<![CDATA[This study examines the effects of artificial intelligence (AI) technologies on audit quality and investigates whether predictive analytics strengthens these relationships. The study focuses on auditors working in Public Accounting Firms (KAP) in DKI Jakarta. Using purposive sampling, 481 auditors participated in the survey. A quantitative approach was employed, with primary data collected through questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4. The results show that machine learning, deep learning, natural language processing (NLP), data mining, and robotic process automation (RPA) have positive and significant effects on audit quality. The structural model explains 61.0% of the variance in audit quality (R² = 0.610), indicating moderate explanatory power. Among the AI technologies, NLP has the strongest effect on audit quality (? = 0.277, p < 0.001). Predictive analytics significantly strengthens the effects of machine learning, deep learning, NLP, and data mining, but its moderating effect on the relationship between RPA and audit quality is not significant.The novelty of this study lies in examining multiple AI technologies, with predictive analytics as a moderating variable, within a single framework. The findings extend Agency Theory and the Technology Acceptance Model (TAM) by demonstrating how predictive analytics enhances the effectiveness of AI technologies in improving audit quality. This study contributes to the AI auditing literature by providing empirical evidence of the complementary role of predictive analytics in strengthening AI's impact on audit quality. The findings are limited to auditors in DKI Jakarta, which may restrict their generalizability.]]>
