金融智能体行业丨研究报告 摘要： 本报告基于技术发展周期视角 ， 对中国金融智能体的落地现状和趋势展开了深度洞察 ，阐述了 金融智能体在关键周期阶段的主要表现 ， 期望能够为行业提供一份 拥有参考价值的研究内容。 三重驱动因素推进金融智能体发展 相比近年来金融机构采纳的各类新兴技术，大模型及智能体的发展在"技术突破、业务创新与政策支持"的多重因素驱动下，展现 出更为强劲的发展势头 近年来，各类新兴技术相继涌现，均在初步探索期获得市场关注，也都经历了从概念炒作到理性回归的过程。这些技术中，部分通过重塑业务流程实 现稳健发展，部分则因未能规模落地而发展停滞。多家金融机构技术负责人反映，尽管各类新兴技术持续影响金融科技战略布局，但很多决策者日趋 理性，会审慎对待市场炒作，从而更关注技术的实际价值。 与其他技术相比，大语言模型、金融大模型及智能体的创新展现出显著不同的特质。它们凭借技术突破和场景应用创新，为金融业务升级开辟了新路 径；加之政策层面的积极引导，共同为技术的发展构建了坚实的支撑。这种技术、场景创新与政策的多重共振，使大模型驱动的智能体在中国市场展 现出强劲的内生动力。目前，很多金融机构也在采取"战略积极、 ...

为此，他提出，应尽早确立金融智能体的法律地位。"主要是明确金融智能体的行为边界，明确金融智 能体与金融客户的法理关系，明确金融机构管理者的决策责任，同时建立金融智能体评估审核制 度。"李礼辉说道。 新京报贝壳财经讯（记者黄鑫宇）当前，人工智能正以前所未有的广度与深度重塑银行业等金融机构发 展格局。 中国银行原行长李礼辉近日在参加由四十人高级金融学院和新金融联盟主办的"数智银行家2025年会暨 新金融联盟九周年庆典——数智同行·共启新程"上表示，金融行业、金融机构的价值取向将影响AI替代 的具体速度和深度，即AI替代发生的时间节点、具体岗位和替代比率。 对于上述观点，李礼辉认为，将取决于（金融）智能体的专业性和可靠性，取决于金融监管对智能体的 评估和审核，同时也取决于劳动就业观念和政策的容纳和许可。 "例如，已经投入应用并逐步升级的智能投资顾问，拥有更大的知识面，更专业的分析能力，更冷静的 情绪判断，可将投资顾问从参差不齐的个人专业水平提升到整齐划一的最佳专业水平，有些金融高管认 为智能投资顾问可能替代60%以上的投资顾问岗位。"李礼辉举例道。 当前，金融场景已成为智能体应用的重要赛道。在2025年，广发银行、 ...

Core Insights - The report provides a comprehensive analysis of the current state and trends of financial intelligent agents in China, emphasizing their development driven by technological breakthroughs, business innovations, and policy support [1][2]. Group 1: Driving Factors - Technological breakthroughs are addressing the "last mile" challenges in the application of large models, enhancing their execution capabilities through advancements in tools and frameworks [6]. - Approximately 33% of financial institutions are actively investing in intelligent agents, indicating a growing recognition of their practical value [7]. - Policy support is providing clear guidelines and goals for the application and development of intelligent agents in finance, with specific focus areas outlined in various governmental documents [8][10]. Group 2: Current Industry Cycle - The financial intelligent agent industry is in its initial exploration phase, with 96% of applications still in the proof of concept or pilot stages, and only 4% having moved to agile practice [12]. - The majority of intelligent agent applications are focused on operational functions, such as knowledge Q&A and office assistance, with expectations of transitioning to agile practice within 1-2 years [16]. - Financial institutions are primarily embedding intelligent agent functionalities into existing systems, which allows for quick adaptation but may limit functionality expansion [18]. Group 3: Project Implementation and Challenges - By 2025, most projects are expected to follow established plans, with a focus on exploring feasible paths for intelligent agents in financial operations [19]. - Approximately 20%-25% of projects may face underperformance or failure risks, influenced by factors such as product capabilities and real-world complexities [22]. - The banking sector leads the market for financial intelligent agents, accounting for 43% of projects, followed by asset management at 27% and insurance at 15% [25][26]. Group 4: Market Size and Growth - The investment scale for intelligent agent platforms and applications in Chinese financial institutions is projected to reach 950 million yuan in 2025, with an expected compound annual growth rate of 82.6% by 2030 [35]. - The market growth is supported by both existing project expansions and new entrants, driven by policy incentives and successful case studies from leading institutions [36]. Group 5: Customer Expectations and Investment Willingness - Financial institutions are increasingly viewing intelligent agents as core drivers for sustainable growth and customer experience innovation, rather than merely tools for efficiency [53][56]. - Investment willingness among financial institutions has risen significantly, with a 27.5% increase in those expressing a positive outlook, driven by peer examples and supportive policies [58]. - Institutions are categorized into three types based on their investment strategies: proactive explorers, pragmatic followers, and cautious observers, reflecting varying levels of resource allocation and risk tolerance [64]. Group 6: Safety and Compliance - Safety and compliance are paramount for financial institutions when adopting intelligent agents, with a strong consensus on the need for secure operational frameworks [71]. - Key concerns include ensuring the reliability of intelligent agent operations, protecting sensitive data, and maintaining regulatory compliance [72]. Group 7: Value Assessment and Practical Implementation - The definition and measurement of value have become critical decision-making factors for financial institutions in adopting intelligent agents, focusing on maximizing value through appropriate scenario selection [73]. - Successful implementation of intelligent agents requires a balance of safety, usability, and a deep understanding of financial business logic [76].

