基于风险的人工智能监管：另一个值得拥有的工具或游戏规则改变者（英）2025

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report emphasizes that AI has the potential to transform financial sector supervision, particularly through risk-based supervision (RBS), which has been the gold standard for the past two decades [12][13] - AI can enhance the efficiency and effectiveness of supervisory processes, enabling proactive and preventive measures even in resource-constrained environments [13][15] - The integration of AI into supervisory practices is seen as a game-changer, allowing for automation of routine tasks and improved data processing capabilities [14][35] Summary by Sections Executive Summary - AI is poised to revolutionize the financial sector, particularly in risk-based supervision, which has faced implementation challenges globally [12][18] - The report highlights the need for supervisors to adapt to AI technologies to enhance their capabilities and address existing challenges [12][18] Main Challenges Faced by Financial Sector Supervisors - Supervisory authorities struggle with implementing effective RBS due to limited human resources and outdated processes [19][28] - The report identifies key challenges such as data quality, granularity, and the need for advanced analytical tools [39][53] - Many supervisors have not fully adopted advanced supervisory technologies, which hampers their ability to effectively implement RBS [26][30] Empowering Financial Supervisors with AI Capabilities - AI can automate time-consuming tasks, allowing supervisors to focus on high-risk activities [64][65] - Machine learning and natural language processing can enhance data analysis and improve compliance monitoring [66][67] - AI technologies can help address data quality issues and improve the granularity of data collected by supervisory authorities [70][72] Use Case of AI in Supporting Activities of Supervisory Authorities - Financial institutions are expected to provide large amounts of data, and AI can analyze this data to uncover trends and anomalies [90][91] - Predictive risk modeling using AI allows for proactive risk management, enabling authorities to mitigate potential issues before they escalate [91][92] - Examples of AI adoption by financial authorities include the Australian Securities and Investments Commission's Market Analysis and Intelligence system, which generates real-time alerts for market anomalies [95][96]