THE IMPACT OF ARTIFICIAL INTELLIGENCE ON AUDIT QUALITY AND EFFICIENCY

This study explores the extent to which AI-driven tools such as machine learning, natural language processing, and data analytics enhance auditors’ ability to detect anomalies, assess risks, and provide deeper insights into financial statements. AI’s capacity to process vast datasets in real time reduces human error, strengthens fraud detection, and enables auditors to focus on judgment-intensive tasks, thereby improving audit quality. Moreover, automation of repetitive audit procedures accelerates workflow, minimizes costs, and enhances overall efficiency. However, the adoption of AI also raises concerns about data security, auditor independence, ethical implications, and the need for continuous skill development. This paper argues that while AI does not replace professional skepticism and human judgment, it serves as a powerful enabler that reshapes auditing practices toward greater reliability, transparency, and efficiency. The findings contribute to ongoing debates on the future of auditing and provide practical insights for regulators, practitioners, and stakeholders.