Integrating LLMs into Financial Data Analysis Workflows for Automated Interpretation and Insights
Abstract
The rapid growth of financial data and the increasing complexity of analytical workflows have created a need for intelligent systems capable of delivering faster, more accurate, and context-aware insights. Large Language Models (LLMs) have emerged as powerful tools for augmenting financial analytics through automated interpretation, natural language reasoning, and multi-modal data understanding. This paper explores a comprehensive framework for integrating LLMs into end-to-end financial data analysis pipelines, including data preprocessing, anomaly detection, forecasting, risk assessment, and narrative report generation. By combining structured financial indicators with unstructured market information, LLMs enable richer analytical context, automated commentary, and improved decision support. The study evaluates the effectiveness of LLM-based interpretation across various financial tasks and highlights measurable improvements in analysis speed, explainability, and user accessibility. Findings show that LLM-augmented workflows significantly reduce manual reporting overhead, enhance analytical consistency, and support real-time insights, setting the foundation for next-generation financial intelligence systems
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