THE ARTIFICIAL NEURAL NETWORKS (ANN) IN AUDITS: THE ARTIFICIAL NEURAL NETWORKS (ANN) IN AUDITS

Abstract

In the context of rapidly growing data volumes and stricter auditing requirements, artificial intelligence technologies—particularly Artificial Neural Networks (ANNs)—are becoming increasingly relevant in the auditing field. This study investigates the impact of expert systems, machine learning methods, and intelligent agents on audit quality.

The empirical data were collected through a structured questionnaire survey conducted among accounting and auditing consulting firms located in Astana, Aktobe, Atyrau, and Aktau. A purposive sampling method was applied to determine the sample size of 375 respondents. The collected primary data were analyzed using descriptive statistics and Ordinary Least Squares (OLS) regression analysis.

The findings reveal a significant positive correlation between the use of expert systems, machine learning, intelligent agents, and audit quality. The application of ANNs enhances the quality of audit reporting and is expected to become a dominant trend in the coming decades, as intelligent systems increasingly take over decision-making tasks.

The study recommends continuous training for accountants and auditors in ANN methodologies to improve audit quality. Furthermore, auditing firms are advised to invest in machine learning tools and expand the use of intelligent agents to assist in classifying audit objects across different categories.