This paper explores the use of Large Language Models (LLMs) as analytical tools for interpreting complex financial regulations, specifically focusing on the Basel III capital requirements. The authors develop a systematic framework that includes prompt engineering, document loading methods, and iterative task decomposition to guide LLMs in extracting key regulatory elements and performing mathematical calculations. Through a case study on market risk capital requirements, they demonstrate that GPT-4 outperforms other models (GPT-3.5, Claude-3, Gemini-1.5) in identifying risk buckets, risk weights, correlations, and executing calculations, achieving 95% accuracy in the latter. The study also finds that image-based document loading significantly improves accuracy over PDF loading, and that detailed prompts are crucial for complex tasks. The framework aims to streamline regulatory compliance for financial institutions, particularly smaller ones with limited resources. The paper concludes by discussing ethical considerations such as data privacy, transparency, and fairness, and suggests future research directions including integration with larger datasets and stress testing.