内容概要：金融大模型通常被定义为AI大模型在金融领域的垂直化研发与应用，即金融行业自主研发 与应用的、具有金融特性的AI大模型，主要以数据、算法和算力（被称为人工智能的"三驾马车"、"稳 定三要素"）为技术支撑，归属于行业垂直大模型范畴。自2023年以来，中国金融大模型发展十分迅 速。数据显示，2024年中国金融大模型行业市场规模为28.66亿元，同比增长79.9%。一方面，金融机构 降本增效需求强烈，对智能投研、智能风控、智能客户服务等场景的需求集中爆发，推动大模型快速规 模化落地。另一方面，大模型技术不断突破，在参数量级、推理成本与隐私保护能力上实现同步提升， 推理成本持续下降，更好满足高频交易、财务分析等场景的实时性与合规性要求。目前，标准化产品凭 借交付效率高、部署门槛低、覆盖场景广等优势，成为金融机构首选方案，占据金融大模型主要市场份 额，2024年标准化产品占比71%，非标准化产品占比29%。企业方面，阿里云、百度智能云、商汤科 技、华为云等企业凭借深厚的技术积累、对行业的精准把握以及丰富的项目落地经验，主导了金融大模 型市场，占据大部分市场份额。2024年，阿里云，百度智能云以及商汤科技位列中国 ...

Core Insights - The rapid advancement of AI technologies, particularly with breakthroughs like DeepSeek, is leading to a significant acceleration in the iteration of AI applications, especially in the financial sector, which is poised to become a leading example of deep integration of large model technologies [1] - There are notable differences in the development of large models between domestic and international financial institutions, with international players often opting for commercial models while domestic institutions focus on open-source or self-built models [2] - Industrial banks, such as ICBC, are developing a "1+X" application paradigm for large models, which aims to enhance business capabilities through a dual integration of technology and business [3] Domestic and International Trends - International financial institutions tend to purchase external commercial large models and utilize public cloud deployment, while domestic institutions prefer self-built or collaboratively developed models with private cloud deployment [2] - The application scenarios in international finance are more diverse, focusing on core business areas like sentiment analysis and risk assessment, whereas domestic institutions are initially targeting efficiency improvements for frontline employees [2] New Application Models - ICBC has established a "1+X" model that includes a financial intelligence core and various capabilities such as knowledge retrieval and data analysis, enabling over 200 application scenarios [3] - The model allows for significant innovation in business processes, transitioning from single-scene empowerment to comprehensive business restructuring [3] Future Trends - Large models are expected to evolve into foundational infrastructure for financial services, with advancements in computing power supporting a "cloud-edge-end" AI deployment model [4] - The development of a model matrix layout is anticipated, featuring one versatile base model complemented by multiple specialized models for specific financial scenarios [5] - Regulatory bodies are expected to introduce clearer standards and guidelines for the ethical and compliant use of AI technologies in finance [6] Challenges in Implementation - Financial institutions face challenges in balancing the costs and value of AI model applications, as the demand for computational resources continues to rise [7] - The slow accumulation of high-quality data poses a significant barrier to achieving optimal AI performance, as the effectiveness of AI applications is increasingly dependent on data quality [7] - There is a notable shortage of interdisciplinary talent capable of bridging the gap between finance and AI technology, necessitating the establishment of robust talent development systems [7] Strategies for Smaller Institutions - Smaller financial institutions are encouraged to adopt a mixed model of "external collaboration + lightweight adaptation" to effectively leverage large models [9] - Focusing on core business areas and creating benchmark application scenarios can help smaller institutions maximize their resources [9] - Building a lightweight data ecosystem through distributed collaboration can address data limitations faced by smaller institutions [9] Future Development Pathways - Financial institutions should aim to enhance their intelligent infrastructure and develop a layered technical architecture to address the complexities of model development and computational infrastructure [10] - Accelerating the iteration of specialized models in vertical fields will enhance competitive advantages in core financial areas [10] - The integration of large model technologies is seen as a key driver for advancing financial services from process optimization to cognitive transformation [10]