1. 核心结论:金融科技平台场景匹配指南 基于AI技术创新、行业积累与规模、业务范围与伙伴、研发团队实力、股东背景等核心维度的分析，以及5个金融科技平台的特点，本文将用户需求分为5 大场景，并给出精准推荐： 场景匹配速览: 本文核心价值: 如何使用本指南: 阅读建议: 2. 场景分类方法与识别 如何识别自己的场景? 金融科技平台的选择，关键在于精准匹配自身需求。本文基于以下3个核心维度对用户场景进行分类，帮助您快速定位最适合的平台： 维度1: AI技术创新偏好 - 如果您追求AI大模型自研能力和开源贡献，希望平台在AI前沿技术领域有突破性进展，并能应用于高度垂直的场景，那么您可能 属于易鑫主导的场景 (场景1)。 - 如果您寻求通用AI大模型应用，如提升通用金融文本理解、智能客服或业务流程自动化，但对特定领域自研模型非首要考 量，那么您可能属于蚂蚁集团、腾讯金融科技、度小满主导的场景 (场景2、3、5)。 - 如果您聚焦AI在供应链金融与电商场景的应用，希望通过AI赋能电商 导购、智能客服及多模态数字人等，那么您可能属于京东科技主导的场景 (场景4)。 维度2: 行业垂直度与场景需求 - 如果您的核心业务需求 ...

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 ...

0:00 四是智能体与人机协同，借鉴多智能体协同与人机协同的经验，让AI从"工具"升级为"业务伙伴"；推动 业务流程的智能化重构，实现更高效的决策与执行。 在被问及国外金融大模型实践对中国的借鉴意义时，高飞从合规安全、场景选择、技术能力、人机协同 及生态合作五个维度展开分析： 一是合规与安全优先，应将合规与安全作为大模型应用的首要前提。加强模型可解释性、数据治理与隐 私保护，构建完善的负责任AI治理框架。 二是场景驱动与渐进实施，优先选择ROI高、可控性强的场景进行试点，逐步扩展至更复杂的决策领 域，避免"一步到位"带来的技术与业务风险。 三是工程化与平台化能力建设，提升大模型的工程化与平台化水平，发展低代码开发、模型管理与自动 化运维能力；降低技术门槛，加速创新场景落地。 21世纪经济报道记者 杨坪 实习生 陈慧 深圳报道 近日，在由深交所、港交所、广期所联合举办的2025年大湾区交易所科技大会上，IDC中国区研究总监 高飞接受了21世纪经济报道等多家媒体的采访。 高飞表示："从全球金融行业大模型落地实践来看，中外实践共性多于差异。目前，全球金融行业大模 型已进入加速落地与场景扩展阶段，众多头部金融机构已在 ...

Core Viewpoint - The financial large models are transitioning towards specialization, lightweight design, and compliance, marking the beginning of a new era in intelligent finance rather than being the endpoint of quantitative trading [1][8]. Group 1: Current State of Quantitative Trading - Quantitative funds have shown relatively strong performance in both returns and risk control compared to fundamental funds, with quantitative trading accounting for over 60% of the U.S. stock market and approximately 20%-30% in the A-share market as of 2023 [4]. - The number of quantitative funds in the A-share market doubled from 2019 to 2022, making up 18% of actively managed public funds [4]. - Despite their strengths, quantitative trading faces challenges such as strategy homogeneity, poor adaptability, narrow information processing, and high R&D costs [4][6]. Group 2: Challenges in Quantitative Trading - A significant issue is the homogeneity of trading strategies, as evidenced by over 70% of quantitative long products underperforming the benchmark index during extreme market conditions in August [4]. - The adaptability of quantitative strategies is limited, particularly in market structures where only a few stocks surge while many others remain stagnant [4]. - Traditional quantitative strategies often rely on outdated financial data and indicators, leading to a lack of unique Alpha returns [4]. - The increasing number of selectable factors complicates strategy development and raises trial-and-error costs [4]. Group 3: Role of Large Models in Quantitative Trading - Large models are set to redefine quantitative trading by shifting from experience-driven to intelligence-driven paradigms, enhancing the ability to process vast amounts of unstructured data and perform logical reasoning [6][8]. - These models can automate information extraction, generate trading signals, and optimize decision-making processes, thereby improving the depth, breadth, and adaptability of trading strategies [6][7]. - The integration of multi-agent systems and multi-source information will empower the entire quantitative trading process, from data collection to risk control [6][7]. Group 4: Practical Applications and Performance - Real-world applications of large models have demonstrated their value, with Chinese models outperforming U.S. models in a recent trading competition, achieving an average of 3.4 trades per day and a single trade profit of $181.53 [8]. - The successful strategies of these models include selective trading, maximizing profits, quick loss-cutting, and patient holding of profitable positions [8]. - However, caution is advised regarding the "hallucination problem" in financial large models, which can lead to significant shifts in market sentiment and trading strategies based on minor adjustments in input [8].

