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Fintech for the Poor: Financial Intermediation Without Discrimination
[Predatory lending and the subprime crisis]

Author

Listed:
  • Prasanna Tantri

Abstract

I ask whether machine learning (ML) algorithms improve the efficiency in lending without compromising on equity in a credit environment where soft information dominates. I obtain loan application-level data from an Indian bank. To overcome the problem of the selective labels, I exploit the incentive-driven within officer difference in leniency within a calendar month. I find that the ML algorithm can lend 60% more at loan officers’ delinquency rate or achieve a 33% lower delinquency rate at loan officers’ approval rate. The efficiency is maintained even when the algorithm is explicitly prevented from discriminating against disadvantaged social classes.

Suggested Citation

  • Prasanna Tantri, 2021. "Fintech for the Poor: Financial Intermediation Without Discrimination [Predatory lending and the subprime crisis]," Review of Finance, European Finance Association, vol. 25(2), pages 561-593.
    • Handle: RePEc:oup:revfin:v:25:y:2021:i:2:p:561-593.
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    File URL: http://hdl.handle.net/10.1093/rof/rfaa039
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    Citations

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    Cited by:

    1. Giorgia Barboni, 2024. "Innovations in the repayment structure of microcredit contracts," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 40(1), pages 129-138.
    2. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    3. Choudhary, Priya & Thenmozhi, M., 2024. "Fintech and financial sector: ADO analysis and future research agenda," International Review of Financial Analysis, Elsevier, vol. 93(C).
    4. Shafiq Ur Rehman & Mustafa Al-Shaikh & Patrick Bernard Washington & Ernesto Lee & Ziheng Song & Ibrahim A. Abu-AlSondos & Maha Shehadeh & Mahmoud Allahham, 2023. "FinTech Adoption in SMEs and Bank Credit Supplies: A Study on Manufacturing SMEs," Economies, MDPI, vol. 11(8), pages 1-15, August.
    5. Tobias Berg & Andreas Fuster & Manju Puri, 2022. "FinTech Lending," Annual Review of Financial Economics, Annual Reviews, vol. 14(1), pages 187-207, November.
    6. Sant'Anna, Dário A.L.M. & Figueiredo, Paulo N., 2024. "Fintech innovation: Is it beneficial or detrimental to financial inclusion and financial stability? A systematic literature review and research directions," Emerging Markets Review, Elsevier, vol. 60(C).
    7. António Porfírio, José & Augusto Felício, José & Carrilho, Tiago, 2024. "Factors affecting digital transformation in banking," Journal of Business Research, Elsevier, vol. 171(C).
    8. D'Acunto, Francesco & Ghosh, Pulak & Jain, Rajiv & Rossi, Alberto G., 2022. "How costly are cultural biases?," LawFin Working Paper Series 34, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
    9. Mestiri, Sami, 2023. "How to use machine learning in finance," MPRA Paper 120045, University Library of Munich, Germany.
    10. Chen, Wen & Wu, Weili & Zhang, Tonghui, 2023. "Fintech development, firm digitalization, and bank loan pricing," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    11. Mestiri, Sami, 2024. "Financial applications of machine learning using R software," MPRA Paper 119998, University Library of Munich, Germany.

    More about this item

    Keywords

    Machine Learning; Discrimination;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

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