DIP: 18.02.054/20251003 
DOI: 10.25215/2455/1003054 
Abstract
The auditing profession is undergoing a transformative shift as artificial intelligence (AI) moves from the periphery to the core of financial assurance processes. This paper explores the evolution of auditing practices from traditional, ledger-based methodologies to modern, intelligent systems driven by AI. It examines how machine learning algorithms, natural language processing, and data analytics are reshaping risk assessment, fraud detection, and decision-making within audit frameworks. While conventional audits rely heavily on sampling and retrospective analysis, AI-powered tools enable real-time examination of entire datasets, enhancing both efficiency and accuracy. The research delves into current applications of AI in auditing, including anomaly detection, predictive analytics, and the automation of repetitive tasks such as journal entry testing and compliance checks.
In addition, the paper evaluates the implications of these advancements on auditor roles, ethics, and regulatory compliance. It presents findings from case studies and industry surveys that highlight both the potential and limitations of AI integration. Challenges such as data governance, model transparency, and the need for professional skepticism in interpreting AI outputs are critically assessed. Finally, the study proposes a strategic framework for organizations to responsibly adopt AI technologies while maintaining audit integrity and stakeholder trust.
By bridging the gap between emerging technology and established audit principles, this research underscores the importance of a balanced approach to innovation—one that leverages AI’s capabilities without compromising professional judgment or accountability. As AI continues to evolve, auditors must adapt, not only to new tools but to new paradigms of evidence, assurance, and trust in the digital age.
Keywords
- Artificial Intelligence (AI)
- Auditing
- Machine Learning
- Audit Automation
- Risk Assessment
- Fraud Detection
- Data Analytics
- Intelligent Systems
- Audit Innovation
- Financial Assurance
- Predictive Analytics
- Audit Technology
- Auditor Roles
- Ethics in AI
- Digital Transformation in Auditing
- AI Governance
- Continuous Auditing
The author(s) appreciates all those who participated in the study and helped to facilitate the research process. 
The author(s) declared no conflict of interest. 
ISSN 2455-670X