Financial Statement Fraud Detection Using Large Language Models
Last Update: February 17, 2024, Ceteris Paribus – The Undergraduate Journal of Economics at UNC-Chapel Hill
We propose a novel financial statement fraud detection model leveraging recent advances in Large Language Models (LLMs), specifically LLaMA2. By integrating deep learning techniques with domain-specific financial expertise, we demonstrate significant enhancements in the detection of fraudulent activities. Unlike prior studies, our approach utilizes LLMs to systematically analyze a comprehensive combination of numerical and textual data extracted from annual 10-K financial statements. Our findings highlight the potential of LLMs to substantially improve predictive accuracy, offering practical value to auditors, regulators, and investors.