Crowd, Lending, Machine, and Bias
This research addresses the challenge of improving decision-making and fairness in fintech platforms for lenders and borrowers, though it is incremental as it builds on existing ML methods with a novel application and debiasing approach.
The study tackled the problem of whether machine learning algorithms can outperform human crowd investors in predicting loan defaults on a peer-to-peer lending platform, finding that a sophisticated ML algorithm predicted defaults more accurately, especially for high-risk listings, and when used for investment decisions, it increased returns for investors and funding opportunities for underserved borrowers, though it exhibited bias in gender and race.
Big data and machine learning (ML) algorithms are key drivers of many fintech innovations. While it may be obvious that replacing humans with machine 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 "debasing" 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.