Fintech For The Poor: Financial Intermediation Without Discrimination
Keyword(s):
The Poor
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Abstract I ask whether machine learning 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.
Keyword(s):
2019 ◽
Vol 1
(2)
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pp. 78-80
Keyword(s):
2017 ◽
Vol 12
(1)
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pp. 21
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Keyword(s):
Keyword(s):
Keyword(s):
2017 ◽
Vol 5
(9)
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