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Field
Abstract
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Before
This study compares the relative effectiveness of standard microcredit and digital credit in minimizing borrower default and producing efficient lending outcomes. In partnership with a financial technology company based in Pakistan, I will implement a randomized trial that randomizes the agent - i.e., loan officer or machine learning algorithm - as well as the information available to that agent in making a credit decision. The results from this intervention will shed light of machine learning techniques' potential, if any, to reduce borrower default relative to a human only model of credit approval in informal credit markets.
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After
This study compares the relative effectiveness of standard microcredit and digital credit in minimizing borrower default and producing efficient lending outcomes. In partnership with a financial technology company based in Pakistan, I implement a randomized trial that randomizes the agent - i.e., loan officer or machine learning algorithm - as well as the information available to that agent in making a credit decision. The results from this intervention will shed light on machine learning techniques' potential, if any, to reduce borrower default relative to a human only model of credit approval in informal credit markets.
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