金融智能体行业丨研究报告 序 - 背景 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 ...

当前，人工智能正以前所未有的广度与深度重塑银行业发展格局。 近日，一场聚焦"智能体重塑银行业：探索与挑战"的闭门研讨会在上海举办。与会者围绕智能体（AI Agent）在银行业的落地路径、现存挑战与未来前景，展开深度交流，勾勒出一幅"人机共生"的金融新 图景。 智能体迈入"人机协作"阶段 2025 年，国务院印发《关于深入实施"人工智能+"行动的意见》，明确要求推动大模型等前沿技术与实 体经济深度融合；中国人民银行亦明确提出"安全稳妥有序推进人工智能大模型等在金融领域应用"，为 行业技术创新划定方向。 金融智能体作为人工智能与金融业深度融合的核心载体，正加速落地渗透，为金融业开启"人机共生"的 全新发展生态。中国银行业协会原首席信息官高峰介绍，行业已形成L1—L5五级智能体分级标准，目 前行业普遍处于L3级人机协作基础阶段，即机器在人类设定目标后，可独立处理客户服务、财富管理 等业务。 未来，金融智能体的发展将摒弃形式化部署，以实际业务价值为核心衡量标尺，并坚守"人机融合"，在 关键环节保留人工干预。浦发银行科技发展部副总经理周骏认为，2025年作为"大模型元年"，AI已从聊 天机器人迈向智能体阶段，正转向商业 ...

五位AI企业负责人直面核心矛盾。 在AI加速渗透各行各业的今天，AI Agent已从炙手可热的概念，迈向价值验证的关键十字路口。它究竟是企业降本增效的利器，还是驱动增长的新引擎？ 在落地过程中，又面临哪些信任、成本与习惯的深层阻力？ 12月3日下午，在2025甲子引力年终盛典中，甲子光年智库院长宋涛作为主持人，对话卓世科技创始合伙人&COO李伟伟、红熊AICEO温德亮、深度原理 COO张露阳、蚂蚁数科AI原生科技总经理王磊、零一万物企业解决方案技术负责人王猛，围绕《智能体社会：人与AI共创的产业秩序》这一主题展开了 深入探讨。 这些观点共同揭示了一个趋势：AI Agent的价值正从技术能力转向真实的商业产出——它必须深入业务，解决问题，并交出可衡量的价值答卷。 以下为本场圆桌的文字实录，经「甲子光年」编辑，在不改变原意的基础上略有删改。 宋涛（主持人）：感谢各位嘉宾的到来，先请各位嘉宾简单的用一句话做自我介绍，介绍一下个人和你们的公司。 王猛（零一万物企业解决方案技术负责人）：我在大模型独角兽公司零一万物在负责打造万智企业大模型一站式平台，这是一款端到端的平台型产品，覆 盖从模型训练、工具链构建到应用开发的全 ...

Core Viewpoint - The report provides an in-depth insight into the current status and trends of financial intelligent agents in China, emphasizing their performance in key cyclical stages and aiming to offer valuable reference content for the industry [1]. Group 1: Driving Factors for Development - The development of financial intelligent agents is driven by three main factors: technological breakthroughs, business innovation, and policy support, showcasing a stronger momentum compared to other emerging technologies [3]. - Technological advancements have improved the execution capabilities of intelligent agents, addressing the "last mile" challenges in practical applications [6]. - Approximately 33% of financial institutions exhibit a positive investment attitude towards intelligent agents, reflecting market recognition of their practical value [7]. - Policy frameworks provide clear guidance and target planning for the application and development of intelligent agents in finance, leading to adjusted technology investment priorities [9]. Group 2: Current Application and Commercial Practice - As of now, 96% of application practices are in the initial exploration phase, with most projects focused on proof of concept (POC), platform deployment, and pilot operations [12]. - Intelligent agents are primarily being explored in peripheral financial business scenarios and operational functions, with a focus on knowledge Q&A and office assistance [17]. - The deployment of intelligent agents follows two main paths: embedding functionalities into existing systems or developing independent intelligent agent applications [21]. Group 3: Project Implementation and Market Distribution - By 2025, most projects are expected to progress according to established plans, with a significant portion of projects still in the delivery phase [21]. - The banking sector accounts for 43% of the financial intelligent agent market, followed by asset management at 27% and insurance at 15% [26][27]. - The majority of intelligent agent application projects are concentrated in the range of 300,000 to 1.5 million yuan, reflecting a cautious investment strategy among financial institutions [35]. Group 4: Market Size and Business Models - The investment scale for intelligent agent platforms and application solutions in Chinese financial institutions is projected to reach 950 million yuan by 2025, with an expected compound annual growth rate of 82.6% until 2030 [39]. - The market growth is supported by both predictable growth from existing projects and potential growth driven by policy support and successful practices from leading institutions [40][41]. - Two primary business models are identified: product delivery, which is straightforward but prone to homogenization, and value delivery, which is more complex but offers significant market potential [44]. Group 5: Industry Challenges and Client Expectations - The current industry cycle is characterized by high market expectations versus the reality of exploration phase outcomes, necessitating a focus on project quality to maintain client trust [48]. - Financial institutions are increasingly viewing intelligent agents as core innovation engines for sustainable business growth rather than merely tools for efficiency [57]. - There is a notable shift in investment willingness among financial institutions, with a 27.5% increase in those expressing a positive investment attitude, driven by peer examples and policy guidance [65]. Group 6: Safety, Compliance, and Value Assessment - Safety and compliance are paramount for financial institutions when adopting intelligent agents, with a strong consensus on the need for secure operational frameworks [77]. - The definition and measurement of value have become critical decision-making anchors for financial institutions, influencing their adoption of intelligent agents [80]. - Institutions are encouraged to establish strategic offices to ensure the systematic application of intelligent agents and continuous value feedback [89].

