Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges
A new survey shows foundation models are reshaping financial engineering, powering smarter AI for market prediction, trading, and data extraction. Researchers introduce a taxonomy for financial foundation models, covering language, time-series, and visual-language types. The study highlights rapid advances in bilingual and multimodal reasoning, plus agent-based financial simulation. Key challenges remain, including limited multimodal datasets, privacy issues, and high computational costs. The authors provide a comprehensive review and a resource hub for future financial AI development.
What it examines
This survey reviews financial foundation models (FFMs), including language, time-series, and visual-language models, designed for finance. It covers their architectures, training methods, datasets, and applications, aiming to highlight current progress, challenges, and future opportunities in applying large AI models to financial engineering tasks.
What it concludes
FFMs are transforming financial analysis, forecasting, and decision-making by enabling scalable, multimodal AI solutions. Key applications include market prediction, trading, and financial simulations. Future research should address data scarcity, privacy, and efficiency to improve model reliability and expand their use in real-world financial systems.
Evidence objects
Foundation models are transforming financial engineering, powering smarter, adaptable AI for market prediction, trading, and knowledge extraction. A new taxonomy splits financial models into language, time-series, and visual-language types, each for specific tasks.
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The survey spotlights a rapid shift from basic BERT-style models to advanced GPT-style and reasoning-enhanced agents, with notable breakthroughs in bilingual understanding, multimodal reasoning, and agent-based financial simulation for autonomous decision-making.
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Despite progress, challenges remain: a lack of large-scale multimodal datasets, privacy and regulatory barriers, and high computational costs hinder real-world deployment. The study offers a comprehensive roadmap and resource hub for future financial AI innovation.
key_findings bullet 3 · key_findings · validation V0
This paper offers a timely, comprehensive survey of foundation models in financial engineering, integrating financial language, time-series, and visual-language models. Its originality stems from breadth and synthesis rather than new algorithms. The unique, organized overview aids practitioners and researchers, highlighting challenges and applications, making it compelling yet moderately novel.
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Raw abstract and provenance
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.
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