Impact of Data Analytics and Artificial Intelligence on Audit Quality of Listed Manufacturing Companies in Nigeria

Abstract

This study examined the impact of data analytics and artificial intelligence (AI) on audit quality of listed manufacturing companies in Nigeria. Specifically, the study investigated the effects of audit data analytics adoption, artificial intelligence utilization, and auditor technological competence on audit quality. The study was anchored on the Technology Acceptance Model and Resource-Based View Theory. An ex-post facto research design was adopted, and data were obtained from annual reports and corporate disclosures of 25 listed manufacturing companies on the Nigerian Exchange Group (NGX) covering the period 2015–2024. The study employed panel regression analysis. Audit quality was proxied by audit report quality, while data analytics adoption, AI utilization, and auditor technological competence served as explanatory variables. The findings revealed that audit data analytics adoption has a positive and significant effect on audit quality (β = 0.421, p = 0.000), artificial intelligence utilization exerts a positive and significant effect on audit quality (β = 0.387, p = 0.002), while auditor technological competence also has a positive and significant influence on audit quality (β = 0.294, p = 0.011). The model explained approximately 68.4% of the variation in audit quality (Adjusted R² = 0.684). The study concluded that data analytics and artificial intelligence significantly enhance audit quality by improving audit efficiency, fraud detection capability, risk assessment accuracy, and financial reporting reliability. The study recommended increased investment in AI-enabled audit systems, continuous auditor training, and the establishment of regulatory guidelines for technology assisted auditing.