Assessing the Role of Audit Analytics in Improving Efficiency and Reducing Detection Risk in Audits

Posted: Nov 21, 2018

• Julian Moore
• Hazel Stevenson
• Sophia Reed

Abstract

The contemporary audit environment is characterized by unprecedented data volumes, increasing regulatory complexity, and heightened stakeholder expectations regarding audit quality. Traditional audit methodologies, developed in an era of paper-based records and manual testing, face significant challenges in maintaining effectiveness and efficiency in this transformed landscape. Audit analytics represents a paradigm shift in how auditors approach their work, moving from sample-based testing to comprehensive data analysis. However, the academic literature has largely treated audit analytics as a collection of discrete tools rather than an integrated system that transforms the fundamental nature of audit work. This research addresses a critical gap in the literature by examining how the integration of multiple analytical approaches creates synergistic effects that transcend the capabilities of individual tools. We propose that the true value of audit analytics lies not merely in automating existing procedures but in enabling entirely new forms of risk assessment and evidence evaluation. Our investigation focuses on two primary research questions: First, how does the integrated application of machine learning and behavioral analytics affect audit efficiency metrics compared to traditional approaches? Second, to what extent can such integration reduce detection risk beyond what would be expected from the simple aggregation of individual analytical tools? The novelty of our approach stems from three key contributions. We develop a theoretical framework that conceptualizes audit analytics as an integrated system rather than a toolkit. We introduce the concept of analytical synergy to explain the non-linear benefits observed when multiple analytical approaches are combined. Finally, we provide empirical evidence from a controlled experimental design that demonstrates both the efficiency gains and risk reduction achievable through our integrated framework.