Consequences of China's 2018 Online Lending Regulation and the Promise of PolicyTech

• 成果类型: Article
• 署名作者: Liu, Yidi; Li, Xin; Zheng, Zhiqiang (Eric)
• 署名单位: The Chinese University of Hong Kong, Shenzhen; The Chinese University of Hong Kong, Shenzhen; City University of Hong Kong; University of Texas System; University of Texas Dallas
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2021.0580
• 发表日期: 2024
• 页码: 1235-1256
• 关键词: credit access evidence; Financial inclusion; Causal Inference; BANK; IMPACT; INFORMATION; households

摘要

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 peer-to-peer (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 on 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 artificial intelligence (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.