Fintech for the Poor: Financial Intermediation without Discrimination

61 Pages Posted: 6 Feb 2020 Last revised: 21 Apr 2020

Date Written: November 1, 2019

Abstract

We ask whether machine learning algorithms improve the efficiency of screening of the loan officers, and thereby help expand access to formal credit. We obtain loan application level data from an Indian bank. To overcome the selective labels problem, we exploit the incentive driven within officer difference in leniency within a calendar month. We find that the ML algorithm is able to lend 26.6% more at loan officers' delinquency rate or achieve 21.3% lower delinquency rate at loan officers' approval rate. Higher efficiency is achieved without compromising on equity.

Keywords: Machine Learning in Finance, Banking, Soft Information, Incentives

JEL Classification: G21, G32

Suggested Citation

Tantri, Prasanna L., Fintech for the Poor: Financial Intermediation without Discrimination (November 1, 2019). Indian School of Business. Available at SSRN: https://ssrn.com/abstract=3518601 or http://dx.doi.org/10.2139/ssrn.3518601

Prasanna L. Tantri (Contact Author)

Indian School of Business ( email )

Hyderabad, Gachibowli 500 032
India
9160099959 (Phone)

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