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Technological revolution and regulatory innovation: How governmental artificial intelligence adoption matters for financial regulation intensity

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  • Pan, Martin
  • Li, Daozheng
  • Wu, Hanrui
  • Lei, Pengfei

Abstract

In the realm of modern financial regulation, the integration of artificial intelligence (AI) is considered to be a potential game-changer. With rapid technological advancement, AI can enhance regulatory capabilities by efficiently processing and analyzing complex financial data. This approach allows for more precise predictions and prevention of market risks, effective monitoring of potential market abuses, and a deeper understanding of financial markets. As a result, AI can substantially improve the efficiency and impact of government regulatory frameworks. This study examined the impact of governmental AI adoption on financial regulatory intensity in China, revealing significant findings across 30 provinces and municipalities from 2012 to 2022. The relevant findings are fourfold. (1) Governmental AI adoption for financial regulation significantly strengthens financial regulatory intensity. (2) The institutional environment and government transparency have respective promotional and restraining influences on this process. (3) Further tests reveal a nonlinear impact of governmental AI adoption for financial regulation on regional financial regulatory intensity. (4) Heterogeneity analysis demonstrates that the enhancing effect of governmental AI adoption is more pronounced in regions located in the east, with strong governance capabilities and well-developed digital environments. The conclusions of this study provide empirical evidence and practical guidance for local governments to develop and refine financial regulatory frameworks by adopting AI.

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  • Pan, Martin & Li, Daozheng & Wu, Hanrui & Lei, Pengfei, 2024. "Technological revolution and regulatory innovation: How governmental artificial intelligence adoption matters for financial regulation intensity," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    • Handle: RePEc:eee:finana:v:96:y:2024:i:pa:s1057521924004678
    DOI: 10.1016/j.irfa.2024.103535
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