The impact of artificial intelligence technologies on improving the accuracy of compliance audits in financial institutions

Abstract This research aims to analyze the impact of artificial intelligence (AI) technologies on supporting compliance audit processes within financial institutions. The applied aspect of the research involved studying the impact of AI technologies on compliance audit accuracy at Al-Rafidain Bank and Al-Rasheed Bank's Anbar branch. Data was collected using a questionnaire distributed to 75 employees, with 69 valid questionnaires returned. To ensure measurement accuracy and the reliability of the results, the questionnaire's validity was internally verified, and the questions were reviewed by four specialized professors. Internal consistency of the variables was also assessed using Cronbach's alpha coefficient. Subsequently, the data were collected and statistically analyzed using SPSS version 25 to draw conclusions and findings. The research results showed that adopting AI technologies in compliance audit processes enhances financial reliability and transparency through early detection of deviations and monitoring compliance with internal regulations and policies. The research recommends that banks expand their use of AI tools such as machine learning, natural language processing, and robotic process automation to improve compliance audit accuracy and reduce errors and deviations. It is also preferable to provide ongoing training programs for auditors on the effective use of modern technologies, with a focus on the integration of automated analysis and professional expertise to ensure full utilization of artificial intelligence capabilities in enhancing the quality of control and compliance

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-20
DOI
https://doi.org/10.5281/zenodo.19128875
Primary Topic
Robotic Process Automation Applications
Type
article
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article

The impact of artificial intelligence technologies on improving the accuracy of compliance audits in financial institutions

Mustafa Azmitawfeeq
Zenodo (CERN European Organization for Nuclear Research)
Robotic Process Automation Applications
article

The impact of artificial intelligence technologies on improving the accuracy of compliance audits in financial institutions

Mustafa Azmitawfeeq
article en

Abstract

Abstract This research aims to analyze the impact of artificial intelligence (AI) technologies on supporting compliance audit processes within financial institutions. The applied aspect of the research involved studying the impact of AI technologies on compliance audit accuracy at Al-Rafidain Bank and Al-Rasheed Bank's Anbar branch. Data was collected using a questionnaire distributed to 75 employees, with 69 valid questionnaires returned. To ensure measurement accuracy and the reliability of the results, the questionnaire's validity was internally verified, and the questions were reviewed by four specialized professors. Internal consistency of the variables was also assessed using Cronbach's alpha coefficient. Subsequently, the data were collected and statistically analyzed using SPSS version 25 to draw conclusions and findings. The research results showed that adopting AI technologies in compliance audit processes enhances financial reliability and transparency through early detection of deviations and monitoring compliance with internal regulations and policies. The research recommends that banks expand their use of AI tools such as machine learning, natural language processing, and robotic process automation to improve compliance audit accuracy and reduce errors and deviations. It is also preferable to provide ongoing training programs for auditors on the effective use of modern technologies, with a focus on the integration of automated analysis and professional expertise to ensure full utilization of artificial intelligence capabilities in enhancing the quality of control and compliance

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 14%
Robotic Process Automation Applications
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