Advancements in Financial Analysis and Risk Management with Large Language Models

The field of financial analysis and risk management is witnessing a significant shift with the integration of Large Language Models (LLMs). Researchers are leveraging LLMs to enhance understanding of competitive markets, facilitate real-time monitoring of equity, fixed income, and currency markets, and automate business process analysis. This has led to the development of innovative approaches for competitor analysis, risk assessment, and financial insight generation. The use of LLMs is enabling more accurate and efficient analysis of financial data, and is promoting financial stability through advanced risk management. Notable papers in this area include:

  • The introduction of the Material Contracts Corpus, a publicly available dataset for empirical research on contract design and legal language.
  • The development of SECQUE, a comprehensive benchmark for evaluating LLMs in financial analysis tasks.

Sources

The Material Contracts Corpus

Language Models Guidance with Multi-Aspect-Cueing: A Case Study for Competitor Analysis

Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets

SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities

Automated Business Process Analysis: An LLM-Based Approach to Value Assessment

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