"金融是人工智能（AI）技术最具价值的试验田。"在2025年服贸会期间举行的第七届中国金融科技 论坛上，奇富科技首席执行官吴海生表示，"金融智能体正在经历从辅助到决策、从外围到核心、从局 部到全局的进化。" 国务院8月26日发布的《关于深入实施"人工智能+"行动的意见》提出，在软件、信息、金融、商 务、法律、交通、物流、商贸等领域，推动新一代智能终端、智能体等广泛应用。 近两年，金融科技、数字科技等平台企业积极在智能客服、自动化运维等场景进行AI大模型多维 度探索，夯实数字化基建底盘，从辅助业务环节入手，逐步渗透至金融业务的核心层，让科技加持的金 融服务"通行能力"实现代际跃升。 在技术赋能方面，中央财经大学中国互联网经济研究院副院长欧阳日辉总结称，平台企业将大数 据、人工智能、区块链等前沿技术以模块化、服务化的方式输出给金融机构，帮助其提升风控能力、优 化产品设计，实现精准营销与服务响应。 专家表示，当金融领域的生态协同从单点探索走向规模推广，制度规范从零散构建迈向体系化完 善，平台的创新应持续聚焦关键议题。 "平台企业的创新并非单向的技术输出或商业收益，而是多方共建、风险共担、价值共享的创新治 理新格局。 ...

蚂蚁数科的 Agentar 企业级全栈智能体平台，通过五大核心支撑构建起金融领域智能应用的可信底座， 既突破了金融场景的专业性、复杂性壁垒，又确保了技术应用的合规性与可靠性，最终推动金融新质生 产力实现跃迁。 支撑一：企业级全栈智能体平台，夯实技术底座 平台以 "1000 + 安全合规水位标准" 为基础，提供从底层架构到上层应用的全栈能力，支撑金融智能体 在复杂业务场景中稳定运行。其核心价值在于打通技术与业务的衔接，让智能体能够适配银行、保险、 证券等多类金融机构的差异化需求，为后续功能落地提供坚实的基础设施支撑。 支撑二：金融大模型，构建智能中枢 蚂蚁数科金融大模型是平台的 "大脑"，具备可靠、可控、可优化三大特性： 相比通用大模型，其在金融领域的语言理解、知识储备、逻辑推理和数学计算能力更突出，通过二次训 练(结合蚂蚁侧高质量金融数据与客户侧专有数据)可形成机构专有模型； 依托专业知识工程，构建了精标长 COT 数据、金融标签体系等高质量训练数据集，确保模型 "懂规 则、精业务"，减少 "幻觉"，保障数值计算准确与逻辑自洽。 支撑三：金融知识工程，破解专业壁垒 通过标准化加工金融知识资产，让智能体具备深度 ...

文｜光锥智能 当AI大模型已开始走向千行百业之时，备受看好的金融行业，却似乎陷入了落地瓶颈。 打开手机银行想查下贷款额度，对着屏幕说了半天，AI客服却只回复 "请点击首页贷款按钮"； 客户经理想用大模型生成一份客户资产配置方案，结果推荐的产品与客户风险等级完全不符； 风控团队测试的AI模型，在审批中小企业贷款时频频给出"幻觉答案"...... 这些看似荒诞的场景，却是当前AI落地金融行业时的真实困境。 当金融机构满怀期待地将AI请进门，却发现它既读不懂复杂的信贷政策，算不清理财产品的费率结构，更搞不懂不同银行的"行话体系"。 通用大模型的"聪明"，在严肃的金融场景里似乎失灵了，大模型与金融场景之间，也仿佛横亘着一道看不见的鸿沟。 "企业和产业需要的不是实验室的技术，而是真正能够解决真实问题的可信生产力。"蚂蚁数科CEO赵闻飙在2025WAIC智能体驱动产业变革论坛上如此说 道。 也正因此，面向AI大模型在具体行业中的落地，想要真正释放AI价值，关键就在于要从水平通用转向垂直专用，只有深度理解行业的大模型，才能懂行 话，做行活儿，创造真实的业务价值。 金融行业的AI落地，更是如此。 因此，构建专业的金融大模型是 ...

