arXiv:2503.22693v2 Announce Type: replace-cross Abstract: The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing, particularly within the financial sector. Financial data is often embedded in intricate relationships across textual content, numerical tables, and visual charts, posing challenges that traditional methods struggle to address effectively. However, the emergence of LLMs offers new pathways for processing and analyzing this multifaceted data with increased efficiency and insight. Despite the fast pace of innova
The rapid development and maturation of large language models are creating new capabilities that were previously unattainable for complex data analysis, particularly in finance.
This development allows for more efficient and insightful analysis of multifaceted financial data, potentially leading to better investment decisions and risk management.
Traditional financial analysis methods facing limitations with intricate, multi-format data are now being augmented or replaced by LLM-powered approaches.
- · Financial institutions adopting LLM technology
- · Data science and AI firms specializing in finance
- · Investors with access to advanced analytical tools
- · LLM developers
- · Traditional financial data analysis firms
- · Analysts reliant solely on legacy methods
- · Institutions slow to adopt AI
Increased accuracy and speed in financial market predictions and risk assessments.
Democratization of sophisticated financial analysis tools, potentially leveling the playing field for smaller firms.
Enhanced market efficiency and reduced arbitrage opportunities as information asymmetries diminish.
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Read at arXiv cs.AI