ARTIFICIAL INTELLIGENCE AND AUDIT QUALITY: AN EMPIRICAL LITERATURE REVIEW FROM SCOPUS DATABASE

This study aims to explore the development and influence of Artificial Intelligence (AI) on audit quality through a comprehensive empirical literature review. Utilizing a qualitative research approach, the study systematically analyzes 325 peer-reviewed articles published in Scopus-indexed journals between 2010 and 2025. The selection was based on the keyword “Artificial Intelligence on Audit Quality” and focused exclusively on finalized, English-language scientific journal articles. The findings reveal a significant rise in scholarly interest in AI applications in auditing, particularly over the past five years, with AI being applied to enhance fraud detection, real-time data analysis, and risk assessment. However, the results also highlight substantial gaps, including limited auditor readiness, ethical and regulatory concerns, and uneven research distribution across regions and institutions. The conclusions emphasize that while AI holds transformative potential for auditing, its optimal impact requires integration with human expertise, ethical frameworks, and regulatory oversight. The study’s implications suggest that future research should prioritize explainable AI, interdisciplinary collaboration, and broader global participation to ensure equitable and effective adoption. Overall, this review provides critical insights for researchers, practitioners, and policymakers on aligning AI innovations with the strategic and ethical demands of modern auditing to improve audit quality and public trust in financial reporting