人工智能是一项系统性工程，强化AI治理，统筹发展与安全，确保人工智能在金融领域安全、稳健、 可持续发展至关重要。钱斌表示，一是要守牢底线，防范AI等新技术的风险。二是坚持技术变革与制 度优化相结合，建立适配的体制机制。三是加快建设人工智能人才高地，塑造智力新优势。要着力培养 兼具业务洞察、技术理解与数据思维的复合型人才，真正打通业务逻辑与技术逻辑的边界，着力培育一 支知识型、技能型、创新型的AI工程化人才队伍。 "更为根本的是，人工智能必须始终坚持人要成为关键新型技术的主人这一基本法则，在关键判断和风 险处置环节，既要发挥技术的效率和精度，更要保留金融应有的温度和责任，有人来最终把关，确保人 工智能在金融领域安全、稳健、可持续发展。"钱斌说道。 （编辑 张昕） 本报讯 （记者熊悦）在12月28日举办的国民财富发展研究合作平台第三届"AI+金融"峰会上，交通银行 副行长、首席信息官钱斌表示，今年以来，AI技术在金融业渗透的速度和深度前所未有，不仅全方位 提升金融服务效能和水平，也将从根本上重塑金融运行逻辑与发展范式。 钱斌表示，"AI+金融"融合发展的趋势不可逆转。银行要适应未来智能经济发展趋势，围绕业务的底层 ...

另一方面，监管层对券商IT领域的合规要求不断升级。从交易系统安全到人员行为规范，全方位的严监 管格局已然形成。值得关注的是，年内还出现首例券商App因个人信息保护问题被强制下架的事件，还 有多家券商App因违法违规收集使用个人信息被监管"点名"。 业内人士指出，单纯追求技术投入规模已无法适应行业发展需求，优化投入结构、强化合规治理、实 现"技术建设"与"合规运营"的双重平衡，才是数字时代券商的生存之道。 投入换挡：不以规模为唯一指标 近年来，证券行业对信息科技的重视程度不断增强，行业信息技术投入逐年增长。 数据显示，2023年，44家券商披露了信息技术投入，总额达281.1亿元。其中，14家券商投入超10亿 元，合计198.06亿元，占总投入的70.46%。34家券商信息技术投入同比增长，占比为77.27%。2024年， 中国券商行业信息技术投入整体呈现增长态势，30家券商增加投入。 【导读】券商数字化转型下合规考验升级 证券行业数字化与AI转型浪潮澎湃，金融科技深度渗透，科技驱动业务已成行业共识。 2025年是《证券公司网络和信息安全三年提升计划(2023—2025)》的收官之年。回顾三年历程，券商对 信息 ...

石岩对金瑞期货一行的到访表示热烈欢迎，并系统展示了同花顺在期货领域的核心产品与服务。他介 绍，同花顺已依托金融大模型在期货数据服务、智能投顾、智能投研、智能风控等领域形成成熟应用体 系，助力期货公司实现数智化经营升级。"期望在大模型赋能下，携手挖掘更多合作可能，为双方业务 拓展与创新注入更多新动能。" 近日，同花顺与金瑞期货在杭州正式签署深度合作协议。同花顺总裁助理石岩、金瑞期货总经理侯心强 代表双方出席签约仪式并签字，标志着两家企业在前期交易服务合作基础上，开启以AI技术为核心、 覆盖多业务领域的数智化协同新篇章。 签约仪式上，侯心强介绍了公司的业务优势与发展规划。他表示，金瑞期货长期深耕期货衍生品领域， 尤其在贵金属风险管理、产业链产融服务方面积累了丰富实践经验。"当前期货行业正加速迈入数智化 转型关键期，金瑞期货早在三年前便启动'数字金瑞'建设，已构建起智能投研平台、衍生品对客系统等 数字化基础。此次选择与同花顺深度合作，正是希望借助其金融大模型技术优势，进一步升级数字化与 智能化水平，在策略定制、数据融合、合规风控等领域实现突破，持续优化客户服务体验。" 侯心强强 调。 作为行业内率先布局数智化转型的 ...

