Review of Finance Vol. 25 No. 2 2021
Fintech for the Poor: Financial Intermediation Without Discrimination
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.
- DOI
- 10.1093/rof/rfaa039
- Volume
- 25
- Issue
- 2
- Pages
- 561-593
- Language
- en
- Sources
- openalex bibtex:phds-export.bib crossref