中国金融大模型发展白皮书：开启智能金融新时代

Investment Rating - The report does not explicitly provide an investment rating for the industry. Core Insights - AI large models have become a crucial component of new productive forces, significantly enhancing production efficiency, optimizing resource allocation, and reducing production costs, thereby supporting high-quality development for enterprises [3][4]. - The financial industry is leading in the research and application of AI large models, with investments projected to reach 19.694 billion yuan in 2024 and 41.548 billion yuan by 2027, marking a growth of 111% [4][25]. - The application of AI large models in the financial sector faces unique challenges, including high demands for data quality, inference accuracy, and compliance with regulatory standards [4][26]. Summary by Sections Chapter 1: Overview of AI Large Model Development - AI large models are integral to the new productive forces, driving significant advancements in digital transformation across various sectors [12]. - Major global regions, including the US, China, Japan, and the EU, are intensifying their efforts in AI large model innovation and application [13][15]. Chapter 2: Focus on the Financial Industry - The financial sector is at the forefront of AI large model investment and application, with a focus on enhancing operational efficiency and compliance [4][25]. - Financial institutions face higher requirements for data governance, model governance, and compliance applications compared to other sectors [26][27]. Chapter 3: Progress in Implementation - The application of generative AI in the financial industry is progressing from simple to complex scenarios, with key areas including payment clearing, intelligent investment research, and fraud monitoring [6][39]. - Financial institutions are advised to adopt a phased approach in selecting and implementing AI applications, focusing on internal operations before expanding to customer-facing services [58]. Chapter 4: Application Paths and Key Capabilities - Financial institutions can choose different paths for implementing AI large models based on their strategic goals, business needs, and resource capabilities [71]. - The report emphasizes the importance of building a robust data value chain management system to ensure high-quality data for AI applications [7].