Examining the Impact of Artificial Intelligence on the Future of External Auditing Practices

Posted: Jul 20, 2022

• Ezra Wallace
• Adeline Ward
• Sage Franklin

Abstract

This research investigates the transformative potential of artificial intelligence in reshaping external auditing practices through a novel methodological framework that combines predictive analytics with qualitative assessment matrices. Traditional auditing approaches have largely remained unchanged for decades, relying on sample-based testing and manual verification processes that are increasingly inadequate for today's complex, data-intensive business environments. Our study introduces an innovative AI-augmented auditing paradigm that leverages deep learning algorithms for anomaly detection, natural language processing for contract analysis, and reinforcement learning for risk assessment optimization. We developed and tested this framework through a comprehensive case study involving three multinational corporations and two major auditing firms over an eighteen-month period. The results demonstrate that AI integration can improve audit accuracy by 47%, reduce false positive rates in fraud detection by 62%, and decrease audit cycle times by 38% compared to traditional methods. Furthermore, our research reveals unexpected emergent properties in AI-auditing systems, including the development of predictive risk models that identify previously undetectable financial statement anomalies. The study also addresses critical ethical considerations and regulatory challenges associated with AI implementation in auditing, proposing a novel governance framework for responsible AI adoption. These findings contribute significantly to both academic literature and professional practice by providing empirical evidence of AI's transformative potential while establishing practical guidelines for its implementation in high-stakes financial verification contexts.