Core Viewpoint - The article discusses the transformative impact of Artificial Intelligence (AI) on the financial industry, highlighting both the advancements in efficiency and service capabilities, as well as the structural challenges and risks that arise from its integration [3][4][5]. Group 1: AI's Integration in Financial Services - AI is reshaping the financial industry's operational logic and ecological structure, moving from a tool for efficiency to a systemic transformation of service models, risk management, organizational structures, and market boundaries [5][6]. - The Chinese government is accelerating policy guidance and strategic deployment to promote the large-scale and commercial application of AI in key sectors, including finance [5][6]. - The service paradigm is shifting from "institution-centered" to "user-centered," enabling personalized services and dynamic responses through technologies like customer profiling and natural language processing [6][7]. Group 2: Changes in Decision-Making and Organizational Structure - Financial decision-making is transitioning from an "experience-driven" approach to a "data-driven" intelligent system, enhancing the scientific and forward-looking nature of financial decisions [6][7]. - The organizational structure is evolving from a "functional division" model to a "platform collaboration" model, where technology capabilities become a core competitive advantage [7]. - Business boundaries are expanding from "closed finance" to "open embedded finance," allowing financial services to integrate seamlessly into various non-financial scenarios [7]. Group 3: Risks and Challenges of AI in Finance - The use of AI introduces risks such as model opacity and insufficient interpretability, which can hinder understanding and accountability in financial decision-making [9][10]. - There is a rising risk of over-reliance on data and potential privacy breaches, as AI systems depend heavily on large-scale, multi-dimensional individual data [10][11]. - Systemic technological risks and amplification effects are significant concerns, particularly in high-frequency trading and automated market-making, where errors can lead to severe market disruptions [12][13].