Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending

Ruyi Ge, Zhiqiang Zheng, Xuan Tian, Li LiaoInformation Systems数字化转型UTD24

Information Systems Research2021-07-20Shanghai Business School; The University of Texas at Dallas; Tsinghua UniversityDOI

Citations109

Influential4

References29

Semantic Scholar

TL;DR

Analysis of the human–robot interaction of financial-advising services in peer-to-peer lending shows that investors who need more help from robo-advisors are less likely to adopt such services.

We study the human–robot interaction of financial-advising services in peer-to-peer lending (P2P). Many crowdfunding platforms have started using robo-advisors to help lenders augment their intelligence in P2P loan investments. Collaborating with one of the leading P2P companies, we examine how investors use robo-advisors and how the human adjustment of robo-advisor usage affects investment performance. Our analyses show that, somewhat surprisingly, investors who need more help from robo-advisors—that is, those encountered more defaults in their manual investing—are less likely to adopt such services. Investors tend to adjust their usage of the service in reaction to recent robo-advisor performance. However, interestingly, these human-in-the-loop interferences often lead to inferior performance.

BusinessPeer-to-peerService (business)Investment (military)DefaultFinanceMarketingComputer scienceWorld Wide WebFinTech, Crowdfunding, Digital FinanceBlockchain Technology Applications and SecurityMicrofinance and Financial Inclusion