Core Insights - The report by iResearch highlights 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] - The financial technology market is projected to exceed 650 billion yuan by 2028, with financial AI becoming a core support for institutional digital transformation [1] - Ant Financial Technology's competitive edge stems from its integration of deep financial business understanding with enterprise-level AI engineering capabilities, allowing it to differentiate itself in key areas such as AI-native apps, wealth management, credit risk control, and intelligent marketing [1] Industry Trends - The report indicates that by the end of 2028, it is expected that 80% of financial institutions will adopt at least one AI tool, with over 35% of financial AI applications achieving scalable implementation [1] - Ant Financial Technology is one of the few firms capable and willing to explore the Results as a Service (RaaS) model in core financial scenarios, which focuses on a pay-for-performance value delivery model [1]

Core Insights - The "2025 Guangming Science City Forum: Greater Bay Area Intelligent Computing Power and Large Model Intelligent Agents Forum" was held in Shenzhen, focusing on intelligent computing infrastructure, large model technology innovation, and multi-modal intelligent agent applications [1][3] Group 1: Forum Highlights - The forum gathered experts and scholars from artificial intelligence, high-performance computing, and multi-modal intelligent agents to discuss cutting-edge topics [1] - The director of Pengcheng Laboratory, Gao Wen, highlighted the progress of the "Pengcheng Cloud Brain III" large scientific device, which aims to accelerate scientific innovation and industrial technology upgrades [1][3] Group 2: Industry Development - Guangming District has attracted nearly 100 high-quality AI enterprises, with an industry scale exceeding 30 billion [3] - The forum aims to deepen the integration of industry, academia, and research to promote innovation in intelligent computing, large models, and intelligent agent technologies [3] Group 3: Technological Innovations - The forum announced several technological advancements, including the open-source model "Pengcheng Brain 2.1" and the AI forecaster assistant "Afu," developed in collaboration with the Shenzhen Meteorological Bureau [5] - Other innovations included the domestic "FenixCOS" inference engine and a financial intelligent agent based on a domestic full lifecycle model toolkit [5] Group 4: Collaborations and Partnerships - Cooperation agreements were signed between Pengcheng Laboratory and various institutions, including the Shenzhen Meteorological Bureau and the National Supercomputing Center in Wuxi [7] - Prominent academics from Tsinghua University, Hong Kong University, and other institutions delivered keynote speeches at the forum [7]

Group 1 - The company has developed over 20 financial intelligent agents in various fields such as large credit, marketing, testing, and risk within the vertical large model domain [2] - The company possesses a model development and management platform that addresses issues related to the opacity of traditional black-box model decision-making processes, regulatory non-compliance, and business distrust [2] - The platform offers explainable, intervenable, and simplified model development and strategy mining capabilities, which have gained recognition from overseas financial industry clients [2]

