julapa Jagtiani, 2021. "Which Lenders Are More Likely to Reach Out to Underserved Consumers: Banks versus Fintechs versus Other Nonbanks?," Working Papers 21-17, Federal Reserve Bank of Philadelphia.
    14. Gu Ning & Zhao Xiangyuan & Fu Chengbo, 2025. "Can Agriculture-Related Enterprises’ Green Technological Innovation Ride the “Digital Inclusive Finance” Wave?," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 19(1), pages 1-25.
    15. Min Jiang & Wei Zhou & Jiani Zong, 2025. "The role of digital finance on FDI inflow: facilitator or inhibitor?," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 161(3), pages 1113-1138, August.
    16. Yiping Huang & Xue Wang & Xun Wang, 2020. "Mobile Payment in China: Practice and Its Effects," Asian Economic Papers, MIT Press, vol. 19(3), pages 1-18, Fall.
    17. Oyundari Byambaa & Chimedtsogzol Yondon & Enkhbat Rentsen & Bayanjargal Darkhijav & Mahfuzur Rahman, 2025. "An empirical examination of the adoption of artificial intelligence in banking services: the case of Mongolia," Future Business Journal, Springer, vol. 11(1), pages 1-16, December.
    18. Brandon Goldstein & Julapa Jagtiani & Catharine Lemieux, 2025. "Fintech Innovations in Banking: Fintech Partnership and Default Rate on Bank Loans," Working Papers 25-21, Federal Reserve Bank of Philadelphia.
    19. Yidi Liu & Xin Li & Zhiqiang (Eric) Zheng, 2024. "Consequences of China’s 2018 Online Lending Regulation and the Promise of PolicyTech," Information Systems Research, INFORMS, vol. 35(3), pages 1235-1256, September.
    20. Kowalewski, Oskar & Pisany, Paweł, 2022. "Banks' consumer lending reaction to fintech and bigtech credit emergence in the context of soft versus hard credit information processing," International Review of Financial Analysis, Elsevier, vol. 81(C).

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedker:96375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kira Lillard (email available below). General contact details of provider: https://edirc.repec.org/data/frbkcus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.