Artificial Intelligence in Auditing: A Systematic Literature Review on Applications, Challenges, and the Future of AI-Driven Audit Practices
Keywords:
Artificial Intelligence, Auditing, Machine Learning, Robotic Process Automation (RPA), Fraud Detection.Abstract
The integration of Artificial Intelligence (AI) into the auditing profession has sparked a transformative shift in how audits are conducted, offering enhanced accuracy, efficiency, and fraud detection. This systematic literature review (SLR) examines the applications, challenges, and future directions of AI-driven auditing practices. AI technologies such as machine learning, robotic process automation (RPA), and natural language processing (NLP) are increasingly used to automate repetitive audit tasks, identify anomalies, and improve risk assessment, allowing auditors to focus on more strategic decisions. However, challenges such as data privacy, the interpretability of AI models, and concerns over algorithmic bias persist, hindering the full-scale adoption of AI tools in auditing. This review synthesizes key findings from recent research on AI-based auditing, addressing its applications in fraud detection, audit quality enhancement, and risk management. The results highlight the potential benefits, including increased efficiency and real-time monitoring capabilities, as well as the ethical and regulatory challenges that must be overcome. Additionally, the study explores how AI is reshaping auditor roles and emphasizes the importance of technological readiness and training to ensure effective implementation. The findings suggest that while AI has the potential to revolutionize auditing, addressing the challenges of trust, transparency, and regulatory frameworks will be essential for its widespread adoption.
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Copyright (c) 2025 Rimba Simandjuntak, M. Irsan Nasution (Author)
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