证券日报网讯金现代（300830）9月18日在互动平台回答投资者提问时表示，公司积极开展数据要素领 域的产品建设，截至目前核心产品矩阵已覆盖融合集成平台、指标平台及智能问数平台等软件，可深度 满足企业在数据整合、分析与高效应用等场景的需求，目前已服务江淮汽车（600418）、福莱特 （601865）、南网数字科技、亿力吉奥等众多行业标杆客户。凭借在数据管理能力方面的优势，公司近 年来先后获得中国电子信息行业联合会认证的"DCMM数据管理能力成熟度稳健级(三级)"资质、"山东 省数据要素领域突破性创新技术奖"等权威认可，标志着公司的数据管理与应用水平已达到行业先进标 准，为后续持续服务客户、创造更多价值奠定坚实基础。 ...

Core Insights - The Chinese banking industry is at a turning point with the emergence of AI technology, particularly AI Agents, which are set to revolutionize core banking functions such as credit and risk management by significantly enhancing productivity and efficiency [1][3][21] - AI Agents, built on large AI models, can autonomously perform tasks, assist in decision-making, and provide personalized financial services, thereby reducing manual intervention and operational costs [1][3][4] Group 1: AI Agent Implementation and Value - AI Agents are becoming a core focus for banks, with significant investments being made to develop and implement these technologies [4][6] - The core values of AI Agents include improving efficiency through end-to-end automation, enhancing decision-making capabilities, and providing personalized customer experiences [3][21] - Major banks like ICBC and Agricultural Bank of China are leading in financial technology investments, with ICBC planning to spend approximately 28.518 billion yuan in 2024 [6][8] Group 2: Bank-Specific Developments - Agricultural Bank of China has introduced the "Mosu Loan Scoring Card" AI Agent, which can generate credit reports in 30 seconds, significantly speeding up the due diligence process [8] - Postal Savings Bank is rapidly advancing its AI capabilities, achieving over 87.5% automation in alarm troubleshooting through its AI Agents [9] - Other banks, including China Merchants Bank and Ping An Bank, are also developing their own AI Agents to enhance data analysis and customer service [11][12] Group 3: Technology Partnerships - Banks are increasingly collaborating with technology companies to bridge the technological gap and enhance their AI capabilities [13][20] - Major tech players like Baidu, Alibaba, and Tencent are providing comprehensive AI solutions and infrastructure, which are crucial for the successful implementation of AI Agents in banking [14][15] - The partnership between banks and tech companies is essential for unlocking the potential of AI in the financial sector, especially for smaller banks [13][20] Group 4: Challenges and Future Outlook - Despite the rapid development of AI Agents, many banks are still focused on non-core applications, indicating a gap between potential and actual implementation [21][22] - The banking sector requires high accuracy and reliability from AI systems, which currently face challenges such as a 95% accuracy rate in leading financial models [23][24] - The transition to AI-driven banking is a long-term process that necessitates a solid AI strategy and collaboration between banks and technology providers to achieve significant ROI [30][31]

Core Insights - The importance of data is increasingly recognized as the "oil" of the new era, with China National Petroleum Corporation (CNPC) launching a 300 billion parameter Kunlun large model, marking a significant advancement in the energy and chemical sector [1][3] - The Kunlun model has demonstrated substantial efficiency improvements, such as increasing seismic interpretation efficiency by 9 times and reducing exploration project cycles by over 20% [1] - CNPC is focusing on digitalization and intelligence as core strategies to build a world-class energy company, implementing a comprehensive plan for "Smart China National Petroleum" [1][4] Group 1: Digital Transformation in Oilfields - The Tarim Oilfield, China's largest ultra-deep oil and gas production base, has initiated a digital transformation pilot project in collaboration with Kunlun Smart, enhancing operational efficiency and decision-making capabilities [4][5] - The digital transformation at Tarim Oilfield integrates 25 production-related systems, significantly improving production assistance and emergency response efficiency [5][6] - The establishment of a standardization system and methodology during the digital transformation serves as a model for other oilfields, with real-time data integration achieving a 90% data entry rate [6] Group 2: Overcoming Data Silos - The complexity of refining processes necessitates overcoming data silos for successful digital transformation, which is being addressed through innovative technologies such as industrial internet and artificial intelligence [9][12] - CNPC has developed a unified data governance system to eliminate data silos, enabling seamless data flow across different systems and enhancing operational efficiency [12] Group 3: Innovations in Exploration and Development - The Kunlun large model has improved the accuracy of subsurface structure identification by at least 10 percentage points compared to traditional methods, covering over 100 scenarios in exploration and refining [13][15] - CNPC has developed proprietary industrial software, such as GeoEast and HiSim, to support exploration and production, filling gaps in domestic capabilities previously dominated by foreign technologies [15][17] Group 4: Talent Development and Future Strategies - CNPC is focusing on cultivating "smart talent" that combines technical and business expertise to adapt to the evolving demands of the industry [21] - The company is implementing AI-driven tools to facilitate data access and enhance operational efficiency, exemplified by the "Smart Inquiry" platform that provides quick data responses [21][22]

Core Insights - The report from Northeast Securities highlights that banks are expected to become pioneers in AI implementation in China due to ample IT budget, market-oriented systems, and high integration of internal data [1][3] - DeepSeek-R1's inference cost is only 1/30 of comparable products, marking a new phase of "AI popularization" in the industry [1] - The year 2025 is anticipated to be the starting point for AI Agents, with significant competition among major companies in this area [2] Group 1: AI Technology and Applications - DeepSeek has launched several well-known open-source models since its establishment in July 2023, with the DeepSeek-R1 model achieving performance comparable to OpenAI's o1 series at a significantly lower cost [1] - Major banks have actively integrated AI technology into various applications such as investment research, customer service, credit approval, and more, enhancing the intelligence of financial services [3] - IDC predicts that the banking sector will account for over 20% of global AI solution spending from 2024 to 2028 [3] Group 2: Specific Companies and Their AI Initiatives - Yuxin Technology has fully integrated DeepSeek models into its product system, focusing on applications in credit, data, and marketing channels [4] - Jingbeifang has launched an AI large model service platform and several intelligent assistants, achieving breakthroughs in smart fraud prevention and investment advisory across multiple industries [4] - Gaoweida has deeply integrated DeepSeek with its credit business, enhancing credit efficiency and financial report analysis through AI applications [4] - Tianyang Technology has released intelligent testing analysis systems and compliance models, providing banks with intelligent data analysis solutions [4] - Shenzhou Information has upgraded its financial knowledge Q&A and coding assistants, improving development efficiency by 20% and automating 30% of code generation [5]