PERAN SERTA TANTANGAN ARTIFICIAL INTELLIGENCE DAN MACHINE LEARNING TERHADAP EFEKTIVITAS AUDIT INTERNAL: A SYSTEMATIC LITERATURE REVIEW

Abstract

This study aims to explore the role, benefits, and challenges of applying Artificial Intelligence (AI) and Machine Learning (ML) to improve the effectiveness of internal audits. The study employed a systematic literature review (SLR) method, analyzing twenty selected articles relevant to the research question, which were indexed in Scopus between 2021 and 2025. The results indicate that AI and ML significantly enhance the effectiveness of internal audits by improving accuracy, accelerating the audit process, and enabling real-time data analysis to detect potential risks and anomalies. Furthermore, the application of these technologies allows auditors to focus more on strategic tasks and improves corporate transparency and compliance. However, the implementation of AI and ML also faces several challenges, such as inconsistent data quality, limitations in auditors' technical competencies, and data security risks. This research contributes to a more comprehensive understanding of the integration of AI and ML in internal audits and serves as a reference for further research and professional practice in the digital age. These findings are expected to benefit future researchers, companies adopting the technology, and internal auditors.