Evaluating the Role of Data Analytics in Enhancing Audit Procedures and Risk Assessment Accuracy

Posted: Dec 27, 2023

• Zane Warren
• Ryder Jacobs
• Talia Simmons

Abstract

The integration of data analytics into audit procedures represents a paradigm shift in how financial information is examined and risk is assessed. Traditional audit methodologies, while effective for detecting straightforward anomalies, often struggle with identifying complex patterns of financial misstatement and systemic risk in increasingly interconnected economic systems. This research introduces a novel computational framework that fundamentally reimagines audit risk assessment through the application of quantum-inspired algorithms and cross-disciplinary analytical techniques. Our approach moves beyond conventional binary classification methods to establish a continuous, multi-dimensional risk assessment model that captures the complex interdependencies within financial data. Contemporary audit practices predominantly rely on sampling techniques and rule-based analytical procedures that, while statistically sound, inherently limit the scope of examination and risk identification. The exponential growth in transaction volumes and complexity of modern business operations necessitates more sophisticated analytical approaches. Previous research has explored various data mining and machine learning applications in auditing, but these efforts have largely remained within the confines of classical computational paradigms. Our research breaks new ground by introducing quantum computational principles to audit analytics, enabling simultaneous evaluation of multiple transaction relationships and risk factors that conventional methods must analyze sequentially.