余滨表示："AI已从'试试看'走向'必须做'，正从成本中心转变为服务、营销与价值创造的核心。"目 前，蚂蚁数科为银行提供的AI应用已从早期的智能客服、知识问答等单点场景，扩展至覆盖营销、风 控、投顾、理赔等核心业务的全栈智能体系统。据试点银行反馈，借助理财经理数字分身，人均服务客 户数从200人提升至2000人，有效覆盖了原本难以触达的中长尾客户，带动收入增长约20%。 10月30日，在2025金融街论坛上，蚂蚁数科AI业务总裁、蚂蚁集团副总裁余滨发表演讲时透露，当前 不少城商行正积极拥抱金融大模型与智能体技术，寻求业绩突围。蚂蚁数科致力于支持银行构建自主可 控的金融大模型，江浙沪地区的部分城商行在员工工作效率、营销转化率、营收规模等方面取得显著增 长。 伴随净息差收窄、营收增速放缓以及同质化竞争加剧，城商行面临前所未有的挑战。原本的区域优势减 弱，大型银行持续挤压其生存空间。在此背景下，越来越多的银行将AI视为"弯道超车"的关键路径。 浙江一家头部城商行则与蚂蚁数科合作，系统性构建了"算力-平台-模型-应用"全栈AI能力，落地30多个 智能体场景，覆盖客户服务与内部运营两端，为未来十年的AI转型奠定坚实基础 ...

北京商报讯(记者刘四红)10月30日，在2025金融街论坛上，蚂蚁数科AI业务总裁、蚂蚁集团副总裁余滨 发表演讲时透露，当前不少城商行正积极拥抱金融大模型与智能体技术，寻求业绩突围。蚂蚁数科致力 于支持银行构建自主可控的金融大模型，江浙沪地区的部分城商行在员工工作效率、营销转化率、营收 规模等方面取得显著增长。 据了解，蚂蚁数科的金融数智化服务已覆盖100%的国有股份制银行、超60%的地方性商业银行及数百 家金融机构。 以上海银行（601229）为例，蚂蚁数科助力其打造的AI手机银行，以"对话即服务"为核心，用户通过自 然语言交互即可办理转账、还款、理财咨询、养老金查询等十余项高频业务。用户不用在复杂菜单中翻 找功能入口，操作门槛低了，老年客户满意度更高了，业务转化率提升了10%。 浙江一家头部城商行则与蚂蚁数科合作，系统性构建了"算力-平台-模型-应用"全栈AI能力，落地30多个 智能体场景，覆盖客户服务与内部运营两端。 余滨强调，不同金融机构的资源禀赋与发展阶段各异，应选择适合自身的AI演进路径。预算有限的区 域性银行，可以选择先从场景应用入手，按效果付费，逐步加大投入；也有很多银行优先将原有的手机 Ap ...

Core Insights - Ant Group's AI business is actively supporting city commercial banks in adopting financial large models and intelligent agent technologies to enhance performance amidst challenges like narrowing net interest margins and intensified competition [1][2] - The shift from AI being a trial to a necessity is emphasized, with AI transforming from a cost center to a core element of service, marketing, and value creation [1] Group 1: AI Adoption in Banking - Many city commercial banks are leveraging AI as a key strategy for performance improvement, with significant growth in employee efficiency, marketing conversion rates, and revenue scale reported in regions like Jiangsu, Zhejiang, and Shanghai [1] - The implementation of AI applications has expanded from basic functions like intelligent customer service to comprehensive systems covering marketing, risk control, investment advisory, and claims processing [1] Group 2: Case Studies and Results - For instance, Shanghai Bank's AI mobile banking service allows users to perform over ten high-frequency tasks through natural language interaction, resulting in a 10% increase in business conversion rates and higher satisfaction among elderly customers [2] - A leading city commercial bank in Zhejiang has developed a full-stack AI capability with over 30 intelligent agent scenarios, enhancing both customer service and internal operations [2] Group 3: Strategic Approaches to AI Implementation - Different financial institutions are advised to choose AI evolution paths that suit their resources and development stages, with options ranging from starting with application scenarios to upgrading existing mobile apps into AI-driven platforms [2] - Some banks are establishing dedicated teams to create comprehensive knowledge bases and datasets, utilizing Ant Group's financial reasoning models to build autonomous "financial brains" for end-to-end business process reengineering [2] Group 4: Market Coverage - Ant Group's financial digitalization services have reached 100% of state-owned joint-stock banks, over 60% of local commercial banks, and hundreds of financial institutions [2]