Core Insights - The article discusses the launch of Ant Group's financial reasoning model, Agentar-Fin-R1, which is designed specifically for the financial industry and has achieved top scores in three major financial benchmark tests, surpassing other models like Deepseek [2] - Ant Group's model aims to address challenges in the financial sector, such as hallucination issues, output stability, and process interpretability, highlighting the necessity for specialized financial reasoning models [2][3] - The company has developed a comprehensive financial task classification system covering six major categories and 66 subcategories, utilizing a vast dataset to enhance the model's ability to handle complex tasks [3] Company Developments - Ant Group has introduced a full-stack solution that includes financial industry models, AI platforms, and upper-layer applications, facilitating the practical application of AI in finance [3] - The company has launched over a hundred financial intelligent agent solutions in collaboration with industry partners, significantly improving frontline employee efficiency by over 80% [3] - Ant Group's AI technology team emphasizes the importance of understanding financial scenarios and practical experience in driving business growth through AI models [3] Industry Trends - The World Artificial Intelligence Conference (WAIC 2025) showcased a variety of intelligent agent applications in the financial sector, indicating a shift from single-point attempts to large-scale applications in core business areas like credit decision-making [4] - The current era is characterized by a proliferation of AI intelligent agents, with a focus on sustained development in vertical fields, particularly finance [4]

Core Insights - The article emphasizes that Qifu Technology is becoming a key partner in the digital transformation of Chinese banks by integrating financial intelligence with business scenarios [1][7] - Qifu Technology's financial intelligence platform, Deepbank, and its various AI applications are designed to meet the real business needs of banks [2][7] Group 1: Financial Intelligence Development - Qifu Technology launched its self-developed financial intelligence platform Deepbank, which includes four core applications: AI Marketing Assistant, AI Approval Officer, AI Decision Assistant, and AI Compliance Assistant [2][3] - The financial intelligence system is built on a heterogeneous large model platform and a multi-agent collaborative framework, enabling deep understanding of financial semantics and user behavior [2][4] Group 2: Comprehensive Credit Solutions - The upgraded "Qifu Credit Super Intelligent Agent" includes five modules that cover the entire credit business process, enhancing decision-making capabilities [3][6] - The "End-to-End Credit Decision" module utilizes over 700 models and more than 1 billion historical decision data points to accurately assess user risk [3][4] Group 3: Technological Empowerment - Qifu Technology employs four technological engines: data flywheel, multi-modal fusion, self-evolution, and multi-agent collaboration to enhance the intelligence of its financial agents [4][5] - The AI Approval Officer can automatically extract key information from user applications and provide instant recommendations, significantly improving efficiency [5][6] Group 4: Practical Applications and Collaborations - Qifu Technology has demonstrated the effectiveness of its financial intelligence through partnerships with banks, leading to improved customer acquisition and reduced compliance risks [6][7] - A strategic cooperation agreement was signed with Guangdong Huaxing Bank to explore deep applications of AI in credit business, showcasing a collaborative model for digital transformation in banking [6][7]

Core Viewpoint - The importance of safety and trustworthiness in digital financial innovation is emphasized, highlighting the need for regulatory clarity and market confidence [1][5][6]. Group 1: Digital Financial Innovation Principles - The "high, medium, initial, small" principle is proposed to balance innovation and risk tolerance, allowing for some flexibility in risk management [1][8]. - Financial models must prioritize safety, reliability, and explainability, with a focus on advanced security technologies to prevent malicious attacks [2][4]. Group 2: Key Considerations for Financial Models - Financial models should avoid pitfalls such as model hallucination, discrimination, algorithmic resonance, AI deception, and the coldness of machine interactions [4]. - The need for legal clarity regarding the status and responsibilities of financial AI is highlighted, ensuring that financial institutions have clear decision-making accountability [4][6]. Group 3: Economic Efficiency and Collaboration - Industry-level financial models should be developed through extensive data pre-training and customization to reduce development costs and expand application ranges [5]. - Collaboration between strong tech companies and financial institutions is encouraged to lead the development of industry-level financial models and applications [5]. Group 4: Regulatory Innovation - The necessity for a robust regulatory framework for digital finance is stressed, including the establishment of clear business norms and a comprehensive regulatory system [6][7]. - The balance between innovation and regulation is crucial, with a call for a flexible approach that does not stifle innovation while ensuring market stability [7][8].