Exploring the Role of Internal Audit in Detecting and Preventing Occupational Fraud and Embezzlement

Posted: Jun 21, 2022

• Faith Nolan
• Logan Patrick
• Olive Barrett

Abstract

Occupational fraud and embezzlement represent significant threats to organizational integrity and financial stability, with global losses estimated to exceed $4 trillion annually. The internal audit function has traditionally served as a primary defense mechanism against such fraudulent activities, yet conventional audit methodologies have demonstrated limited effectiveness in detecting sophisticated fraud schemes in their early stages. This research addresses the critical gap between traditional internal audit practices and the evolving sophistication of occupational fraud by developing an innovative computational framework that enhances detection capabilities through advanced analytics and machine learning. The conventional internal audit approach typically relies on periodic sampling, manual review processes, and predefined control testing, which often fail to identify emerging fraud patterns until substantial organizational damage has occurred. This reactive posture stems from methodological limitations in processing large volumes of data, identifying subtle behavioral patterns, and detecting collusive activities across organizational boundaries. Our research introduces a paradigm shift by integrating multiple analytical dimensions—behavioral analysis, network dynamics, and financial pattern recognition—into a unified detection system that operates in near real-time.