Integrating predictive analytics into external audit planning for intelligent risk assessment

The evolution of predictive analytics has fundamentally transformed external auditing by enhancing the efficiency, accuracy, and intelligence of audit planning. This review synthesizes theoretical foundations, methodological innovations, and practical applications of predictive analytics in external auditing, emphasizing its role in intelligent risk assessment. Drawing from interdisciplinary literature across auditing, data science, and artificial intelligence, the paper explores how predictive models such as logistic regression, decision trees, random forest, and deep learning strengthen auditors’ capacity to identify high-risk areas, optimize resource allocation, and enhance audit quality. It also discusses emerging challenges, including data governance, model transparency, ethical implications, and regulatory adaptation. The paper concludes by proposing a conceptual framework for integrating predictive analytics into audit planning and outlines a future research agenda aimed at balancing technological innovation with professional judgment to sustain audit credibility in the digital age.