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

Author:

Ge Ruyi1,Zheng Zhiqiang (Eric)2ORCID,Tian Xuan3,Liao Li3

Affiliation:

1. Department of Electronic Commerce, Shanghai Business School, Shanghai 200235, China;

2. Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080;

3. PBC School of Finance, Tsinghua University, Beijing 100083, China

Abstract

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.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Library and Information Sciences,Information Systems and Management,Computer Networks and Communications,Information Systems,Management Information Systems

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