Core Viewpoint - The article discusses the evolution and application of financial AI agents, emphasizing their potential to transform the financial industry by enhancing efficiency and accuracy in various tasks, particularly in high-value, technology-intensive areas rather than low-value, labor-intensive sectors [4][5][9]. Group 1: Evolution of AI Technology - Recent advancements in AI technology can be categorized into three main areas: transitioning from unimodal to multimodal capabilities, evolving from AI assistants to AI agents, and reducing energy consumption through innovative algorithms [5][6]. - The latest AI models can process and generate various types of unstructured data, including text, audio, video, images, and code, thus expanding their applicability across different tasks [5]. - AI agents, particularly financial agents, are designed to perform complex tasks in various scenarios, potentially surpassing traditional productivity levels [5]. Group 2: Application Environment of Financial AI Agents - Financial AI agents are being deployed across banking, insurance, securities, funds, and wealth management sectors, gradually replacing human roles, especially in knowledge-intensive positions [7][9]. - For instance, Baidu's digital credit manager can draft due diligence reports in one hour with over 98% accuracy, significantly reducing the time required for such tasks [9]. - The integration of AI in financial advisory roles could lead to a potential replacement of over 60% of investment advisor positions, indicating a shift in the human resource structure within the financial industry [9]. Group 3: Reliability and Economic Viability - The deployment of financial AI agents necessitates advanced security technologies to mitigate risks such as data poisoning and algorithmic biases, ensuring the integrity and reliability of financial transactions [11][12]. - High reliability, interpretability, and economic efficiency are crucial for the successful implementation of financial AI agents, which must be trusted by clients, markets, and regulators [12]. - The focus should be on creating trustworthy AI models that can handle market analysis, customer segmentation, and investment advisory tasks with minimal errors [12]. Group 4: Data Quality and Sharing - The financial sector is data-intensive, and the current data-sharing environment faces challenges such as administrative fragmentation and insufficient circulation of non-public data [14][15]. - To enhance data quality and availability, there is a need for public data to be shared more openly and for private data to be utilized in a market-oriented manner, ensuring privacy and security [15][16]. - Establishing a comprehensive financial database that integrates various data types and sources is essential for the effective functioning of financial AI agents [16].

