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.

该研究探讨了大型语言模型作为解读复杂金融法规的分析工具的创新应用。其主要目标是设计有效的提示，指导语言模型将冗长而复杂的监管文本，如巴塞尔 III 资本要求规定，压缩为简明的数学框架，然后转化为可执行的代码。这种新颖方法旨在简化全球银行机构在财务报告和风险管理系统中实施监管要求的过程。通过案例研究评估了各种语言模型的性能，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。案例研究使用了包括固定收益、股票、货币对和大宗商品在内的资产持有进行数值模拟，以展示语言模型如何有效实施巴塞尔 III 资本充足性要求。