  This study explores the innovative use of Large Language Models (LLMs) as
analytical tools for interpreting complex financial regulations. The primary
objective is to design effective prompts that guide LLMs in distilling verbose
and intricate regulatory texts, such as the Basel III capital requirement
regulations, into a concise mathematical framework that can be subsequently
translated into actionable code. This novel approach aims to streamline the
implementation of regulatory mandates within the financial reporting and risk
management systems of global banking institutions. A case study was conducted
to assess the performance of various LLMs, demonstrating that GPT-4 outperforms
other models in processing and collecting necessary information, as well as
executing mathematical calculations. The case study utilized numerical
simulations with asset holdings -- including fixed income, equities, currency
pairs, and commodities -- to demonstrate how LLMs can effectively implement the
Basel III capital adequacy requirements.
  Keywords: Large Language Models, Prompt Engineering, LLMs in Finance, Basel
III, Minimum Capital Requirements, LLM Ethics