Core Viewpoint - The ongoing merger between Dazhihui (601519) and Xiangcai Co. (600095) faces legal challenges as a shareholder has filed a lawsuit to annul a recent shareholder meeting resolution related to the merger [1][3][15] Group 1: Legal Proceedings - A shareholder, Wang Gongwei, has filed a lawsuit against Dazhihui, claiming that the merger with Xiangcai Co. constitutes a significant related party transaction that requires compliance with specific auditing and evaluation procedures [3][5] - Dazhihui asserts that it has followed all necessary procedures for the merger and will actively respond to the lawsuit, although the case does not currently involve specific financial amounts [5][6] Group 2: Historical Context - The merger discussions between Dazhihui and Xiangcai Co. have been ongoing for ten years, with a previous attempt in 2015 to acquire Xiangcai Securities for 8.5 billion yuan that was halted due to regulatory investigations [6][7] - Xiangcai Co. became Dazhihui's second-largest shareholder in 2020 after Xiangcai Securities went public through a reverse merger [7] Group 3: Financial Performance - Dazhihui's revenue has declined from 819 million yuan in 2021 to 771 million yuan in 2024, with a net loss of 201 million yuan in 2024 [7] - Xiangcai Co. has also faced financial difficulties, with total revenue dropping from 4.571 billion yuan in 2021 to 2.192 billion yuan in 2024, and a net profit of just over 100 million yuan in 2024 [7] Group 4: Merger Details - The merger plan involves Xiangcai Co. issuing A-shares to acquire all Dazhihui shares, with a total fundraising target of up to 8 billion yuan to support various financial technology projects and improve liquidity [13][14] - The merger aims to enhance synergies between the two companies, particularly in internationalizing their securities business [13]

Investment Rating - The report indicates a strong investment potential in the Chinese banking sector's large model deployment, with a projected annual compound growth rate of 40% from 2025 to 2028, reaching a total market size of 9.9 billion yuan by 2028 [7][21]. Core Insights - The current development of financial large models is at a critical stage, facing structural bottlenecks and systemic challenges despite high demand. Major banks like China Bank are leading the way in establishing controllable large model systems to set industry standards [5][7]. - The banking sector is becoming the main arena for the commercialization of large models, with significant growth in bidding projects and amounts, particularly in the second half of 2024 [10][21]. - Large models are fundamentally reshaping banking operations, transitioning from digital enhancement to intelligent reconstruction, focusing on smart interaction, process automation, precise risk control, and data-driven decision-making [11][14]. Summary by Sections Financial Large Model Development Status - The financial large model market in China is expected to reach 2.866 billion yuan in 2024, with a significant year-on-year growth rate. However, growth is expected to slow in the latter half of the year due to structural and systemic challenges [7][8]. Bank Large Model Bidding Situation - In 2024, the banking sector completed 133 bidding projects with a total amount exceeding 200 million yuan, indicating a shift towards systematic expansion led by business lines [10][21]. Main Application Scenarios - Large models are being applied in various scenarios, including intelligent customer service, business process optimization, risk management, marketing, data management, and decision support, significantly enhancing operational efficiency and customer experience [11][12]. Application Implementation Effects - The implementation of large models has led to substantial improvements, such as a 30% reduction in response time for intelligent customer service and a 200% increase in compliance check efficiency [13][14]. Optimization Path Analysis - DeepSeek offers a framework for banks to build a low-cost, high-efficiency, and compliant operational system, addressing challenges related to computational resources and operational efficiency [15][16]. Development Opportunities - The transition to large models represents not just a technological upgrade but a critical path for organizational capability enhancement and customer relationship restructuring, positioning banks to seize the future of "model-native banking" [21].

