This study examines the transformative role of Artificial Intelligence (AI) in enhancing audit quality within the financial services sector. Grounded in the Technology Acceptance Model (TAM), the research investigates five key AI components—AI-powered fraud detection, AI-based data analytics, real-time monitoring, AI-assisted risk assessment, and human–AI collaboration. A quantitative research design was adopted, and data were collected from auditors using a structured questionnaire. Hierarchical multiple regression analysis revealed that AI-based data analytics and human–AI collaboration are the most influential predictors of audit quality, while the remaining AI dimensions also contribute significantly. The model explains 71% of the variance in audit quality, demonstrating the substantial impact of AI-enabled tools on assurance effectiveness, anomaly detection, and decision accuracy. Findings highlight the need for updated audit standards, enhanced AI training, and ethical governance frameworks to support responsible adoption. The study contributes empirical evidence to AI-driven auditing and offers practical insights for audit firms and regulators
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