How external auditors in the Arab Republic of Egypt perceive and accept machine learning technologies in their auditing?

Abstract

Our study aimed to investigate the perspectives of external auditors on the advantages and ease of use of machine learning in ARE auditing. Through analysis, the differences between local and international audit offices in the ARE regarding the ease of handling machine learning are revealed. The study gathered information from one hundred external auditors who work for both local and international audit offices in the ARE. The questionnaire was distributed using both an online survey tool and through manual distribution during in-person interviews. The study's findings suggest that external auditors have shown limited interest in and perception of the ease of use of machine learning, and they do not have differing opinions regarding the ease of using machine learning in auditing. The main reason for the importance of the research's findings is the absence of study evidence on machine learning's apparent advantages and simplicity of usage in external audits within the ARE. Thus, this research provides new empirical information by evaluating the assessments of external auditors about the handling of machine learning in the ARE. This paper fulfills an identified need to study whether the Arabic Republic of Egypt's (ARE) external auditors confirm the benefits and usability of machine learning (ML).