Authors
Department of Accounting, College of Business and Economics, Qassim University,
P.O. Box: 6640, 51452, Buraydah, Saudi Arabia
[email protected]
Department of Accounting, College of Business and Economics, Qassim University,
P.O. Box: 6640, 51452, Buraydah, Saudi Arabia
[email protected]
Abstract
The research problem is represented in the fact that audit firms in the Qassim region face several challenges related to the quality of external audit evidence, such as the limited sufficiency and relevance of audit evidence and the difficulty of ensuring its reliability. This may negatively affect the effectiveness of the audit process and the issuance of an independent audit opinion. This study aims to examine the impact of using artificial intelligence technologies on improving the quality of external audit evidence by measuring their effect on the sufficiency, relevance, and reliability of audit evidence. The field research methodology was adopted, whereby 120 questionnaires were distributed to external auditors working in audit firms, and 100 valid questionnaires were analyzed using descriptive statistical methods (mean, standard deviation, t-test, and Chi-square test) and inferential statistical analysis (correlation and regression). The study includes two main hypotheses concerning the impact of artificial intelligence on the sufficiency and reliability of external audit evidence. The results indicate that the majority of respondents tend to agree or strongly agree on the positive impact of artificial intelligence on the quality of audit evidence, with strong statistical significance for all statements. Furthermore, the correlation and regression analyses reveal a strong and positive relationship between the use of artificial intelligence and both the sufficiency and reliability of audit evidence, with explanatory power reaching up to 71%. Accordingly, the study recommends enhancing auditors’ training on artificial intelligence tools, developing the technological infrastructure of audit firms, integrating intelligent systems across all stages of the audit process, and establishing standardized guidelines to ensure the improvement of external audit evidence quality and to enhance the overall effectiveness of the audit process.