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 financial industry is at the forefront of digital technology, with significant advancements driven by AI models and domestic innovation [1] - The current IT development in the financial sector is characterized by two main trends: localization and intelligence [1] - There is a notable shift towards domestic software and hardware solutions, with many financial institutions actively transitioning to these technologies [1][2] Localization and Market Demand - The demand for domestic databases, cloud platforms, and new core systems is increasing among securities and insurance firms, with many projects currently underway [1] - Major financial institutions have entered a normalization phase for domestic construction, with banks approximately 60% complete and insurance and securities around 20% [1] - Despite a slight reduction in IT budgets, financial institutions are prioritizing investments in domestic technology architecture [1] AI Model Implementation - AI models are crucial for the intelligent transformation of the financial sector, with a focus on identifying key application scenarios [4] - There are over a hundred financial clients utilizing mixed models, with applications such as AI code assistants and intelligent customer service [5] - The "big model credit due diligence assistant" has significantly reduced the time required for due diligence reports from 10 days to 1 hour [5] Insurance Sector Developments - In the insurance industry, AI models are being used to build intelligent enterprise knowledge bases and provide training for insurance agents [6] - Companies are integrating AI models into various business scenarios, enhancing operational efficiency and addressing user pain points [6] - The approach to AI model development emphasizes embedding capabilities across a wide range of business applications, leveraging vast amounts of data for continuous improvement [6]