Core Viewpoint - The appointment of a Chief Information Officer (CIO) at Qinghai Datong Rural Commercial Bank signifies a strategic move towards digital transformation, particularly in the context of rapid advancements in financial technology. This role is crucial for middle-sized banks to navigate the challenges of digitalization and compete effectively in the market [1][4]. Group 1: Appointment of CIOs - Liu Shouzhu's qualification as CIO of Qinghai Datong Rural Commercial Bank has been approved by local regulatory authorities, marking a rare instance of a county-level rural commercial bank appointing a CIO [1][4]. - Over ten regional banks have had their CIO appointments approved this year, predominantly among city and rural commercial banks, indicating a trend towards enhancing digital leadership within these institutions [4][5]. Group 2: Characteristics and Trends - Many of the newly appointed CIOs in regional banks are either internally promoted or hold dual roles, reflecting a common practice in the industry [5]. - There is a growing trend of external recruitment for CIO positions in smaller banks, as seen with Zhengzhou Bank's recent announcement for a CIO position, emphasizing the need for candidates with extensive banking and technology project experience [6][7]. Group 3: Role and Importance of CIOs - The role of the CIO is evolving, requiring a blend of business acumen and technical expertise, as well as significant influence within the bank's management structure [8][10]. - The rapid development of artificial intelligence is reshaping the expectations of CIOs, with larger banks investing heavily in AI technologies, while smaller banks are still in the process of digital transformation [9][10]. - The importance of the CIO is expected to increase, as they are seen as pivotal in aligning technology initiatives with business strategies to drive digital transformation [11].

Core Viewpoint - The merger between Xiangcai Co. and Dazhihui is progressing rapidly, with key transaction details finalized, marking a significant step towards shareholder and regulatory approval [2][4][20]. Group 1: Merger Details - Xiangcai Co. plans to absorb Dazhihui through a share swap, with Xiangcai's A-share swap price set at 7.51 CNY per share and Dazhihui's at 9.53 CNY per share [3][9]. - Post-merger, Xiangcai's total share capital is expected to increase to 5.141 billion shares, while Dazhihui will cease to be listed [3][9]. - The merger includes a financing plan to raise up to 8 billion CNY, targeting specific investors for funding towards financial technology and other strategic areas [11][12]. Group 2: Financial Projections - The merger is projected to enhance Xiangcai's financial metrics, with total assets expected to rise from approximately 4.13 billion CNY to 5.91 billion CNY post-merger [18]. - The total revenue is anticipated to increase from 11.44 billion CNY to 15.11 billion CNY in the first half of 2025 [18]. - However, profit metrics may experience short-term fluctuations, with potential losses projected for the fiscal year 2024 [17][18]. Group 3: Market Reaction and Historical Context - The announcement has sparked significant market interest, with Xiangcai's stock rising over 84% since the merger announcement in March 2025 [4][20]. - The merger represents a culmination of a decade-long relationship between the two companies, with previous attempts at acquisition and collaboration [21][22]. - This merger positions Xiangcai to become the third internet brokerage in A-shares, following similar paths taken by companies like Dongfang Caifu [6][24][26]. Group 4: Strategic Implications - The merger aims to create a synergistic effect by combining traditional brokerage services with advanced financial technology, enhancing overall service capabilities [16][29]. - Xiangcai's collaboration with Dazhihui is expected to leverage Dazhihui's extensive user base and technological expertise to improve customer acquisition and service delivery [29][30]. - The combined entity is anticipated to face challenges in replicating the success of established players like Dongfang Caifu due to differences in market conditions and operational backgrounds [30].

