Abstract
Financial regulators often focus on containing risks in financial services, while paying inadequate attention to regulation’s adverse effects on the underprivileged. This study examines how the upward mobility of borrowers was affected by China’s suppressive regulation of P2P lending in 2018, which switched from an “all-in” policy to an “all-shutdown” policy, leading to a massive closure of P2P lending companies and the effective shutdown of the entire industry by 2021. Leveraging a unique dataset on the Chinese credit market, we show that this one-size-fits-all regulation obstructed borrowers’ upward mobility as reflected by their credit scores and their choices of loan channels. Moreover, we found that those borrowers whose upward mobility was nixed by the regulation tended to commit more crimes. To address these adverse effects, we advocate the use of AI to help stipulate personalized regulatory policies (PolicyTech). By restricting borrowers from accessing P2P lending according to their AI predicted financial risks, we demonstrate the possibility of protecting borrowers’ upward mobility while containing financial risk at the same time, lending to significant theoretical and societal implications.

Biography
Xin is a professor in the Department of Information Systems at City University of Hong Kong. His research interest is on Digital Economy, Data Science/Machine Learning, Social Networks, and Applied Econometrics. His publications appear on MISQ, ISR, JMIS, INFORMS JOC, DSS, I&M, IEEE/ACM Transactions, Nature Nanotechnology, among others. He is a senior member of IEEE, and ACM.