Group 1 - The article emphasizes the dual drivers of policy promotion and market demand in the financial sector's adoption of AI innovations, with over 80% of financial institution leaders showing high interest in intelligent agents [2] - Approximately 65% of financial IT leaders believe that intelligent agents have significantly surpassed the capabilities of process automation robots and virtual assistants, enabling them to handle complex tasks more efficiently [2] - About 63% of financial institution respondents express interest in the value creation potential of intelligent agents in financial services, rather than viewing them merely as efficiency tools [2] Group 2 - The report titled "Research on the Development of Financial Intelligent Agents in China and Vendor Evaluation Report (2025)" aims to provide a comprehensive understanding of market development through systematic research and vendor assessment [3] - The report will utilize numerous case studies and empirical data, along with corporate research and expert interviews, to conduct its analysis [4] - The report is divided into two parts: the first part analyzes the current status and trends of financial intelligent agents in China across various dimensions, while the second part evaluates vendors based on customer feedback and market competitiveness [5] Group 3 - Inclusion in the "iResearch Vendor Insight: Competitiveness Quadrant of Financial Intelligent Agents in China (2025)" can enhance a vendor's brand recognition and industry influence [7] - Analysts will regularly engage in technical exchanges with financial institutions within the iResearch ecosystem, prioritizing recommendations for vendors included in the quadrant [8] - The report will be published on the iResearch official website and WeChat account, along with dissemination through various media channels linked to iResearch [9]

Group 1 - The core viewpoint of the article emphasizes the dual drive of policy promotion and market demand, leading financial institutions to actively embrace AI innovations for business value growth [2] - Over 80% of financial institution leaders show high concern for intelligent agents, with about 65% of financial IT leaders indicating that intelligent agents have significantly surpassed the capabilities of process automation robots and virtual assistants, enabling them to handle complex tasks more efficiently [2] - Approximately 63% of financial institution respondents express interest in the value creation of intelligent agents in financial business rather than merely as efficiency tools [2] Group 2 - The report titled "Research on the Development of Financial Intelligent Agents in China and Vendor Evaluation Report (2025)" is officially launched by iResearch, aiming to provide a comprehensive understanding of market development for financial institutions and intelligent agent vendors [3] - The report will utilize numerous case studies and empirical data, along with enterprise research and expert interviews, to conduct its analysis [4] - The report is divided into two parts: the first part analyzes the current status and trends of financial intelligent agents in China across dimensions such as industry development, application practices, customer needs, and technology and product capabilities [5] Group 3 - The second part of the report will be based on research and evaluation of financial intelligent agent vendors, integrating feedback from financial institution clients to create the "iResearch Vendor Insight: Competitive Landscape of Financial Intelligent Agent Vendors (2025)" [5] - This evaluation will cover various dimensions including technology and product capabilities, strategic planning, ecosystem development, commercialization ability, and customer reputation [5] - Vendors selected for the "iResearch Vendor Insight" can enhance their brand awareness and industry influence [7]

Core Viewpoint - The core viewpoint of the report is that the key to AI application in finance is not to engage in a technology race for the sake of AI, but to return to the essence of technology serving business, using ROI as a benchmark to calibrate application paradigms and optimize implementation paths [1][4]. Group 1: Current State of AI in Finance - A productivity revolution driven by large models is quietly occurring in leading financial institutions, indicating a paradigm shift in the industry [1]. - By 2025, it is anticipated that the financial industry will deeply integrate AI and realize the benefits of large model technologies [1]. Group 2: Transformative Practices - A leading bank has reduced complex credit approval report analysis from hours or days to just 3 minutes, with accuracy improved by over 15% [3]. - A top brokerage firm has implemented AI agents to monitor over 5,000 listed companies 24/7, significantly enhancing research coverage and response speed [3]. - An overseas top investment bank has deployed hundreds of AI programmers, with plans to increase this number to thousands, aiming to boost engineer productivity by three to four times [3]. Group 3: Strategic Framework - The report aims to provide a strategic compass that is both forward-looking and actionable, emphasizing the importance of understanding opportunities and challenges, making proactive layouts, and building systematic capabilities [4][8]. - The financial industry is seen as the core battlefield for the comprehensive reconstruction driven by AI, where technology and human wisdom will collaborate to explore the essence of financial services [6][8]. Group 4: Trends and Challenges - The report identifies six core trends driving industry evolution, aiming to provide a strategic roadmap for financial decision-makers and innovators [9]. - The evolution of large models is characterized by a shift from capability exploration to efficiency revolution, with a focus on high-value data rather than just large-scale data [11]. - Financial institutions are moving from experimental phases to large-scale deployment of AI applications, with banks leading the way [12]. Group 5: Implementation Challenges - The implementation of large models in finance reflects the deepening contradictions of digital transformation, requiring institutions to balance fragmented construction, resource allocation, and compliance with safety [14][15]. - Key challenges include data fragmentation, unclear strategic planning and ROI, low tolerance for error in technology adaptation, and lagging organizational talent upgrades [15]. Group 6: Future Outlook - AI is driving financial services towards unprecedented levels of inclusivity, intelligence, and personalization, redefining operational and management models [16]. - The integration of AI with human expertise is expected to accelerate the demand for innovative financial talent, with high-quality private data becoming a core competitive advantage for institutions [16].