How Can Implementing Artificial Neural Networks Enhance the Quality of Internal Audits?

Abstract

Considering the increasing dependence on artificial intelligence in various fields, including accountability and scrutiny in a wide range, the need to develop internal audit work in economic units has shown by resorting to artificial intelligence applications, and accordingly, this study aims to know the impact of adoption of artificial neurological networks (ANN) in enhancing the level of internal audit quality in Iraqi banks, and to achieve the research goal that has developed a questionnaire of a questionnaire for this The purpose and consisting of pivotal, the first includes 11 paragraphs to measure the role and importance of artificial nervous networks, while the second consists of 12 paragraphs to measure the reality of internal audit in light of the information technology, 417 questionnaires were distributed to auditors and accountants in Iraqi banks listed in the 46 Banks,  Data analysis used the descriptive, and factor analysis to ensure the sincerity and stability of the question naive, and as well as the simple linear regression to prove the hypotheses. The findings indicated a negligible correlation of 5.5% between artificial neural networks and the quality of internal auditing, with a significance level of 0.133, much over the permissible threshold of 5% in social sciences.  Moreover, utilising neural networks does not compromise the quality of internal auditing.