尚福林：“AI+金融”不只是简单的技术叠加 要关注中小机构可能面临的数字化鸿沟

Core Viewpoint - The rapid development of artificial intelligence (AI) presents new tools for enhancing financial services while also posing challenges to financial operating mechanisms, risk management models, and governance systems. The integration of AI in finance is not merely a technical addition but a systemic transformation involving financial development concepts, business models, and governance methods [1][2][3]. Group 1: Challenges and Considerations - The deep application of AI in finance faces several urgent issues that need attention, including the need to balance technological innovation with prudent management, the increasing urgency of data governance, model interpretability, and responsibility boundaries, as well as the structural disparities caused by the digital divide [3]. - There are objective differences among institutions regarding resource allocation for technology investment, data foundations, and application levels. It is essential to encourage leading institutions while also addressing the potential digital divide faced by smaller institutions, requiring a comprehensive approach to maintain inclusivity in the financial system [3]. Group 2: Governance and Regulatory Framework - The integration of AI in finance demands higher governance capabilities, as the rapid development and frequent iteration of AI technologies necessitate continuous adaptation of traditional regulatory and governance methods. Constructing a regulatory framework and governance mechanism that aligns with digital productivity is a challenge that both regulatory bodies and market participants must address [2][3]. Group 3: Promoting Healthy Development of AI in Finance - To promote the healthy development of "AI + finance," it is crucial to balance innovation with regulation, efficiency with safety. Financial institutions should be encouraged to explore AI application scenarios within the bounds of legal compliance, enhancing the functionality of the financial system while serving the real economy and technological innovation [4]. - It is also important to establish robust safety measures by improving data governance systems, internal control processes, and risk prevention mechanisms, ensuring that AI operates in a controllable, trustworthy, and sustainable manner to maintain financial security [4].