论坛预告 近年来，监管层一再倡导理性投资、价值投资、长期投资理念以促进资本市场高质量发展。但在中国资 本市场，公司定价和资源配置效率一直有待提高。背后原因众多，除了和中国资本市场散户众多、机构 投资行为散户化相关之外，还和资本市场缺乏一套被广泛认可和接受的基本面评估体系有关。有很多资 讯和研究甚至变成加剧市场无效程度的噪音。 在人工智能时代，第三方研究机构和专业财经媒体，能否结合前沿AI技术，打造一套独立于传统投 研，独立、客观、量化的基本面评价体系，提升资本市场的效率？智能投研能否赋能投资端、交易端、 服务端等多维度共同推动"三投资"理念落地？对于普通个人投资者来说，在人工智能时代需要什么样的 投顾服务？AI技术和标准化投研能否赋能券商买方投顾为投资者发现价值？ 9月10日，来2025 Inclusion·外滩大会见解论坛，听买方、卖方、上市公司、学者分别从投研、投资、投 顾的角度来前瞻下一代金融科技吧！ | 梁宇峰 上海壹评信息技术有限公司总经理 | | | --- | --- | | 14:20-14:25 ○ 榜里友布 | 2025年"壹评级" | | | 中国上市公司最具护城河榜 | | 圆桌讨论 ...

Core Insights - The article discusses the digital transformation of wealth management in the Chinese securities industry, highlighting the need for breaking down departmental silos and addressing core pain points such as "information islands" [1][2][3] Group 1: Digital Transformation Challenges - The lack of cross-departmental collaboration and application barriers are identified as major obstacles to digital transformation in wealth management [2] - Traditional organizational structures are often rigid, making it difficult to adapt to the fast-changing market demands, necessitating a flexible and efficient organizational framework [3] Group 2: Pathways for Digital Transformation - The article outlines a four-stage pathway for digital transformation: "Foundation," "Consolidation," "Expansion," and "Long-term," focusing on building a unified data lake and middle-office structure [4] - Companies are encouraged to create a digital service closed loop, integrating user needs and data flow to eliminate "information islands" [5] Group 3: Future Directions and Innovations - Future directions include enhancing customer experience through AI technology integration, ensuring that technology serves real human needs while maintaining compliance and ethical standards [6] - The importance of establishing a comprehensive digital governance system covering technology development, service delivery, and risk management is emphasized [6]

Core Insights - The article discusses the digital transformation of wealth management in the Chinese securities industry, highlighting the need for collaboration across departments to overcome barriers such as "information silos" and application barriers [1][2][3] Group 1: Industry Challenges - The lack of cross-departmental collaboration and application barriers are identified as major obstacles to the digital transformation of wealth management [2] - Traditional organizational structures are often too rigid to adapt to the fast-changing market demands, necessitating a flexible and efficient organizational framework [3] Group 2: Solutions and Strategies - The article outlines a four-stage path for digital transformation in the industry: "Foundation," "Consolidation," "Expansion," and "Long-term," focusing on building a unified data system and a middle-office structure [4] - Companies are encouraged to create a digital service closed loop centered on user needs, integrating internal and external data sources to form user profiles [5] - The importance of deep integration across all channels is emphasized, ensuring consistent and efficient service delivery to clients [5] Group 3: Future Directions - AI technology is highlighted as a core driver for future transformation, with a focus on enhancing customer experience through intelligent interactions and data integration [6] - The establishment of a comprehensive digital governance framework is recommended to optimize AI transparency and ensure user data protection [6]

Core Insights - The emergence of the DeepSeek-R1 model is refocusing attention on China's AI development and prompting a reevaluation of the value of Chinese tech stocks by global investors [2] - The financial industry is poised to benefit significantly from AI, with potential applications in various operational and customer-facing scenarios [3][20] Group 1: AI Impact on Financial Industry - The financial sector is actively exploring generative AI due to its data-rich environment and high labor intensity, which may lead to greater transformation compared to other industries [3] - UBS is committed to becoming an AI-driven institution, continuously investing in technology to benefit clients, employees, and shareholders responsibly and sustainably [2][16] - The deployment of AI in financial institutions is expected to increase significantly, especially following the introduction of DeepSeek, which alleviates previous constraints [5][6] Group 2: AI Application Development Stages - Financial institutions are progressing through three stages of AI application development, moving from internal applications to more complex customer-facing scenarios [7] - The "Application 1.0" phase includes initial explorations of AI applications such as customer service assistants and risk management tools, while "Application 2.0" will see advancements in areas like intelligent trading and investment decision support [7][11] Group 3: Policy and Regulatory Environment - The Chinese government has established a framework for AI development, emphasizing the importance of technology in financial services and the need for regulatory measures to ensure responsible AI use [8][15] - Recent policies aim to enhance the application of AI in financial services, with a focus on high-value use cases and regulatory compliance [8][6] Group 4: Model and Application Maturity - The performance of large models is critical for industry application penetration, with expectations for significant advancements in domestic models to match international standards [9] - The financial sector is expected to see a shift from B2B applications to more complex B2C applications as model capabilities mature and costs decrease [10] Group 5: UBS's Strategic Initiatives - UBS views AI as a tool to create value, reduce risks, and enhance efficiency, with a focus on large-scale deployment and employee training [16][17] - The company has allocated significant resources to AI governance, ensuring responsible use and compliance with ethical standards [17] Group 6: Support for Chinese Tech Enterprises - UBS is actively involved in supporting Chinese tech companies through diverse financing services, contributing to their growth and internationalization [18][19] - The firm has played a key role in major capital market transactions, including significant IPOs and private placements for leading tech firms [19]

Group 1 - The core viewpoint is that the Chinese capital market is expected to enter a long bull market, driven by improved ROE returns and the increasing influence of leading brands, even if GDP growth slows to a medium-high rate [3][4]. - Wang Sheng has been appointed as the new General Manager of Shenwan Hongyuan Research, succeeding Zhou Haichen, and aims to explore a more flexible and agile organizational structure to empower analysts [1][5]. - The research institute will focus on intelligent investment research, leveraging big data, algorithms, and computing power to enhance its research methodologies and frameworks [6]. Group 2 - The Chinese capital market is characterized by a well-designed top-level structure, improved corporate governance, and a rising awareness of shareholder returns, with dividends and buybacks exceeding financing for three consecutive years [3][4]. - The emergence of Chinese technology companies, such as Huawei and ByteDance, is creating a unique opportunity for growth in the new economy sector, coinciding with the global advancement of artificial intelligence [4]. - Wang Sheng emphasizes the importance of stable teams, solid research styles, and systematic frameworks in building client trust within the sell-side research sector [5].