Abstract
This research aims to investigate the impact of adopting Artificial Neural Networks (ANNs) as a prominent application of artificial intelligence in enhancing and improving the quality of internal auditing in the banking sector. The research problem stems from the challenges facing traditional auditing methods in light of the influx of Big Data and the complexity of financial operations, necessitating the search for advanced technological tools that ensure the accuracy and speed of supervisory performance. The study adopted a descriptive-analytical approach, whereby the research instrument (a questionnaire) was designed and distributed to a purposive sample of (140) auditors and accountants and IT unit officials working in a group of local banks (Rafidain Bank, Rasheed Bank, Al-Taif Islamic Bank, and the National Bank of Iraq). To analyze the data and test the hypotheses, descriptive and inferential statistical methods were used using advanced statistical packages. The research reached a number of conclusions, most notably the existence of a statistically significant correlation and effect between the adoption of artificial neural networks and the improvement of internal audit quality. These technologies have contributed to increasing the efficiency of detecting complex fraud patterns, reducing the time and effort spent on routine tasks, and demonstrating a superior ability to predict financial risks based on non-linear data processing. The results also showed that the supervisory staff in the banks studied possessed a good level of knowledge and preparedness to work with these intelligent systems.