Assessing the Effectiveness of Risk-Based Auditing Approaches in Detecting Corporate Fraud Schemes

Posted: Dec 14, 2022

• Reed Coleman
• Alice Newman
• Olivia Flores

Abstract

This research investigates the efficacy of risk-based auditing methodologies in identifying and preventing corporate fraud through a novel computational framework that integrates machine learning algorithms with traditional audit procedures. While risk-based auditing has gained prominence as a proactive approach to fraud detection, its effectiveness remains inadequately quantified in existing literature. Our study introduces an innovative hybrid methodology that combines natural language processing of financial disclosures, behavioral pattern analysis of transaction data, and network analysis of corporate governance structures to create a multidimensional fraud risk assessment model. We developed and tested this framework on a comprehensive dataset comprising 450 publicly traded companies across multiple industries over a five-year period. The results demonstrate that our integrated approach significantly outperforms conventional risk-based auditing methods, achieving a 34