Crowds, Lending, Machine, and Bias

Runshan Fu, Yan Huang, Param Vir SinghInformation Systems数据分析UTD24

Information Systems Research2021-02-03Carnegie Mellon UniversityDOI

Citations122

Can machines outperform crowds in financial lending decisions? Using data from a crowd-lending platform, we show that, compared with portfolios created by crowds, a reasonably sophisticated machine can construct financial portfolios that provide better returns while controlling for risk. Further, we find that the machine-created portfolios benefit not only the lenders, but also the borrowers. Borrowers receive loans at a much lower interest rate as the machine can weed out the riskiest loans better than the crowds. We also find suggestive evidence of algorithmic bias in machine decisions. We find that, compared with women, men are more likely to receive loans by machine. We propose a general and effective “debiasing” method that can be applied to any prediction-focused machine learning (ML) applications. We show that the debiased ML algorithm, which suffers from lower prediction accuracy, still improves the crowd’s investment decisions in our context. Our 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.

CrowdsDebiasingComputer scienceContext (archaeology)Machine learningArtificial intelligenceCrowdsourcingInvestment (military)Computer securityWorld Wide WebFinTech, Crowdfunding, Digital FinanceBlockchain Technology Applications and Security