Addressing Traditional Credit Scores as a Barrier to Accessing Affordable Credit
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Abstract
Suggested Citation
DOI: 10.18651/ER/v108n3Toh
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References listed on IDEAS
- 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.
- 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.
- Julapa Jagtiani & Catharine Lemieux, 2018. "The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform," Working Papers 18-15, Federal Reserve Bank of Philadelphia.
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Cited by:
- 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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