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.
@article{arxiv.2507.18577,
title = {Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges},
author = {Liyuan Chen and Shuoling Liu and Jiangpeng Yan and Xiaoyu Wang and Henglin Liu and Chuang Li and Kecheng Jiao and Jixuan Ying and Yang Veronica Liu and Qiang Yang and Xiu Li},
journal= {arXiv preprint arXiv:2507.18577},
year = {2025}
}