Events

Prof. Xin Li
Professor
Department of Information Systems
City University of Hong Kong

ABSTRACT

Financial regulators often focus on containing risks in financial services; however, they may not simultaneously pay adequate attention to regulation’s adverse effects. This study examines how the economic development of borrowers was affected by China’s suppressive regulation of P2P lending in 2018, which unexpectedly switched from an “all-in” policy to an “all-shutdown” policy, leading to a massive closure of P2P lending companies and the eventual shutdown of the entire industry by 2021. Leveraging data from individuals’ credit applications, we show that this one-size-fits-all regulation obstructed borrowers’ economic development potential, especially for underprivileged and underserved borrowers, as reflected by their credit scores and their selection of financial channels. To alleviate the unintended adverse effects, we advocate using AI to stipulate personalized regulation as a PolicyTech solution. We demonstrate that by restricting some borrowers’ access to P2P lending according to their AI-predicted financial risk, it is possible to protect borrowers’ overall economic development opportunity while containing credit risks. This work yields significant theoretical and societal implications.