Core Insights - The report by iResearch indicates that 2025 will be a pivotal year for the development of financial intelligent agents, although the industry is still in its exploratory phase, with 96% of applications in proof of concept and pilot stages, and only 4% in agile practice [1][2] - The market for financial intelligent agent platforms and application solutions is projected to reach 950 million yuan in 2025, with an expected surge to 19.3 billion yuan by 2030, reflecting a compound annual growth rate of 82.6% from 2025 to 2030 [1][3] Market Distribution - The banking sector leads with 43% of project numbers, benefiting from diverse business scenarios and high-frequency interactions, while asset management institutions account for 27%, and the insurance industry represents 15% [2] - Internet finance companies and other institutions each hold 7% of the market share, with the former focusing on smart marketing and risk control, and the latter exploring niche applications in areas like financing leasing [2] Project Viability and Risks - Despite the increase in project numbers, there is a significant gap between pilot projects and effective implementation, with an estimated 20%-25% of projects likely to underperform or fail due to inadequate product capabilities, cost mismanagement, and environmental constraints [4] - The report identifies two project types: embedded intelligent agent functions (52.9%) and independent intelligent agent applications (47.1%), with successful vendors demonstrating a deep understanding of financial business logic and providing secure technology frameworks [7] Competitive Landscape - The market features a diverse competitive landscape with various types of vendors, including cloud providers and specialized technology firms, evaluated based on their competitive strength and market performance [4] - Leading firms such as Alibaba Cloud, Baidu Smart Cloud, and Tencent Cloud are positioned as comprehensive leaders, while others like iFlytek and Zhongguancun KJ focus on specific technical areas as core competitors [4][5] Future Outlook - The success of financial intelligent agents will depend on the ability to transition from being merely usable to becoming indispensable, requiring vendors to evolve from technology suppliers to business co-creation partners [8] - The next few years will witness a "survival of the fittest" scenario, where only those firms that truly understand finance and can consistently deliver value will remain in the market [8]

Core Insights - iResearch has positioned Ant Group's Ant Financial Technology as a comprehensive leader in the financial AI sector, recognizing its technological leadership and ability to implement solutions in real-world scenarios [1][3] Industry Overview - Current policies focus on a "technology-industry-finance" cycle, with predictions indicating that the fintech market will exceed 650 billion yuan by 2028 [3] - Financial AI has become a core support for institutional digital transformation, with Ant Financial's competitive edge stemming from its "financial-native" foundation serving over a billion users [3] Technological Innovation - Ant Financial's innovative "Four Workshops" engineering architecture (intention, planning, execution, expression) dissects the complex AI construction process into traceable and explainable engineering workflows, addressing the "black box decision-making" issue in traditional AI applications [3][4] - The self-developed financial reasoning model, Agentar-Fin-R1, with versions containing 32 billion and 8 billion parameters, has achieved top performance in three key financial benchmark tests, surpassing several mainstream open-source models [3] Business Model Innovation - Ant Financial demonstrates industry foresight by exploring the Results as a Service (RaaS) model, which allows financial institutions to reduce upfront investments and share risks and rewards through a "pay-for-performance" value delivery model [4] - Successful implementations, such as the AI-native mobile banking solution for Shanghai Bank, have significantly improved customer satisfaction and business conversion rates by 10% [4] - The intelligent decision-making system co-developed with Ningbo Bank has increased the accuracy of complex problem responses from 68% to 91%, with response times reduced to milliseconds [4] Future Projections - iResearch forecasts that by the end of 2028, 80% of financial institutions will adopt at least one AI tool, with over 35% of financial AI applications achieving scalable implementation, marking the industry's transition into a phase of expansion [4]

Core Insights - The report by iResearch highlights Ant Group's technology leadership and practical application capabilities in the financial AI sector, placing it in the comprehensive leader quadrant [1] Industry Summary - iResearch predicts that by the end of 2028, 80% of financial institutions will adopt at least one AI tool [1] - More than 35% of financial AI applications are expected to reach a scalable implementation phase during this period, indicating the industry's transition into a phase of scale expansion [1]

Core Insights - The "14th Five-Year Plan" emphasizes the implementation of the "AI+" initiative, aiming to integrate artificial intelligence with various sectors, including finance, to enhance productivity and innovation [1] - Financial technology is evolving from mere application to ecosystem reconstruction, driven by policy guidance, technological advancements, and market demand [1] - The integration of technology and finance is expected to lead to high-quality development in financial technology, focusing on technical empowerment, scenario deepening, and ecological collaboration [1] Group 1: AI Applications in Finance - Artificial intelligence is showing vast application potential in the financial sector, particularly in areas such as intelligent risk control, customer service, and investment research [1] - Beijing is positioning itself as a hub for financial technology, leveraging its digital technology and financial resource advantages to build a vibrant modern financial system [2] - Financial institutions in Beijing are actively incorporating AI into over 300 scenarios, enhancing efficiency and transitioning from passive to proactive service models [2] Group 2: Development of Financial Intelligent Agents - The shift from large models to intelligent agents is seen as a necessary evolution for AI to transform into productive forces within the financial sector [2] - Intelligent agents are reshaping workflows, service models, organizational structures, and value chains in the financial industry [2] - The development of financial intelligent agents requires a deep understanding of each scenario's needs and pain points, focusing on demand assessment, value analysis, and ecosystem construction [3] Group 3: Challenges in Financial Technology - Financial technology faces significant challenges, including technical risks, regulatory balance, the digital divide, and international competition [5] - Technical risks are particularly concerning, with issues such as algorithmic opacity, data forgery, and systemic security risks arising from AI becoming a core engine [6] - The industry must enhance technical safety research and improve risk prevention capabilities to address emerging risks from advanced technologies like quantum computing and large models [6]

