Fintech for the Poor: Financial Intermediation Without Discrimination. (12th September 2020)

• Record Type: Journal Article
• Authors: Tantri, Prasanna
• Is Part Of: Review of finance. Volume 25:Number 2(2021)
• Page Start: 561
• Page End: 593
• Publication Date: 2020-09-12
• Subjects: G21 -- G23 -- G51; Machine Learning -- Discrimination; Finance -- Europe -- Periodicals; Financiën; Finance; Periodicals; 332.09405
• DOI: 10.1093/rof/rfaa039
• Languages: English
• ISSNs: 1572-3097

Abstract

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.