The Role of Large Language Model (LLM) in Automating Business Intelligence and Finance

Abstract

This study investigates the impact of Large Language Models (LLMs) on automating business intelligence and financial processes, employing a quantitative approach to analyze their efficacy in enhancing decision-making efficiency. A structured survey was conducted with 268 industry professionals, utilizing a 7-point Likert scale to assess perceptions of LLMs in financial analytics. Regression analysis reveals a significant positive relationship (R2 = 0.635, β = 0.797, p < 0.001), indicating that LLMs substantially contribute to automation outcomes. The findings highlight LLMs' role in improving analytical accuracy and operational efficiency while addressing concerns such as interpretability and integration complexities. Despite their potential, a portion of respondents remain neutral, suggesting challenges in adoption. Diagnostic tests confirm model validity, ensuring robust inferences.