Large Language Model in Financial Regulatory Interpretation
This addresses the challenge for global banking institutions in streamlining regulatory implementation, though it is incremental as it applies existing LLMs to a new domain.
This study tackled the problem of interpreting complex financial regulations by using Large Language Models (LLMs) to distill texts like Basel III into mathematical frameworks for code implementation, demonstrating that GPT-4 outperformed other models in processing information and executing calculations.
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