以下文章来源于科创板日报 ，作者田野 黄心怡 科创板日报 . 专注科创板和科技创新，上海报业集团主管主办，界面财联社出品。 聚焦AI赋能实体，发掘千行百业智能标杆。 2025年，人工智能已从技术探索迈入规模化应用落地开花，"AI+应用"不再是前沿概念的代名词，而是扎根工业制造、金融决策、校园课 堂、医疗健康、文化旅游等千行百业的务实实践，以精准赋能重构行业效率、优化民生体验，成为激活新质生产力的新路径。 为全景展现国内AI技术落地的实战成果，提炼可复制、可推广的行业赋能经验，推动AI与实体经济深度融合，上海报业集团主管主办财联社 与《科创板日报》，正式启动"2025中国AI+应用Top50"优秀案例征集活动，面向全国工业、金融、教育、医疗、文旅等行业领域，评选 50个兼具实效价值与示范意义的优秀AI+应用产品及服务。 联合主办: 聚焦AI赋能实体 发掘千行百业智能标杆 2025中国AI+应用TOP50 优秀案例评选 征集时间： 2025.12.16-2026.1.16 「活动背景 2025年，人工智能已从技术探索迈入规模化应用 落地开花，"Al+应用"不再是前沿概念的代名词, 而是扎根工业制造、金融服务、校园 ...

石岩对金瑞期货一行的到访表示热烈欢迎，并系统展示了同花顺在期货领域的核心产品与服务。他介 绍，同花顺已依托金融大模型在期货数据服务、智能投顾、智能投研、智能风控等领域形成成熟应用体 系，助力期货公司实现数智化经营升级。"期望在大模型赋能下，携手挖掘更多合作可能，为双方业务 拓展与创新注入更多新动能。" 近日，同花顺与金瑞期货在杭州正式签署深度合作协议。同花顺总裁助理石岩、金瑞期货总经理侯心强 代表双方出席签约仪式并签字，标志着两家企业在前期交易服务合作基础上，开启以AI技术为核心、 覆盖多业务领域的数智化协同新篇章。 签约仪式上，侯心强介绍了公司的业务优势与发展规划。他表示，金瑞期货长期深耕期货衍生品领域， 尤其在贵金属风险管理、产业链产融服务方面积累了丰富实践经验。"当前期货行业正加速迈入数智化 转型关键期，金瑞期货早在三年前便启动'数字金瑞'建设，已构建起智能投研平台、衍生品对客系统等 数字化基础。此次选择与同花顺深度合作，正是希望借助其金融大模型技术优势，进一步升级数字化与 智能化水平，在策略定制、数据融合、合规风控等领域实现突破，持续优化客户服务体验。" 侯心强强 调。 作为行业内率先布局数智化转型的 ...

证券行业发展空间广阔，大有可为。一家大型券商的相关负责人表示，在监管政策持续完善下，证券行 业格局有望深度重塑，专业能力突出、稳健合规的优质券商将在行业机遇期加速转型，一流投行建设加 快推进，未来，资产配置、创新能力和国际化布局将成为证券公司提升的重点领域。 并购浪潮下一流投行渐近 并购重组正在成为证券行业转型升级的主路径，以国泰海通合并等标志性重组案例平稳落地，初步实 现"1+1>2"的效果。吴清指出，头部机构要进一步增强资源整合的意识和能力，用好并购重组机制和工 具，实现优势互补、高效配置，力争在"十五五"时期形成若干家具有较大国际影响力的头部机构。 新"国九条"以来，已有多家券商完成重组，包括国泰君安合并海通证券、西部证券合并国融证券、国信 证券合并万和证券、浙商证券合并国都证券等，中金公司拟吸收合并东兴证券、信达证券的并购事项在 推进过程中。 近日，证监会主席吴清在中国证券业协会会员大会上的讲话，明确了证券行业高质量发展的方向，包括 鼓励券商用好并购重组机制和工具实现优势互补，加快打造一流投资银行和投资机构；差异化监管促进 券商特色化发展；畅通证券业创新试点工作机制，丰富监管沙盒应用场景等。 中小券商 ...

Core Insights - The company has initiated its AI application strategy since 2017, adopting the "AI in All" approach to empower various business lines and systems [1] - With advancements in large model technology, the company has upgraded its AI strategy to "ALL in AI," transitioning from enabling AI to transformative AI [1] - The company has implemented over 150 AI application scenarios across various business areas, creating a new development pattern of "ubiquitous intelligence" [1] Technology Challenges - The company identifies the "hallucination" and interpretability issues of AI as significant challenges, particularly in the finance sector where precision is critical [2] - To address these challenges, the company has developed a "1+N" application system, combining general large models with industry-specific models to ensure reliable service outputs [2] Investment Focus - The company emphasizes the need to focus AI investments on core business areas to generate sustainable business value, especially as the enthusiasm for large model applications wanes [2] - The return on investment will become a central concern for companies, necessitating AI to address industry pain points effectively [2] Industry Development - The company advocates for the establishment of a regulated development order to maintain a healthy industry ecosystem, urging collaboration among regulators, institutions, and clients [2] - Industry associations are working on guidelines to standardize development paths, which will support the healthy growth of AI in the securities sector [2] Future Outlook - The company acknowledges the immense potential of AI while recognizing the challenges, suggesting that a collaborative approach can transform technical challenges into new development opportunities for the securities industry [2]

