Evaluating the Impact of Audit Analytics on the Efficiency and Accuracy of Audit Procedures

Posted: Jan 16, 2018

• Annalise Barrett
• Brody Steele
• Theo Watson

Abstract

This research presents a comprehensive evaluation of audit analytics implementation across diverse organizational contexts, examining both quantitative efficiency gains and qualitative improvements in audit quality. Through a multi-method approach combining longitudinal performance metrics analysis with practitioner interviews, we demonstrate that organizations implementing advanced audit analytics achieve average efficiency improvements of 42% in transaction testing cycles and 67% reduction in manual sampling requirements. More significantly, our findings reveal a paradigm shift in audit methodology: analytics-driven approaches enable continuous monitoring capabilities that transform traditional periodic audits into real-time assurance frameworks. The study introduces a novel Audit Analytics Maturity Model that categorizes implementation stages from basic automation to predictive intelligence, providing organizations with a structured framework for assessing their analytical capabilities. Our research contributes original insights by documenting how analytics not only accelerates procedural execution but fundamentally enhances auditors' cognitive processes through advanced visualization and anomaly detection. The findings challenge conventional audit sampling methodologies and propose a new framework for integrating machine learning algorithms into professional skepticism practices. This study represents one of the first comprehensive examinations of how digital transformation is reshaping the fundamental nature of audit evidence collection and evaluation in the big data era.