ARTIFICIAL INTELLIGENCE IN AUDIT PLANNING AND EXECUTION: ENHANCING DECISION-MAKING ACCURACY IN MULTINATIONAL COMPANIES IN NIGERIA

Abstract

This study examines the impact of Artificial Intelligence (AI) integration in audit planning and execution on decision-making accuracy in multinational companies operating in Nigeria. Specifically, it investigates how AI-driven technologies, such as data mining, machine learning, and image recognition, influence audit quality. Using a survey research design, the study collects primary data from 120 accounting firms in Nigeria that have adopted AI in their auditing processes. Descriptive statistics, correlation analysis, and Ordinary Least Squares (OLS) regression were employed to analyze the data. The results suggest that while data mining has a significant positive effect on audit quality, machine learning and image recognition have limited independent effects. The interaction between data mining and machine learning, however, shows a stronger impact on audit quality, implying that combining AI technologies enhances audit efficiency. The study highlights the need for strategic AI integration, regulatory support, and continuous capacity building in the Nigerian auditing sector to fully exploit AI’s potential. The findings contribute to the growing body of knowledge on AI in auditing and provide insights into the challenges and opportunities of implementing AI in audit processes in developing economies.