Core Insights - The 2025 Greater Bay Area Exchange Technology Conference highlighted the transition of AI technology in the securities industry from conceptual exploration to deep implementation, presenting both opportunities for efficiency and challenges for governance [1] - AI is positioned as a core driver for high-quality development in capital markets, with a focus on integrating AI capabilities with market governance needs [1] - The conference emphasized the importance of aligning AI advancements with regulatory frameworks to enhance market development and regulatory enforcement [1] Group 1: AI Technology Development - AI is recognized as a strategic technology leading a new wave of technological revolution and industrial transformation, with the year 2025 being termed the "Year of AI Agents" [2] - The securities industry is becoming a significant application scenario for AI, driving the sector towards greater intelligence, efficiency, and inclusivity [2] - AI's role in the securities industry is more critical than in other sectors, providing substantial support in customer acquisition and revenue generation [2] Group 2: Implementation and Challenges - Companies like Guotai Junan Securities have integrated AI across various business sectors, achieving over 150 AI applications that enhance risk control, investment research, and trading [3] - Regulatory bodies are actively embracing AI to improve oversight and compliance, integrating AI throughout the regulatory process [3] - The financial industry is accelerating its adoption of AI, overcoming challenges such as data governance and computational power limitations [4] Group 3: Future Outlook and Collaboration - AI is expected to enhance the overall competitiveness of the securities industry by improving customer service, operational efficiency, and promoting high-quality development [4] - Challenges such as AI's "hallucination" problem and lack of interpretability pose risks in the finance sector, necessitating careful consideration [4] - The industry is working towards overcoming AI application bottlenecks through technological advancements, regulatory adaptations, and collaborative innovation [5] Group 4: Strategic Initiatives - The Shenzhen Stock Exchange aims to build a world-class digital and intelligent trading platform by focusing on risk prevention, regulatory strength, and high-quality development [6] - Key initiatives include planning intelligent computing infrastructure, implementing cloud applications, and enhancing AI integration in core business areas [6] - The potential of AI in the securities industry is significant, but its development requires guidance from regulatory bodies, practical exploration by institutions, and cooperation from clients [6]

Core Insights - The "Digital Risk Control Project" won the second prize in the national finals of the 2025 "Data Element ×" competition, showcasing a significant achievement among 22,000 participating projects [1] - The competition aims to promote the marketization of data elements and the deep integration of data with industries, with a focus on various sectors including financial services [1] Group 1: Project Overview - The "Digital Risk Control Project" addresses industry pain points such as data integration, circulation, and application difficulties, establishing the first "data-risk-ecosystem" digital risk control system in the insurance sector [2] - The project utilizes a robust data foundation from Ping An Group, creating a comprehensive database covering ten high-quality data categories, with a total data volume exceeding PB level [2] - It integrates over 370 authoritative data sources, achieving a compliance data fusion model and a claims knowledge engineering system, with data standards reaching DCMM level five [2] Group 2: Technological Innovations - The project has developed a large model and knowledge engineering system for the insurance domain, utilizing trillions of insurance corpus and hundreds of millions of claims data to create an interpretable knowledge graph and intelligent reasoning chain [2] - The automation rate of knowledge has reached 70%, while the data knowledge rate stands at 50%, significantly enhancing risk identification accuracy and control efficiency [2] - The model has been implemented in scenarios such as claims risk control, risk pricing, and fraud detection, benefiting over 20 insurance institutions and generating economic and social benefits exceeding 10 billion [2] Group 3: Company Strengths - Ping An Group's technological innovation and ecological collaboration are highlighted through this award, reflecting its systemic strength in driving intelligent financial development with data elements [3] - The company has accumulated over 30 trillion bytes of data, covering nearly 250 million individual customers, and has trained large models based on this vast data [3] - AI has been fully integrated into Ping An's core business, achieving a 63% automation rate in personal injury claims and processing car insurance applications in an average of one minute [3] Group 4: Future Directions - Financial One Account will continue to act as a technology output window, collaborating with the Ping An ecosystem and the industry to explore new intelligent financial models driven by data elements [4] - The aim is to contribute to the high-quality development of the financial industry, support the real economy, enhance financial risk prevention capabilities, and promote new productive forces [4]

