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<p>Large Language Models (LLMs) are reshaping financial analytics, enabling automated summarization, sentiment analysis, numerical reasoning, and decision support. In this survey, we provide a clear and structured overview of LLM adoption in finance. We introduce a taxonomy of key tasks, including text processing, forecasting, and question answering, and review core architectures and adaptation strategies for both general-purpose and finance-specific LLMs. We compare performance trade-offs across models, examine evaluation metrics relevant to financial applications, and discuss deployment challenges such as data privacy, bias, and explainability. Our multi-level adoption framework offers practical guidance for balancing accuracy, cost, and privacy across institutions. Finally, we outline future research opportunities, including cross-lingual modeling, symbolic reasoning, and open finance benchmarks. This survey aims to help researchers and practitioners responsibly leverage LLMs for more transparent, effective, and inclusive financial AI. </p>

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Bridging finance and AI: a comprehensive survey of large language models in financial system

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Ameer Tamoor Khan,
  • Shuai Li,
  • Xinwei Cao

摘要

Large Language Models (LLMs) are reshaping financial analytics, enabling automated summarization, sentiment analysis, numerical reasoning, and decision support. In this survey, we provide a clear and structured overview of LLM adoption in finance. We introduce a taxonomy of key tasks, including text processing, forecasting, and question answering, and review core architectures and adaptation strategies for both general-purpose and finance-specific LLMs. We compare performance trade-offs across models, examine evaluation metrics relevant to financial applications, and discuss deployment challenges such as data privacy, bias, and explainability. Our multi-level adoption framework offers practical guidance for balancing accuracy, cost, and privacy across institutions. Finally, we outline future research opportunities, including cross-lingual modeling, symbolic reasoning, and open finance benchmarks. This survey aims to help researchers and practitioners responsibly leverage LLMs for more transparent, effective, and inclusive financial AI.