基础模型（Foundation Models, FMs）是一类经过大规模预训练、具备强泛化能力的神经网络模型，其发展为金融工程开辟了全新的前沿研究方向。以GPT-4、Gemini为代表的通用基础模型，已在财务报告摘要生成、基于情绪感知的市场预测等任务中表现出优良性能。然而，诸多金融应用场景存在多模态推理、监管合规、数据隐私等领域专属的特殊要求，使得通用基础模型的落地应用受到显著限制。上述现实挑战推动了金融垂域基础模型（Financial Foundation Models, FFMs）的诞生，即专门面向金融领域构建的垂直基础模型。本文对金融基础模型开展系统性综述，构建包含三大核心模态的分类体系，具体涵盖金融语言基础模型（FinLFMs）、金融时间序列基础模型（FinTSFMs）以及金融视觉语言基础模型（FinVLFMs）。文章系统梳理了三类模型的架构设计、训练方法、数据集资源及各类实际应用场景，同时总结了当前该领域在数据可得性、算法可扩展性、基础设施条件等方面面临的核心挑战，并针对未来研究方向与发展机遇提出思考与展望。本文的研究成果既可作为理解金融基础模型领域的系统性参考资料，也可为该领域后续创新发展提供切实可行的研究路线。

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.