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The Application Boundaries and Risk Management of AI in Financial Transactions: An Empirical Study

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  • Jun Xin

    (Aeon Insurance Asset Management Co., Ltd., Shanghai 200120, China)

Abstract

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.

Suggested Citation

  • Jun Xin, 2025. "The Application Boundaries and Risk Management of AI in Financial Transactions: An Empirical Study," Innovation in Science and Technology, Paradigm Academic Press, vol. 4(10), pages 15-21, November.
    • Handle: RePEc:bdz:inscte:v:4:y:2025:i:10:p:15-21
    DOI: 10.63593/IST.2788-7030.2025.11.003
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