Core Insights - The integration of AI in the financial sector is enhancing operational efficiency and service quality, with AI's accuracy in risk control audits reaching 90% [1][8] - The 2025 Financial Street Forum highlighted the transition of AI from a technological application to a value-creating tool in finance, sparking discussions among experts [1][2] AI in Financial Services - AI is driving the intelligent upgrade of traditional insurance processes, improving pricing accuracy and risk prevention, thus addressing the shortcomings of conventional insurance models [1][2] - The penetration rate of large models in the financial sector is currently at 35%, with a focus on understanding specific scene demands and pain points for effective implementation [2] Data Governance and Collaboration - Emphasis on enhancing data governance through better data integration, quality improvement, and risk prevention is crucial for the development of digital insurance [2] - Collaboration between insurance institutions and academic research organizations is necessary to cultivate interdisciplinary talent for digital insurance [2] Financial Institutions' Practices - The financial support for technological innovation is increasing, but challenges remain, such as the reliance on indirect financing and mismatches in risk control for tech enterprises [3] - Asset management institutions are encouraged to focus on human-centered approaches to discover new asset values and optimize investor demand profiles [3] AI's Role in Banking - AI is becoming essential for city commercial banks to navigate challenges like narrowing net interest margins and intensified competition, transitioning from a cost center to a core service and value creation tool [4][5] - Different financial institutions are advised to adopt AI evolution paths suited to their capabilities, with regional banks encouraged to start with practical applications [5] Regional Financial Cooperation - The digital financial landscape among Shanghai Cooperation Organization (SCO) countries presents opportunities for collaboration despite existing disparities in digital finance levels [5] - Beijing is positioned to lead in areas such as digital currency, cross-border settlement, and data security, leveraging its technological and policy advantages [5][6] AI and Risk Management - Experts agree that AI is transforming financial business models, necessitating the establishment of matching risk governance systems [7] - The challenges posed by AI, including algorithmic opacity and data integrity, require a focus on human-machine collaboration and clear accountability in decision-making [7][8]

Core Insights - The financial sector is identified as the most valuable testing ground for artificial intelligence (AI) technology, with a significant evolution from auxiliary roles to core decision-making processes [1] - The Chinese government has initiated actions to promote the application of AI across various sectors, including finance, emphasizing the integration of advanced technologies like big data and blockchain to enhance risk control and product design [1][2] - Platform companies are transitioning from single technology outputs to collaborative ecosystems, significantly lowering barriers between financial institutions and tech companies [2][3] Group 1 - Financial intelligent agents are evolving to autonomously handle complex processes such as data filtering and risk assessment, addressing the challenges of deploying large models in practical scenarios [3][4] - The collaboration between tech platforms and financial institutions is fostering a new governance model that emphasizes shared risks and values, focusing on key issues like financial data security and inclusive finance [3] - Companies like JD Technology and Tencent Cloud are leveraging AI to improve credit assessment models and enhance customer service through intelligent platforms [2] Group 2 - The core value of intelligent agents lies in their ability to replace certain human functions, transforming the application logic of AI from simple assistance to more complex, human-like workflows [4] - The future of AI applications in finance is expected to show a trend of deeper specialization and collaborative coexistence, with platform companies enhancing their technical capabilities and vertical service providers focusing on specific financial sectors [4] - The integration of AI technology with specific banking operations is seen as a way to extract actual business value, with some companies aiming to create generalized intelligent agent platforms for broader industry applications [4]