证券业大模型布局渐入佳境建立AI能力分级认证制成共识

Core Insights - The forum highlighted the application and challenges of AI large models in the securities industry, with several chief information officers from various brokerages sharing their experiences and strategies [1] Group 1: AI Model Implementation - Shanxi Securities has successfully integrated AI large models into specific business scenarios, achieving a tenfold increase in efficiency for bond trading by reducing response time from 30 seconds to 3 seconds [2] - Guoyuan Securities has established a six-layer AI empowerment system, focusing on the application of AI tools for investment banking projects, including intelligent verification and regulatory Q&A capabilities [2] - Huafu Securities allocates approximately 25% of its annual IT investment to AI, implementing performance assessments based on AI project usage and depth [3] - Southwest Securities has initiated AI large model exploration in 2023, establishing a dedicated digital transformation office and implementing applications such as intelligent knowledge bases and investment advisory assistants [3] - Guotai Junan Securities has adopted an "All in AI" strategy, enhancing employee understanding of AI through competitions and developing AI tools for client services [3] Group 2: Regulatory Framework - There is a consensus in the industry on the need to improve the regulatory framework for AI applications, with suggestions for a tiered certification system for AI financial services and clear responsibility definitions for AI services [4] - Recommendations include establishing data usage norms to ensure transparency and compliance when using customer data, as well as promoting standardization of AI technology to enhance service quality [4][5] Group 3: Future Industry Trends - The rapid evolution of technology is expected to significantly change service models and operational logic in the securities industry, with predictions of a "disillusionment period" for AI large models in the next couple of years [5][6] - The potential for AI large models to evolve into decision-making tools is highlighted, with the ability to abstract various elements and incorporate external market changes into algorithms [6] - The industry anticipates a shift towards AI-native applications and an increase in the use of domestic computing power, which is expected to surpass other heterogeneous computing resources [6][7] - The emergence of a comprehensive intelligent agent matrix is predicted, which could transform business models and ethical considerations within the industry [6]