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].

Group 1 - The core viewpoint of the report indicates that large model technology has entered a critical phase of deep integration with business, with 64% of Chinese enterprises expecting their AI investments to grow by 10-30% [8][10] - The deployment cycle and application speed of large models in enterprises have exceeded expectations, with an average deployment period reduced to 6-12 months, especially in digitally advanced companies [9] - Enterprises are actively exploring business scenarios to unlock the value brought by large model deployment, aiming to create application practices that align with their development strategies [10] Group 2 - The report highlights that over 47% of enterprises believe that establishing reliable partnerships with leading large model vendors is key to project success [11] - The report emphasizes that large model technology is driving efficiency leaps and innovative experiences in enterprises, with a significant increase in investment and pilot projects [14][15] - IDC research indicates that 37.7% of surveyed enterprises are focusing on investing in AI large models, with expectations to introduce AI software and related training and services in the next three years [14] Group 3 - The report identifies multiple challenges faced by enterprises in deploying large models, including high costs, complex investments, and a lack of talent [28] - A significant 92% of enterprises consider the lack of computing resources as the biggest challenge in the engineering phase of large model deployment [28][29] - The report notes that 87% of enterprises believe that model accuracy does not meet deployment requirements, leading to difficulties in model selection and adaptation [31] Group 4 - Leading enterprises that have successfully deployed large models are already reaping benefits, with a clear revenue curve observed among early adopters [37] - The report describes a revenue curve indicating that the degree of AI adoption correlates with the benefits received, with higher investment leading to earlier returns [38] - Examples of successful implementations include SAIC Motor's use of large models to enhance customer feedback processing and improve service quality [42] Group 5 - The report outlines three stages for building comprehensive large model business deployment capabilities: planning and preparation, model deployment, and iterative optimization [52][56] - The planning stage emphasizes the need for clear initial intentions and resource assessment to support large model deployment [52] - The iterative optimization stage focuses on enhancing the effectiveness of AI applications and expanding their use across various business areas [59]

Investment Rating - The report does not explicitly state an investment rating for the industry. Core Insights - The financial technology (FinTech) sector is undergoing a significant transformation, emphasizing digitalization and the integration of data-driven innovations to enhance financial services [2][16]. - The construction of open financial ecosystems is crucial for financial institutions to improve service resilience and expand revenue streams [7][10]. - The adoption of cloud-native technologies is becoming a core strategy for financial institutions to enhance operational agility and efficiency [25][32]. Summary by Sections Introduction - The report highlights the importance of accelerating the digital transformation of financial institutions and strengthening regulatory oversight in FinTech [2]. Open Financial Ecosystem - Financial institutions are increasingly building and expanding industry ecosystems to meet complex customer needs through shared resources and capabilities [4]. - Large banks are expected to lead the development of open ecosystems, enhancing their business resilience and revenue generation through collaboration with various partners [7][10]. - By 2026, a significant percentage of leading banks are projected to share data and resources across multiple industry ecosystems to improve operational resilience [7]. Cloud-Native Technologies - The report indicates that 84% of financial institutions are planning to implement or are experimenting with cloud-native technologies to meet the demands of digital business [25]. - Cloud-native technologies are essential for financial institutions to achieve operational flexibility and rapid application deployment [25][32]. - The integration of AI applications within cloud-native architectures is expected to enhance business agility and efficiency [31]. Data Intelligence and AI - Financial institutions are focusing on building data intelligence capabilities using big data and AI technologies to drive digital transformation across various business functions [51]. - By 2025, a significant percentage of financial institutions are expected to leverage AI models to enhance their data intelligence capabilities [57]. Blockchain and Digital Currency - The report predicts that by 2026, a notable portion of cross-border payments will be facilitated through blockchain technology, enhancing transaction efficiency and reducing costs [64]. - Central Bank Digital Currencies (CBDCs) are anticipated to play a crucial role in the future of digital payments, with a growing emphasis on security and efficiency [58][84]. Privacy-Preserving Computing - Privacy-preserving computing technologies are becoming increasingly important for enabling secure data sharing and collaboration among financial institutions [66][92]. - The report suggests that the application of privacy-enhancing technologies will expand from retail to corporate business applications, addressing the need for secure data management [67]. Quantitative Trading - The report notes the rapid development of quantitative trading strategies among financial institutions, driven by advancements in AI and machine learning technologies [71][73]. - High-frequency trading is expected to increasingly rely on low-latency technologies to improve execution efficiency and success rates [74].