Crowds, Lending, Machine, and Bias

40 Pages Posted: 21 Jul 2018 Last revised: 25 Jun 2020

See all articles by Runshan Fu

Runshan Fu

Carnegie Mellon University

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business

Param Vir Singh

Carnegie Mellon University - David A. Tepper School of Business

Date Written: June 24, 2020

Abstract

Big data and machine learning (ML) algorithms are key drivers of many fintech innovations. While it may be obvious that replacing humans with machines would increase efficiency, it is not clear whether and where machines can make better decisions than humans. We answer this question in the context of crowd lending, where decisions are traditionally made by a crowd of investors. Using data from Prosper.com, we show that a reasonably sophisticated ML algorithm predicts listing default probability more accurately than crowd investors. The dominance of the machine over the crowd is more pronounced for highly risky listings. We then use the machine to make investment decisions, and find that the machine benefits not only the lenders but also the borrowers. When machine prediction is used to select loans, it leads to a higher rate of return for investors and more funding opportunities for borrowers with few alternative funding options.

We also find suggestive evidence that the machine is biased in gender and race even when it does not use gender and race information as input. We propose a general and effective “debiasing” method that can be applied to any prediction focused ML applications, and demonstrate its use in our context. We show that the debiased ML algorithm, which suffers from lower prediction accuracy, still leads to better investment decisions compared with the crowd. These results indicate that ML can help crowd lending platforms better fulfill the promise of providing access to financial resources to otherwise underserved individuals and ensure fairness in the allocation of these resources.

Keywords: fintech, peer-to-peer lending, crowdfunding, machine learning, algorithmic bias, machine vs humans

JEL Classification: Z33, M1

Suggested Citation

Fu, Runshan and Huang, Yan and Singh, Param Vir, Crowds, Lending, Machine, and Bias (June 24, 2020). Available at SSRN: https://ssrn.com/abstract=3206027 or http://dx.doi.org/10.2139/ssrn.3206027

Runshan Fu

Carnegie Mellon University ( email )

5000 Forbes Ave.
Pittsburgh, PA 15213-3890
United States

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Param Vir Singh (Contact Author)

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
412-268-3585 (Phone)

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