Crowds, Lending, Machines, and Bias
38 Pages Posted: 21 Jul 2018 Last revised: 5 Aug 2019
Date Written: July 13, 2019
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 loan 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 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 fintech 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
JEL Classification: Z33, M1
Suggested Citation: Suggested Citation