Large Language Model Agents in Finance: A Survey Bridging Research, Practice, and Real-World Deployment (2025)

Abstract: Large language models (LLMs) are increasingly applied to finance, yet challenges remain in aligning their capabilities with real-world institutional demands. In this survey, we provide a systematic, dual-perspective review bridging financial practice and LLM research. From a practitioner-centric standpoint, we introduce a functional taxonomy covering five core financial domains—Data Analysis, Investment Research, Trading, Investment Management, and Risk Management—mapping each to representative tasks, datasets, and institutional constraints. From a research-focused perspective, we analyze key modeling challenges, including numerical reasoning limitations, prompt sensitivity, and lack of real-time adaptability. We comprehensively catalog over 30 financial benchmarks and 20 representative models, and compare them across modalities, tasks, and deployment limitations. Finally, we identify open challenges and outline emerging directions such as continual adaptation, coordination-aware multi-agent systems, and privacy-compliant deployment. We emphasize deeper researcher–practitioner collaboration and transparent model architectures as critical pathways to safer and more scalable AI adoption in finance.

## taxonomy of llm-based agents in finance agent-finance taxonomy alignment. to ensure the practical relevance of our agent taxonomy, we verify its consistency with established financial workflows (appx. b). financial institutions typically operate through five specialized divisions @xcite : data analytics departments transform unstructured information into structured insights; research divisions generate investment theses and forecasts; trading operations execute market transactions; investment management teams make strategic allocation decisions; and risk management divisions ensure regulatory compliance and stability. this creates a consistent workflow where processed data becomes research insights, driving trades and portfolio strategies while undergoing continuous risk monitoring. our agent taxonomy mirrors this structure: data analysis agent corresponds to financial data processing teams; investment research agent to research departments; trading agent to trading desks; investment manager agent to portfolio managers; and risk management agent to risk divisions. as shown in figure 1 , each agent specializes in tasks from unstructured data processing to market forecasting and portfolio optimization (formalized in alg. a1). tables 1 and 2 summarize datasets, benchmarks, evaluation metrics, and stateof-the-art models, concluding with an analysis of their limitations, while ## conclusion we present this survey that systematically analyzes the deployment of large language model (llm) agents across core financial functions, including data analysis, investment research, trading, investment management, and risk management. for each functional division, we introduce representative subtasks, curated datasets, and stateof-the-art llm-based solutions, along with their practical constraints in real-world finance. to support broader adoption, we also catalog benchmark datasets covering diverse modalities and detail their coverage, licensing, and evaluation metrics. concluding the paper, we outline persistent challenges and emerging directions, including real-time adaptation, uncertainty-aware reasoning, and coordination among heterogeneous agents for future research in llm-empowered financial ai.