"我们观察到多家银行的AI业务已从'试试看'转为'必须做'，其整体战略布局已被重构。远程银行不仅是 银行数字化转型成果的集中展现，更是其关键输出端口。它不再是成本中心，而是新的服务核心、营销 中心和价值创造中心。"蚂蚁数科副总裁余滨在接受《证券日报》记者采访时分享了他的见解。 从业者的直观感受，正是当前银行业数字化转型深入推进的真实写照。在数智驱动下，金融服务提质升 级，有力推动了银行跑出金融为民的"加速度"。作为数字化转型的"桥头堡"，远程银行由信用卡中心、 电话银行中心、网络银行部等传统部门整合而成，形成独立的"远程银行部"或"线上客户经营中心"，并 提升至与线下网点同等重要的战略层级，成为银行全面数字化转型的重要支点。 随着"数字中国"建设及做好"数字金融"大文章的深入推进，以创新为核心的新质生产力正迅速崛起，成 为推动金融高质量发展的核心动力。在新形势下，银行与用户的关系正在重塑，服务渠道与工具也在不 断更新迭代，一幅"新金融"的蓝图正徐徐展开。 从功能叠加 走向业务重构 余滨长期深耕在业务一线，致力于服务机构的远程银行建设。他向记者讲述："如今，我们为银行提供 的AI应用已从最初的智能客服、知识问答 ...

一笔百万元级别的国际货款，从上海汇出，抵达美国账户只用了7秒，手续费仅1元。 上海这座金融城市，是否真的有机会在未来十年内，挑战纽约和伦敦融中心长达百年的地位？ 纽约和伦敦的地位，是建立在数十年甚至上百年形成的资本制度，后来者极难逾越。 但竞争的赛道本身正在被重构，上海的目标并非在旧跑道上追赶，而是要利用"金融科技"这条新赛道，改变 游戏规则。 传统金融中心的强大之处，在于其规则的确定性。全球资本之所以流向纽约华尔街和伦敦金融城，是因为那 里有最完备的法律体系、最丰富的金融产品。 但上海的打法完全不同。早在2020年1月，上海就发布了《加快推进上海金融科技中心建设实施方案》，明 确提出要用5年时间，将上海建设成为具有全球竞争力的金融科技中心。 当老牌中心的银行还在优化其复杂的内部系统时，上海的金融机构已经开始大规模应用人工智能、区块链和 大数据，从根本上改变信用评估和服务交付的方式。 对于个人而言，金融服务正在从过去的"大锅饭"模式，进化到精准的"千人千面"。比如，上海银行与蚂蚁数 科合作推出的AI手机银行，以及浦发银行的AI财富管家，它们不再是简单地推荐几款理财产品。 "人工智能"、"区块链"，这些词汇听起 ...

Core Viewpoint - The banking industry is actively pursuing AI development, with various banks announcing projects related to AI capabilities, indicating a significant trend towards AI integration in financial services [1][4][6]. Group 1: AI Deployment Strategies - Different types of banks are forming differentiated AI development paths based on regional characteristics, customer structures, and digitalization foundations [2][5]. - State-owned banks tend to be conservative in their application of financial vertical models, focusing on foundational applications, while city commercial banks and joint-stock banks show a stronger willingness for transformative AI strategies [5]. - Current implementations show that state-owned banks are building platforms and ecosystems, while joint-stock banks emphasize scalability and systematic construction [5]. Group 2: Commonalities Across Banks - All types of banks are focused on how AI can enhance customer experience, optimize business processes, reduce operational costs, and strengthen risk control [6]. - As of August, 31% of customer service centers and remote banking have completed large model deployments within banks [6]. - The total financial technology investment by the six major state-owned banks reached 125.46 billion yuan, a year-on-year increase of 2.15% [6]. Group 3: Challenges in AI Application - The application of AI in financial institutions is primarily focused on general areas, with lower penetration in critical business areas such as marketing and risk control [7][8]. - Three core challenges hinder deeper AI application: technology maturity, professional requirements, and cost considerations [8]. - Financial institutions are currently in a phase of observing and experimenting with AI, particularly in general scenarios, while being cautious in core business areas [8]. Group 4: Technology and Market Dynamics - The integration of finance and AI is driving a dual upward spiral of "technology" and "market" [10]. - Financial institutions are feeling anxious about how to effectively utilize advanced technologies like large models, especially as peers achieve breakthroughs [10]. - The current stage is primarily driven by technology, but as banks recognize AI's value, business demands will increasingly shape technology development [10][11].

Group 1 - The core viewpoint of the article highlights the significant advancements in Shanghai Bank's AI strategy, particularly through the "AI Mobile Banking" service, which has notably improved customer satisfaction among elderly clients and increased business conversion rates by 10% [2] - Shanghai Bank's "AI Mobile Banking" was recognized with the "Financial Industry Intelligent Body" Innovation Award by IDC, standing out among 130 cases in the financial technology application innovation category [2] - The AI Mobile Banking service, powered by Ant Group's technology, focuses on "dialogue as service," allowing users to efficiently access financial services through natural conversation, thus enhancing user experience and operational efficiency [2][3] Group 2 - Ant Group provides a comprehensive suite of intelligent agent services to Shanghai Bank, including natural language interaction technology, intelligent agent development, model management, and computing power scheduling, which lowers the AI application threshold for financial institutions and boosts R&D efficiency [3] - The integration of Ant Group's practical experience in financial scenarios with Alibaba's Tongyi large model enables the development of specialized intelligent agent applications that are easy for financial institutions to implement [3] - The intelligent resource allocation improves GPU utilization and resource management capabilities, further enhancing the operational efficiency of financial institutions [3]

