Affiliations:
Department of Accounting, College of Business Administration, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia
The aim of this study is to explore the impact of artificial intelligence (AI) on internal audit analytics in the private sector of the Kingdom of Saudi Arabia. The sample consisted of 370 internal audit professionals, including audit managers, auditors, data analysts, and financial and accounting staff. A descriptive–analytical approach was adopted, and data were collected using a questionnaire. The results show that the adoption of AI technologies significantly enhances internal auditing processes in Saudi private-sector companies. In particular, descriptive analytics, diagnostic analytics, machine learning, robotic process automation, predictive analytics, and natural language processing all have notable effects on auditing practices. Advanced techniques such as machine learning and predictive analytics are especially effective in identifying discrepancies and improving the proactive role of auditing. In contrast, descriptive and diagnostic analytics, as well as process automation, mainly improve efficiency, speed, and error reduction. Based on these findings, the study recommends increasing investment in AI technologies and integrating them into internal audit strategies. It also highlights the importance of providing specialized training programs to help audit professionals effectively use advanced analytical tools.
Artificial intelligence in auditing, Internal audit analytics, Machine learning applications, Predictive analytics, Saudi private sector
https://doi.org/10.21833/ijaas.2026.03.024
Almahuzi, A. S. (2026). Artificial intelligence and its influence on internal audit analytics. International Journal of Advanced and Applied Sciences, 13(3), 234–243. https://doi.org/10.21833/ijaas.2026.03.024