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FinTech Lending to Borrowers with No Credit History

Author

Listed:
  • Laura Chioda
  • Paul Gertler
  • Sean Higgins
  • Paolina C. Medina

Abstract

Despite the promise of FinTech lending to expand access to credit to populations without a formal credit history, FinTech lenders primarily lend to applicants with a formal credit history and rely on conventional credit bureau scores as an input to their algorithms. Using data from a large FinTech lender in Mexico, we show that alternative data from digital transactions through a delivery app are effective at predicting creditworthiness for borrowers with no credit history. We also show that segmenting our machine learning model by gender can improve credit allocation fairness without a substantive effect on the model’s predictive performance.

Suggested Citation

  • Laura Chioda & Paul Gertler & Sean Higgins & Paolina C. Medina, 2024. "FinTech Lending to Borrowers with No Credit History," NBER Working Papers 33208, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:33208
    Note: CF DEV
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    More about this item

    JEL classification:

    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G5 - Financial Economics - - Household Finance
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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