The Role of Audit Analytics in Improving Detection of Anomalous Transactions in Corporate Accounts

Posted: Feb 23, 2022

• Gabrielle Ford
• Shane Matthews
• Amira Diaz

Abstract

The detection of anomalous transactions in corporate accounting systems represents a critical challenge for modern audit functions. As financial transactions grow in volume, velocity, and complexity, traditional auditing methodologies increasingly demonstrate limitations in identifying sophisticated irregularities. The conventional approach, heavily reliant on statistical sampling and manual verification, often fails to capture the nuanced patterns that characterize contemporary financial anomalies. This research addresses this gap by developing and validating a comprehensive audit analytics framework that leverages advanced computational techniques to enhance detection capabilities. Corporate financial malfeasance continues to evolve in sophistication, with anomalous transactions often designed to evade traditional detection mechanisms. These anomalies may manifest as subtle deviations from normal patterns, coordinated activities across multiple accounts, or transactions that exploit timing and relationship dynamics. The financial consequences of undetected anomalies can be substantial, ranging from regulatory penalties to significant reputational damage. Despite substantial investments in audit technology, many organizations continue to rely on methodologies that have changed little in decades, creating a critical need for innovative approaches. This study introduces a novel multi-modal analytical framework that integrates temporal pattern recognition, behavioral network analysis, and semantic transaction profiling. Unlike previous approaches that typically focus on single detection modalities, our framework synthesizes multiple analytical perspectives to create a more robust detection system.