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Large Language Model in Financial Regulatory Interpretation

Zhiyu Cao, Zachary Feinstein

TL;DR

The paper tackles the challenge of interpreting dense financial regulations with large language models by proposing a framework that combines careful document loading, engineered prompts, and multi-step problem solving, validated on Basel III capital requirements. It systematically compares multiple LLMs, analyzes the impact of loading methods (PDF vs image) and prompts (naive vs detailed), and demonstrates accurate extraction and computation of minimal capital requirements, notably achieving high math performance with GPT-4. The findings indicate that image-based document loading and detailed prompts substantially improve accuracy in identifying risk buckets, weights, and correlations, enabling reliable translation of regulatory text into actionable code. This work has practical implications for automating regulatory compliance in banking, offering a blueprint for integrating LLMs into risk management and reporting workflows while addressing key ethical considerations.

Abstract

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

Large Language Model in Financial Regulatory Interpretation

TL;DR

The paper tackles the challenge of interpreting dense financial regulations with large language models by proposing a framework that combines careful document loading, engineered prompts, and multi-step problem solving, validated on Basel III capital requirements. It systematically compares multiple LLMs, analyzes the impact of loading methods (PDF vs image) and prompts (naive vs detailed), and demonstrates accurate extraction and computation of minimal capital requirements, notably achieving high math performance with GPT-4. The findings indicate that image-based document loading and detailed prompts substantially improve accuracy in identifying risk buckets, weights, and correlations, enabling reliable translation of regulatory text into actionable code. This work has practical implications for automating regulatory compliance in banking, offering a blueprint for integrating LLMs into risk management and reporting workflows while addressing key ethical considerations.

Abstract

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
Paper Structure (13 sections, 1 equation, 2 figures, 5 tables)

This paper contains 13 sections, 1 equation, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Literature Review: Large Language Models in Finance
  3. Framework
  4. Document Loading Methods
  5. Prompt Design
  6. Case Studies
  7. Dataset
  8. Minimal Capital Requirement Calculation
  9. Comparative Analysis across Different LLMs
  10. Document Loading Method
  11. Naive Prompt vs Detailed Prompt
  12. LLM Ethical Considerations
  13. Conclusion

Figures (2)

  • Figure 1: Visual representation of the process for interpreting financial regulation documents.
  • Figure 2: Detailed schematic of the Large Language Model used for financial document analysis.