Developing a predictive audit risk index using multivariate analytics for cross sectoral analysis

The growing complexity of global financial reporting and regulatory oversight has underscored the need for more advanced, data-driven approaches to audit risk assessment. This study develops a Predictive Audit Risk Index (PARI) that applies multivariate analytics to enhance the precision and consistency of risk evaluation across multiple industry sectors. Using empirical data from the banking, insurance, and capital markets industries, the research employs statistical and machine learning models including logistic regression, principal component analysis, and random forest algorithms to identify, quantify, and predict areas of heightened audit risk. The proposed framework enables external auditors to move beyond traditional judgment-based techniques toward intelligent, evidence-based audit planning and resource allocation.

The findings reveal that the PARI model significantly improves the detection of risk anomalies and facilitates comparative analysis of audit risk patterns across sectors. By integrating predictive analytics within established professional standards such as US GAAS, PCAOB, and IFRS, the study demonstrates how emerging technologies can strengthen compliance assurance, transparency, and audit quality. This research contributes to the evolving field of Intelligent Audit Transformation and Predictive Risk Assessment, offering a scalable and practical model that supports the modernization of external audit practices in a rapidly digitalizing financial landscape.