Paper: arXiv 2405.06808

Authors: Zhiyu Cao, Zachary Feinstein

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

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper introduces an innovative application of LLMs for translating complex Basel III regulatory text into actionable mathematical formulas and code. While the underlying financial mathematics are standard regulatory calculations rather than advanced derivations, the empirical evaluation using numerical simulations across multiple asset classes offers practical value for financial engineering workflows.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Automate Basel III Regulatory<br>Interpretation & Coding via LLMs"] --> B["Data Inputs:<br>Basel III Texts & Portfolio Data<br>(Bonds, Equities, FX, Commodities)"]
    B --> C["Methodology:<br>Prompt Engineering for Regulatory<br>Distillation & Math Frameworks"]
    C --> D["Computational Process:<br>LLM Processing, Information Extraction<br>& Code Generation"]
    D --> E["Evaluation:<br>Numerical Simulations for<br>Capital Adequacy Requirements"]
    E --> F["Key Findings:<br>GPT-4 Outperforms Other LLMs<br>in Extraction & Math Execution"]

Cited References (24)

  1. Advances in neural information processing systems (2013)
  2. Text and context: Language analytics in finance (2014)
  3. Guiding pretraining in reinforcement learning with large language models (2023)
  4. Prompt Engineering in Large Language Models (2023)
  5. Text as data (2019)
  6. Machine learning for financial risk management: a survey (2020)
  7. Natural Language Processing for Financial Regulation — → 2311.08533
  8. Textual analysis in accounting: What’s next? (2023)
and 16 more
  1. Finbert: A pre-trained financial language representation model for financial text mining (2021)
  2. FinBERT: A large language model for extracting information from financial text (2023)
  3. Enhancing financial sentiment analysis via retrieval augmented large language models — → 2310.04027
  4. Bloomberggpt: A large language model for finance (2023) — link
  5. Prompt engineering with ChatGPT: a guide for academic writers (2023)
  6. Flexkbqa: A flexible llm-powered framework for few-shot knowledge base question answering (2024)
  7. Scaling relationship on learning mathematical reasoning with large language models (2023) — link
  8. Fingpt: Open-source financial large language models — → 2306.06031
  9. Mathprompter: Mathematical reasoning using large language models (2023) — link
  10. Large language models for mathematical reasoning: Progresses and challenges (2024) — link
  11. Pixiu: A large language model, instruction data and evaluation benchmark for finance (2023) — link
  12. Investlm: A large language model for investment using financial domain instruction tuning — → 2309.13064
  13. Language models are few-shot learners (2020)
  14. LLMs for Financial Advisement: A Fairness and Efficacy Study in Personal Decision Making (2023)
  15. Large Language Models in Finance: A Survey (2023)
  16. Navigating LLM Ethics: Advancements, Challenges, and Future Directions (2024) — link

4 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.