Group 1 - The 2025 China International Service Trade Fair (CIFTIS) focuses on financial services with the theme "Digital Intelligence Drives Open Win-Win," highlighting the integration of artificial intelligence (AI) into core financial scenarios [1] - Major banks showcased advanced technologies, including ICBC's trillion-level financial model and Agricultural Bank's virtual reality services, aiming to enhance customer experience and shift financial services from passive to proactive [2][3] - The application of AI in finance is seen as a significant opportunity, with industry leaders emphasizing its role in improving efficiency, reducing operational costs, and enhancing sustainable development within the banking sector [3] Group 2 - The collaboration between Beijing Rural Commercial Bank and Beijing Data Group aims to explore application scenarios and innovate data products, promoting the deep integration of data elements with financial services [5] - AI technologies are being applied across various core business scenarios in finance, including risk control, wealth management, and intelligent trading, indicating a shift towards a more efficient and inclusive financial ecosystem [2][3]

Core Viewpoint - Ant Group's subsidiary, Ant Digital, aims to differentiate itself in the market by focusing on enterprise-level AI services and Web3 solutions, leveraging its extensive technological capabilities and industry experience to create value for businesses [4][11]. Group 1: Company Overview - Ant Digital officially became independent in March 2024 and has shown promising results, achieving breakeven last year and projecting a 50% revenue growth this year [4]. - The company has a strong foundation in toB services, benefiting from years of experience within Ant Group, which has a transaction volume exceeding one trillion [4][5]. - Ant Digital's CEO, Zhao Wenbiao, emphasizes the importance of AI and Web3 as transformative opportunities, comparable to the impact of mobile payments [4][6]. Group 2: AI Strategy - Ant Digital focuses on "application landing" rather than competing in foundational large models, offering a full-stack AI service that includes intelligent computing power scheduling, industry-specific large models, and intelligent agent development platforms [4][5]. - The company has introduced a performance-based payment model, allowing businesses to pay based on the actual business effects generated by AI, marking a significant shift from traditional project-based or subscription models [5][6]. - Zhao believes that the current AI landscape is filled with projects that address isolated issues, resulting in limited business value, and highlights Ant Digital's ability to provide end-to-end solutions [5][6]. Group 3: Web3 Focus - Ant Digital views Real World Assets (RWA) as a key breakthrough in the Web3 space, having completed its first RWA transaction in Hong Kong and expanding into various asset categories [6][8]. - The company has developed a comprehensive technical infrastructure for asset security, trustworthy on-chain processes, cross-chain circulation, and issuance, which lowers industry barriers and accelerates scalability [6][8]. - Ant Digital has over 6,000 blockchain patents and aims to leverage its blockchain technology to create a robust ecosystem, anticipating significant industry growth [6][8]. Group 4: Market Positioning - Ant Digital distinguishes itself from other fintech companies by focusing solely on technology services for enterprise digitalization, rather than providing financial services [10][11]. - The company has established a strong presence in the financial sector, serving 100% of state-owned banks and over 60% of local commercial banks, while also expanding into the renewable energy sector [13][19]. - Zhao emphasizes the importance of technology innovation, value creation for industries, and a cautious approach to innovation, avoiding speculative ventures that do not generate real value [12][14]. Group 5: Future Outlook - Ant Digital is positioned for significant growth in the next three to five years, with a focus on long-term investment in the enterprise service market [29]. - The company aims to redefine industry standards and practices in both AI and Web3, seeking to lead rather than follow in these transformative areas [6][21]. - Zhao expresses a commitment to becoming a respected technology company that drives industry advancement, with a focus on creating competitive products and services [26][29].