Does Skillful Use of Hard Information by Machines Outperform a Combination of Hard and Soft Information of Loan Officers in Lending Decisions?

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.