开启智能金融新时代 ⸺中国金融大模型发展白皮书 | 核心观点 | 01 | | --- | --- | | 第一章 百舸争流:Al大模型发展概述 | 04 | | 1.1 Al大模型与新质生产力 | 05 | | 1.2 国内外Al大模型的发展现状 | 05 | | 1.3 Al大模型应用发展整体现状 | 07 | | 第二章 聚焦行业： 金融行业大模型概述 | 09 | | 2.1 金融行业大模型应用的特殊性 | 10 | | 2.2 金融行业大模型应用落地面临的挑战 | 11 | | 第三章 落地进展：大模型催生效率变革 金融行业务实求效 | 13 | | 3.1 大模型在金融行业的典型应用场景梳理 | 14 | | 3.2 生成式AI在金融行业场景应用流程梳理 | 22 | | 第四章 金融行业大模型的应用路径与关键能力 | 26 | | 4.1 金融机构落地大模型的应用路径 | 27 | | 4.2 金融机构选择或部署大模型时的关键能力要素 | 34 | | 第五章 展望未来：金融行业大模型的发展趋势 | 42 | | 5.1 大模型技术创新与发展趋势 | 43 | | 5.2 行业应用场景的拓展趋势 ...

2025年 中国人工智能计算力 发展评估报告 目录 | 核心观点 | 01 | | --- | --- | | 第一章 全球及中国人工智能发展概述 | 03 | | 1.1 全球：生成式人工智能成为重要新型工作负载，人工智能算力呈现五大发展趋势 | 04 | | 1.2 中国：系统性提高算力效能，加速智能涌现和智能应用 | 09 | | 第二章 人工智能算力及应用 | 14 | | 2.1 芯片和服务器：向高性能与高效能方向演进，重视开放多元体系建设 | 15 | | 2.2 存储和网络：分布式存储与全闪存提升性能，先进网络架构优化数据访问速度 | 17 | | 2.3 可持续数据中心：液冷技术成为关注重点，聚焦智能算力散热革命 | 18 | | 2.4 边缘计算：大模型的部署向边缘迁移，智慧边缘加速模型推理 | 19 | | 2.5 算法和模型：算法创新与模型迭代解锁更高算力利用率，实现卓越性能与效率 | 21 | | 2.6 人工智能算力服务：构建全栈服务体系，加速大模型应用落地 | 22 | | 2.7 应用：积极探索人工智能应用场景，加速智能对于业务发展的价值转化 | 23 | | 第三章 人工智能算 ...

大模型应用落地白皮书 企业 转型行动指南 CONTENTS 目录 | | | | 第一章：祛魅务实，大模型加速从探索走向落地 | | | --- | --- | | | 02 | 1.2 百舸争流，大模型服务商竞逐AI浪潮新时代 06 | 第二章：知易行难，企业落地面临的挑战与机遇 | 08 | | --- | --- | | 2.1 大模型落地面临多重挑战 | 09 | 第三章：加快AI转型，构建全方位的大模型业务落地能力 17 | 3.1 大模型业务落地能建设三阶段 | 19 | | --- | --- | | 3.2 破除落地大模型的思维误区 | 23 | | | | 4.1 大模型应用场景不断扩宽，应用日渐成熟 4.2 众多行业企业深入大模型落地实践 26 30 第五章：攻克有径， 跨越大模型落地技术难题 50 | 5.1 大模型落地部署技术步骤 | 51 | | --- | --- | | 5.2 精准选模、高效落地、持续挖掘——落地三要素 | 55 | 1.1 业务驱动，大模型助力效率飞跃，实现融合的体验创新 2.2 领先企业已从大模型成功落地中率先获益 03 13 | 第六章：信赖之选，火山引擎 ...

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].