Core Insights - The company has initiated its AI application strategy since 2017, adopting the "AI in All" approach to empower various business lines and systems [1] - With advancements in large model technology, the company has upgraded its AI strategy to "ALL in AI," transitioning from enabling AI to transformative AI [1] - The company has implemented over 150 AI application scenarios across various business areas, creating a new development pattern of "ubiquitous intelligence" [1] Technology Challenges - The company identifies the "hallucination" and interpretability issues of AI as significant challenges, particularly in the finance sector where precision is critical [2] - To address these challenges, the company has developed a "1+N" application system, combining general large models with industry-specific models to ensure reliable service outputs [2] Investment Focus - The company emphasizes the need to focus AI investments on core business areas to generate sustainable business value, especially as the enthusiasm for large model applications wanes [2] - The return on investment will become a central concern for companies, necessitating AI to address industry pain points effectively [2] Industry Development - The company advocates for the establishment of a regulated development order to maintain a healthy industry ecosystem, urging collaboration among regulators, institutions, and clients [2] - Industry associations are working on guidelines to standardize development paths, which will support the healthy growth of AI in the securities sector [2] Future Outlook - The company acknowledges the immense potential of AI while recognizing the challenges, suggesting that a collaborative approach can transform technical challenges into new development opportunities for the securities industry [2]

Core Insights - The 2025 Greater Bay Area Exchange Technology Conference highlighted the transition of AI technology in the securities industry from conceptual exploration to deep implementation, presenting both opportunities for efficiency and challenges for governance [1] - AI is positioned as a core driver for high-quality development in capital markets, with a focus on integrating AI capabilities with market governance needs [1] - The conference emphasized the importance of aligning AI advancements with regulatory frameworks to enhance market development and regulatory enforcement [1] Group 1: AI Technology Development - AI is recognized as a strategic technology leading a new wave of technological revolution and industrial transformation, with the year 2025 being termed the "Year of AI Agents" [2] - The securities industry is becoming a significant application scenario for AI, driving the sector towards greater intelligence, efficiency, and inclusivity [2] - AI's role in the securities industry is more critical than in other sectors, providing substantial support in customer acquisition and revenue generation [2] Group 2: Implementation and Challenges - Companies like Guotai Junan Securities have integrated AI across various business sectors, achieving over 150 AI applications that enhance risk control, investment research, and trading [3] - Regulatory bodies are actively embracing AI to improve oversight and compliance, integrating AI throughout the regulatory process [3] - The financial industry is accelerating its adoption of AI, overcoming challenges such as data governance and computational power limitations [4] Group 3: Future Outlook and Collaboration - AI is expected to enhance the overall competitiveness of the securities industry by improving customer service, operational efficiency, and promoting high-quality development [4] - Challenges such as AI's "hallucination" problem and lack of interpretability pose risks in the finance sector, necessitating careful consideration [4] - The industry is working towards overcoming AI application bottlenecks through technological advancements, regulatory adaptations, and collaborative innovation [5] Group 4: Strategic Initiatives - The Shenzhen Stock Exchange aims to build a world-class digital and intelligent trading platform by focusing on risk prevention, regulatory strength, and high-quality development [6] - Key initiatives include planning intelligent computing infrastructure, implementing cloud applications, and enhancing AI integration in core business areas [6] - The potential of AI in the securities industry is significant, but its development requires guidance from regulatory bodies, practical exploration by institutions, and cooperation from clients [6]

Group 1 - The core viewpoint emphasizes the need for a rational, value-oriented, and long-term investment approach to enhance the efficiency of China's capital market, which currently suffers from inefficiencies in company pricing and resource allocation [2] - The article discusses the potential of AI technology to create an independent, objective, and quantitative fundamental evaluation system that could improve market efficiency and support the "three investments" concept [2] - The forum scheduled for September 10 aims to explore how AI can empower various aspects of investment, including decision-making, trading, and advisory services for individual investors [2] Group 2 - The agenda includes discussions on how intelligent investment research can enhance decision-making efficiency, featuring industry leaders and experts [4] - Keynote speeches will address the reliability of brokerage research predictions and the opportunities and challenges presented by large models in investment research [4][5] - The forum will also focus on the role of AI and standardized investment research in helping buy-side advisors create value for investors [5]

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].