Evaluating the Effectiveness of Computer-Assisted Audit Techniques in Detecting Accounting Fraud

Posted: Jan 27, 2023

• Fiona Myers
• Evie Martinez
• Hayden Ellis

Abstract

This research presents a comprehensive evaluation of computer-assisted audit techniques (CAATs) in detecting sophisticated accounting fraud through the development and implementation of a novel multi-modal detection framework. Traditional CAATs have primarily focused on rule-based anomaly detection, leaving organizations vulnerable to increasingly complex fraud schemes that evade conventional detection methods. Our study introduces an innovative approach that integrates behavioral analytics, network analysis, and temporal pattern recognition with traditional financial data analysis. We developed and tested this framework using a unique dataset comprising 2,847 corporate transactions from 127 organizations over a three-year period, including both legitimate transactions and confirmed fraud cases. The methodology employs machine learning algorithms trained on multi-dimensional features extracted from financial records, employee behavior patterns, and inter-organizational transaction networks. Results demonstrate that our integrated framework achieves a 94.3