Core Viewpoint - The current development of financial IT can be summarized by two key terms: "domestication" and "intelligence," which are driving the industry into a full-chain upgrade phase [3][6]. Group 1: Trends in Financial IT Development - The demand for digital transformation among financial institutions has been increasing, extending from banks, securities, and insurance to consumer finance and leasing, with clear requirements for overall progress [5]. - The trend has led to an explosive growth of related projects by 2025, with a significant increase in the number of projects involving domestic database selection, cloud platform selection, hardware procurement, and new core system ISV collaborations among brokerages and insurance institutions [6]. - The number of domestic database vendors has decreased by over 60 in the past year, with financial institutions preferring mature products from leading vendors that have undergone extensive business verification and stability testing [7]. Group 2: AI and Intelligent Applications - The emergence of DeepSeek marks a significant turning point for the application of AI large models in the financial sector, enabling a broader range of institutions to develop applications at a low cost [8][10]. - AI applications are evolving through four stages: from "able to chat" to "able to work," then to "self-planning," and finally to "multi-Agent collaboration" [8]. - Current AI applications in financial institutions include code assistants that enhance development efficiency and knowledge base applications, while initial applications in trading and risk control are still in the early planning stages [10][11]. Group 3: Strategic Implementation and Challenges - Financial institutions are actively investing in AI, with many executives placing it at a strategic level, focusing on "phased usable scenarios" during implementation [13]. - A collaborative system of "small models + large models" is recommended, where institutions first establish usable datasets and then train small models for specific tasks while large models handle content generation [14]. - The implementation of intelligent architecture requires modular planning and stepwise advancement, with clear timelines, responsibilities, and acceptance criteria for each phase [14][15].

Core Insights - The global research on large models and application scenarios is experiencing a Cambrian explosion, with rapid iteration of foundational models and diverse applications emerging quickly [2] - The main trends in financial IT development are "domestication" and "intelligence," which are driving the industry towards a comprehensive upgrade [3] Group 1: Domestic and Intelligent Development - The demand for digital transformation in financial institutions is increasing, extending from banks and securities to consumer finance and leasing, with a clear push for overall progress [4] - The trend is expected to lead to explosive growth in related projects by 2025, particularly in the selection of domestic databases, cloud platforms, hardware procurement, and collaboration with independent software vendors (ISVs) [4] - The peak period for domestic usage is anticipated in the coming years, with necessary investments in technology architecture despite overall IT investment reductions [4] Group 2: Database and AI Model Transition - The domestic database transition is deepening, with a 60% reduction in the number of domestic database vendors over the past year, leading financial institutions to prefer mature products from leading vendors [5] - The introduction of DeepSeek marks a significant turning point for AI large models in the financial sector, enabling institutions of all sizes to develop applications quickly and cost-effectively [6] - AI applications are evolving through four stages, from basic interaction to collaborative multi-agent systems capable of executing complex tasks [6] Group 3: Maturity and Challenges in AI Applications - AI technologies, particularly large models, are becoming core drivers of digital transformation in finance, with some applications already demonstrating practical value [7] - Code assistants based on large language models are enhancing development efficiency across financial institutions, while risk control models are still in early stages of application [8] - The risk control model developed by Tencent Cloud has shown a 10%-20% improvement in user identification accuracy across various scenarios [8] Group 4: Strategic Investment and Implementation - Financial institutions are making strategic-level investments in AI, focusing on developing usable scenarios while optimizing resource allocation [9] - A collaborative system of "small models + large models" is recommended, emphasizing the need for specialized data sets to enhance AI's effectiveness in specific financial tasks [10] - The implementation of intelligent architecture requires modular planning and stepwise advancement, with clear timelines and responsibilities [10] Group 5: Long-term Vision and Execution - The journey towards financial intelligence is described as a "protracted battle," where top-level planning, collaborative mechanisms, and stepwise exploration are essential for balancing high investment with long-term returns [11]