Artificial Intelligence Adoption in Financial Reporting and Audit Quality: Evidence from Nigerian Listed Firms

This study examines the effect of artificial intelligence (AI) adoption in financial reporting on audit quality in an emerging market context, using evidence from Nigerian listed firms. Drawing on the governance substitution perspective, complemented by institutional and technology diffusion theories, the study argues that AI-enabled reporting systems enhance audit quality by strengthening firm-level governance mechanisms. Using a mixed-method quantitative design, the study combines content analysis of corporate annual reports with archival audit data and applies Structural Equation Modeling (SEM) to test both direct and indirect relationships. AI adoption is operationalized through a disclosure-based AI adoption index, while audit quality is modeled as a latent construct reflected by financial restatements and audit fees. The results reveal that AI adoption is positively associated with audit quality and that this relationship is partially mediated by improvements in reporting transparency and internal control quality.

The These findings suggest that AI adoption enhances the reliability of financial reporting and the effectiveness of audits by reducing information asymmetry and strengthening internal monitoring processes. The study contributes to the literature by providing firm-level evidence from an underexplored emerging market setting, advancing methodological approaches to measuring AI adoption, and offering policy-relevant insights for regulators, audit firms, and corporate boards. Overall, the findings highlight AI adoption in financial reporting as a governance-enhancing mechanism with significant implications for audit quality in emerging economies.