上海银行总行网络金融部总经理助理姜玉坤分享"AI手机银行"项目 生成式AI的发展推动了金融行业业务效率和用户体验的双重升级。上海银行AI手机银行以"对话即服务"为核心，用户通过自然对话即可高效获取金融服务。 用户无需在复杂菜单中反复查找，对着手机屏幕问一句"我这个月的养老金到账了吗"，AI助手即可快速响应并完成业务办理，目前已覆盖转账、还款、理财 咨询、养老金管理等十余项高频服务。不仅是用户界面交互创新，AI手机银行可根据用户习惯推送对应服务，如自动切换沪语模式、专属理财建议、常去 网点预约提醒等。 相比传统手机银行，上海银行AI手机银行以"服务找人"替代"人找服务"，通过上下文理解实现多轮对话，显著降低操作门槛、并满足千人千面的个性化服 务；同时依托多项适老化策略，为老年群体及残障人士提供便捷服务，成为银行数智化转型的行业样本。 近日，上海银行公布AI战略最新进展，"AI手机银行"已推动上海银行老年客户满意度显著提升，业务办理转化率提升10%。8月7日，全球知名咨询机构IDC 发布中国金融行业技术应用场景创新案例。上海银行"AI手机银行"从130个案例中脱颖而出，获得"金融行业智能体"创新奖。据悉，上海银行 ...

Core Insights - The World Artificial Intelligence Conference (WAIC) highlights the increasing practicality of AI applications in the financial sector, with a focus on digital labor and the restructuring of business interactions [1][2] - Financial intelligent agents are evolving from single-point applications to core business scenarios, such as credit decision-making, indicating a shift towards scalable applications [1][2] - The financial industry is seen as an ideal sector for AI implementation due to its high digitalization and data density, with various financial sub-industries exploring intelligent agent applications [2] Group 1: AI Applications in Finance - Financial intelligent agents are expanding in both depth and breadth, with a significant increase in the number of application scenarios and improvements in business efficiency [2] - Major banks have implemented a technology architecture combining general models, specialized models, and intelligent agents, while leading securities firms have launched multiple AI application products [2] - The insurance sector is advancing the construction of intelligent claims systems, showcasing the diverse applications of AI across financial services [3][4] Group 2: Transformation of Business Interactions - The interaction model in financial services is transforming, exemplified by Shanghai Bank's launch of an AI mobile banking app that allows users to conduct transactions through conversational interfaces [5] - This shift from traditional menu navigation to "dialogue as a service" enhances user experience and personalizes financial services, particularly benefiting older customers [5] Group 3: Challenges in AI Implementation - Despite the advancements, challenges remain, including the "hallucination" issue of large models, which can lead to inaccuracies in instruction adherence [6] - The need for high-quality data sets for training specialized models is critical, requiring significant investment and long-term commitment [6][7] - Many financial institutions lack the engineering capabilities to integrate business needs, computational power, models, data, and knowledge bases effectively [7]

Core Insights - The article highlights the significant advancements in AI applications within the financial sector, emphasizing the shift from conceptual models to practical implementations, with keywords such as "landing," "trustworthy," and "embodied" being central to the discussions at the WAIC2025 [1][2]. AI Financial Applications Breakthroughs - The Shanghai Artificial Intelligence Finance Institute (SAIF) introduced the new Smith RM financial reasoning model, which effectively addresses hallucination issues in financial credit analysis, generating a 12,000-word credit report in 30 seconds with a hallucination rate of under 0.3%, a 97% reduction from the previous generation [3][4]. - The Agricultural Bank of China's Shanghai branch reported that the new system has assisted in approving 4.7 billion yuan in technology loans over three months, reducing the average approval time from 5.7 working days to 11 minutes [3]. Innovations in Customer Interaction - The "Xiao Jiao" embodied intelligent robot from the Bank of Communications can provide various services, enhancing customer experience and offering a new approach to intelligent financial services [5]. - Ant Group showcased the "Look and Pay" smart glasses payment solution, allowing users to complete transactions through voice commands and visual recognition, streamlining the payment process [6]. Challenges in AI Implementation - Despite the breakthroughs, the implementation of AI in finance faces challenges, as discussed in the "FinAI Artificial Intelligence Financial Leaders" roundtable. Experts emphasized that AI deployment is not merely a technical or management issue but a comprehensive integration of strategy and operations [9]. - The importance of collaboration between banks and fintech companies was highlighted, with the need for banks to embrace technology to remain competitive [9]. Future Financial Landscape - The discussion on how AI will reshape the financial landscape identified banks, tech companies, and regulatory bodies as the three main driving forces of financial innovation, with computing power being the core foundation [12]. - The potential of China's computing power in the financial sector was noted, with the country ranking second globally, despite existing challenges in chip technology [13]. Importance of Standards and Collaboration - The IEEE's efforts in developing AI standards were discussed, emphasizing the need for global cooperation and consensus in AI technology development to ensure ethical and socially responsible applications [14]. - The concept of data sovereignty and its implications for sustainable development was introduced, advocating for a new form of digital assets to promote sustainability in AI applications [14].