Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges

2025-07-07

Engineering

AI Summary

Financial foundation models (FFMs) face domain-specific challenges—including multimodal reasoning, regulatory compliance, and data privacy—that general-purpose foundation models cannot adequately address. To systematize FFM research, this work proposes the first tripartite taxonomy—FinLFM (language), FinTSFM (time-series), and FinVLFM (vision-language)—and comprehensively surveys advances in architecture design, training paradigms, datasets, and real-world deployment. We introduce a unified training framework integrating language modeling, time-series representation learning, and cross-modal alignment, leveraging heterogeneous financial data sources such as financial statements, market feeds, charts, and regulatory documents. Furthermore, we formally delineate the core capability boundaries distinguishing FFMs from general large language models. Key contributions include: (i) the first holistic survey of FFMs; (ii) Awesome-FinFMs—an open, dynamically updated resource repository; and (iii) a reproducible benchmark suite, a technical selection guide, and an open research roadmap.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 serves as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation. An updated collection of FFM-related publications and resources will be maintained on our website https://github.com/FinFM/Awesome-FinFMs.

Problem

• Addresses unique financial domain requirements like multimodal reasoning and compliance
• Reviews specialized financial foundation models across language, time-series, and visual modalities
• Identifies challenges in data availability, algorithmic scalability, and infrastructure constraints

Innovation

• Financial foundation models designed for domain-specific requirements
• Taxonomy includes language, time-series, and visual-language modalities
• Addresses challenges in data, scalability, and infrastructure constraints