Comparative Analysis of Workers' Compensation Commission Decisions Using Large Language Models

Music City Center 

In today's rapidly evolving AI landscape, advanced technologies such as Large Language Models (LLMs) present significant opportunities for transforming workers' compensation processes. This research leverages LLMs to analyze case records—often consisting of extensive textual data—and to predict the decision. By classifying and identifying nuanced patterns in these records, LLMs offer improved accuracy and efficiency compared to conventional analytic methods. The study addresses a critical gap in the workers' compensation domain, where comprehensive text analysis has historically been limited by the complexity and scale of available data. The research uses a publicly available dataset from the North Carolina Industrial Commission, wherein disputed workers' compensation claims are resolved through an administrative law process rather than traditional court litigation. The insights gained from this investigation have broad implications for actuaries, insurance companies, and legal professionals, highlighting how AI-driven techniques can streamline claims management, enhance predictive modeling, and contribute to fairer and more transparent outcomes. By demonstrating the utility of LLMs for classifying case decisions, this research underlines the transformative potential of AI in modernizing the workers' compensation sector.

Text analysis