Subject. This article studies the trends in the development of artificial intelligence technologies in the field of internal audit. Objectives. The article aims to highlight the main trends in the development of artificial intelligence technologies in the field of internal audit, substantiating the prospects for the application of such technologies in the context of the development of a new concept of internal audit. Methods. For the study, we used analysis and synthesis, induction and deduction, systems approach, and the computational and graphic, and accounting and analytical methods. Results. The article reveals the prerequisites for the discrepancy between business expectations aimed at obtaining advisory support in making management decisions and the existing paradigm of internal audit, focused on testing the internal control and risk management system. The analysis of the existing machine learning methods in the context of its potential applicability in the internal auditor’s work helped identify key areas for using machine learning to improve the efficiency of performing audit procedures. Conclusions and Relevance. Machine learning as a field of knowledge of the theory of artificial intelligence can help automate the routine processes of internal audit and orient this function in the field of information and consulting support for business, contributing to its shifting away from control and audit activities. The use of individual elements of artificial intelligence technology contributes to improving the efficiency of internal audit. The results of the study can be used both for practical application by internal audit services, and for the development of the theory and practice of this field of knowledge.
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