Large Language Models for Financial Knowledge Extraction Analytical Insights and Corporate Planning Support

Abstract

Large language models (LLMs) have emerged as transformative technologies in financial services, demonstrating unprecedented capabilities in extracting structured knowledge from unstructured financial documents, generating analytical insights, and supporting strategic corporate planning decisions. This review paper examines the comprehensive applications of LLMs including GPT-4, Claude, PaLM, and domain-specific financial models in automating knowledge extraction from diverse sources including earnings calls, financial reports, regulatory filings, and market commentary. We analyze how transformer-based architectures (TA) leverage attention mechanisms and contextual embeddings to understand complex financial terminology, temporal relationships, and causal connections in financial narratives. The paper explores advanced techniques including prompt engineering, few-shot learning, retrieval-augmented generation (RAG), and fine-tuning strategies that adapt general-purpose LLMs to specialized financial tasks. We examine applications in sentiment analysis of financial texts, automatic summarization of lengthy reports, entity recognition for companies and products, relationship extraction between financial events, and question-answering systems for financial queries. The review investigates how LLMs generate analytical insights through scenario analysis, trend identification, risk assessment, and competitive intelligence synthesis. We analyze corporate planning support applications including strategic initiative identification, market opportunity analysis, resource allocation recommendations, and investment thesis generation. Furthermore, we discuss integration architectures combining LLMs with structured databases, time-series models, and visualization tools to create comprehensive decision support systems. The paper addresses critical challenges including hallucination mitigation, accuracy verification, regulatory compliance, data privacy concerns, and the need for human oversight in high-stakes financial decisions. We examine evaluation methodologies for financial LLM applications, including domain-specific benchmarks, expert assessment protocols, and real-world performance metrics. Through synthesis of current research and deployed systems, we identify limitations including computational costs, update frequency challenges, bias in training data, and difficulties in explaining model reasoning. The review concludes by outlining promising research directions including multimodal financial analysis, real-time information integration, federated learning for privacy-preserving collaboration, and neuro-symbolic approaches combining neural language understanding with formal financial reasoning.