上海资产管理协会会长、中保投资董事长贾飙：资管业务边界迎扩展

Core Insights - The core viewpoint of the articles emphasizes that AI technology is a transformative force in the asset management industry, redefining investment processes and enhancing efficiency [1][2]. Group 1: AI's Impact on Investment - AI significantly expands human cognitive boundaries by identifying early trends in technology, industry, and market through deep learning of vast, multidimensional, and unstructured data [2]. - AI upgrades risk management from static to dynamic and intelligent, enabling real-time monitoring, stress testing, and proactive warnings against "black swan" and "gray rhino" events [2]. - AI enhances the quality and efficiency of investment research and due diligence, moving from reliance on a few experts to a "data + model" approach that processes large amounts of information for better decision-making [2]. Group 2: Expansion of Boundaries - The service client boundary is expanded, making wealth management services more accessible to the general public through technologies like smart advisory and intelligent customer service [3]. - The service content boundary is broadened, shifting from standardized products to personalized and scenario-based wealth management solutions that adapt to clients' diverse needs [3]. - The investment decision boundary is extended, transitioning from experience-driven to a dual-driven model of "data + model," enhancing the breadth, depth, and speed of decision-making [3]. - The investment target boundary is widened to include emerging digital asset classes such as data assets and cryptocurrencies, beyond traditional stocks, bonds, and funds [3]. - The institutional ecosystem boundary is transformed from a "vertical silo" model to an "open and integrated" ecosystem, fostering collaboration with tech companies and data service providers [3]. Group 3: Core Competencies for Long-term Investors - The application of new technologies like AI helps long-term investors build four core competencies: multi-scenario analysis under macro trend fluctuations, data processing centered on asset value discovery, comprehensive risk management covering the entire investment lifecycle, and cost reduction and efficiency enhancement [3].