Core Insights - The recent recruitment initiatives by several Chinese banks, including Bank of China, China Construction Bank, Nanjing Bank, and GF Securities, emphasize the urgent need for "financial + technology" interdisciplinary talents, indicating a strategic shift towards technology-driven high-quality development [1][2][4] Recruitment Focus - The banks are prioritizing candidates with experience in artificial intelligence, financial technology, and related fields, reflecting a preference for practical skills over purely academic qualifications [2][4] - Nanjing Bank's recruitment announcement specifies that applicants must have obtained a PhD within the last three years or be graduating by July 2026, with a focus on candidates under 35 years old [2] - GF Securities is also targeting PhD graduates from January 2024 to August 2026, with similar age and qualification requirements, emphasizing backgrounds in computer science, artificial intelligence, big data, and quantitative finance [2][3] Strategic Shift - The banks are transitioning from traditional scale expansion to a focus on efficiency and risk management, driven by the need to adapt to a challenging financial environment characterized by narrowing net interest margins [4][5] - The research topics set by these banks highlight their commitment to integrating advanced technologies like AI into their operations, with specific areas of focus including digital transformation, risk management, and enhancing customer service [3][4] Talent Demand Transformation - There is a notable shift in the banking sector's talent requirements from "single skill" to "cross-disciplinary integration," emphasizing the need for professionals who can bridge finance and technology [4][5] - The banks are increasingly valuing practical experience and the ability to produce actionable research outcomes, contrasting with the traditional academic focus of universities [4][5] Future Directions - The banks aim to leverage AI for smart risk control, intelligent operations, and inclusive finance, marking a significant evolution in their business models from service providers to intelligent solution providers [5]

Group 1 - The company, Tuolsi, focuses on providing software products and data services in the financial technology sector, specifically targeting banking financial institutions [2] - Current financial-related services include intelligent risk control, intelligent consumer protection, inclusive lending, and green finance [2] - The company has not yet ventured into electronic payment financial security [2]

Core Insights - The aging population in China is accelerating, leading to a diversified demand for elderly care services, with a focus on the development of inclusive and intelligent elderly finance [1][2] - Artificial intelligence (AI) is being integrated into the entire elderly finance chain, addressing issues such as high service thresholds, narrow coverage, and weak data support [1][2] Group 1: Demographics and Market Needs - By the end of 2024, the elderly population aged 60 and above in China is projected to reach 31.03 million, accounting for 22.0% of the total population, while those aged 65 and above will be 22.02 million, making up 15.6% [2] - The demand for specialized and precise elderly finance services is increasing as the aging population grows [2] Group 2: Role of AI in Elderly Finance - AI can lower the cost and threshold of elderly finance services, allowing for a broader reach to small and medium enterprises and flexible employment groups [2][3] - AI enhances the transparency and adaptability of elderly finance products, fostering consumer trust and engagement [3] - AI can integrate multi-source data for risk assessment and demand forecasting, optimizing product design and service delivery [3][4] Group 3: Challenges in AI Application - The application of AI in elderly finance faces challenges such as insufficient depth of use, unclear boundaries for data privacy protection, scarcity of high-quality financial data, and inadequate computational support [4][5] - Data sharing issues exist, with public data often fragmented and non-public data circulation being inefficient [4][5] Group 4: Collaborative Efforts Required - The development of elderly finance is a long-term endeavor that requires collaboration among government, market, society, and families [6][7] - There is a need for top-level design and institutional supply to drive the cross-sector development of AI in elderly finance [7] - Expanding public data sharing and establishing a national public database are essential for maximizing the value of data in elderly finance [7][8] Group 5: Technological Integration and Service Innovation - Companies are encouraged to build unified platforms that integrate health records, care documentation, and financial assets to provide personalized services [8] - The use of IoT and smart devices in various scenarios, such as health management and safety monitoring, is being promoted to enhance service efficiency and quality of life for the elderly [8]

Core Insights - The aging population in China is accelerating, leading to a diversified demand for elderly care services, with a focus on the development of inclusive and intelligent elderly finance as a key area for improving the quality of life for seniors and supporting the pension system [1][3] - The integration of artificial intelligence (AI) technology into the entire elderly finance chain is seen as a solution to address high service thresholds, narrow coverage, and weak data support [1][3] Demographic Trends - By the end of 2024, the population aged 60 and above in China is projected to reach 31.03 million, accounting for 22.0% of the total population, while those aged 65 and above will number 22.02 million, making up 15.6% of the total [3] AI's Role in Elderly Finance - AI can lower the cost and threshold of elderly finance services, allowing for a broader reach beyond traditional high-net-worth individuals and large enterprises to include small and micro enterprises and flexible employment groups [3][4] - AI enhances the transparency and adaptability of elderly finance products, fostering consumer trust and engagement by providing personalized planning and asset allocation advice based on individual risk preferences and life scenarios [4] Challenges in AI Application - Despite the potential of AI in elderly finance, challenges remain, including limited application depth, unclear boundaries for data privacy protection, scarcity of high-quality financial data, and insufficient computational power [5][6] - Data sharing issues exist, with public data often fragmented across administrative divisions and non-public data facing circulation challenges [5][6] Collaborative Efforts Required - The development of elderly finance is a long-term endeavor that requires collaboration among government, market, society, and families to leverage AI tools effectively [7] - There is a need for top-level design and institutional support to ensure that AI-driven innovations in elderly finance benefit a wider population [7][8] Technological Integration - Companies are encouraged to build unified platforms that integrate health records, care documentation, consumption preferences, and financial assets to create comprehensive profiles for elderly individuals [8] - The use of IoT, smart devices, and advanced technologies in various scenarios such as health management and daily care is essential for enhancing service efficiency and improving the quality of life for seniors [8]