Crowd, Lending, Machine, and Bias

TL;DR

This paper compares machine learning algorithms to human investors in crowd lending, showing ML's superior prediction accuracy, benefits for lenders and borrowers, and addressing biases with a debiasing method.

Contribution

It demonstrates ML's effectiveness over crowds in predicting defaults, improves investment outcomes, and introduces a debiasing technique for fairer ML predictions.

Findings

ML predicts default risk more accurately than crowds.

Using ML increases returns and funding for underserved borrowers.

ML exhibits bias in gender and race, but can be mitigated with debiasing.

Abstract

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.