The Application Boundaries and Risk Management of AI in Financial Transactions: An Empirical Study
DOI:
https://doi.org/10.63593/IST.2788-7030.2025.11.003Keywords:
AI financial transactions, application boundary quantification, risk spillover, cross-institutional transmission, governance framework, large-scale asset management, VAR model, 3% boundary rule, three-tier governance framework, transaction risk management, empirical research, asset management institutionsAbstract
This paper focuses on quantifying the application boundaries of AI in financial transactions, identifying the cross-institutional risk spillover and transmission patterns, and constructing a multi-dimensional governance framework. Based on a dataset of 986 daily observations from 123 product accounts with a total asset value of 360 billion yuan from 2020 to 2024, combined with in-depth interviews with 15 leading asset management (AM) AI executives and comparative case studies, we develop a two-dimensional “scene-fit-risk tolerance” boundary quantification model and an “AI-securities firm-bank-asset management” risk transmission chain model. We propose the “3% boundary rule” and a three-tier governance framework of “technology-process-regulation.” Empirically validated by a century-old insurance asset management company, this framework reduced the AI transaction risk loss rate from 0.85% to 0.18% while maintaining a 35% improvement in transaction efficiency. It effectively addresses the core pain points of large-scale asset management institutions, such as blind AI application, concealed risk transmission, and an incomplete governance system, providing a solution with both theoretical support and practical value for the industry.
