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

翻译：本研究探索了将大型语言模型（LLMs）作为分析工具用于解读复杂金融监管条例的创新性应用。其主要目标是设计有效的提示词，以引导LLMs将冗长复杂的监管文本（例如《巴塞尔协议III》资本要求条例）提炼为简洁的数学框架，并随后将其转化为可执行的代码。这一新颖方法旨在简化全球银行业金融机构在其财务报告和风险管理系统中对监管要求的实施流程。本研究通过一个案例评估了多种LLMs的性能，结果表明GPT-4在处理和收集必要信息以及执行数学计算方面优于其他模型。该案例研究利用包含固定收益、股票、货币对和大宗商品在内的资产持仓进行数值模拟，展示了LLMs如何有效实施《巴塞尔协议III》的资本充足率要求。关键词：大型语言模型，提示工程，金融领域LLMs应用，巴塞尔协议III，最低资本要求，LLM伦理