<![CDATA[This research aims to analyze the role of artificial intelligence (AI) technologies in improving tax collection procedures within the Iraqi General Authority for Taxes during the period 2020–2024, in light of the challenges facing tax administration, such as increasing cases of tax evasion, data fragmentation, and the slowness of manual procedures. The research adopted an applied methodology combining quantitative and qualitative analysis, relying on administrative data and internal reports from the Authority, in addition to the opinions of specialists working in the collection and audit departments. The applied aspect included designing a prototype based on machine learning algorithms (such as XGBoost and Isolation Forest ) for the early detection of tax risks, analyzing anomalies in tax returns, and targeting the most likely cases of tax evasion. The research also explored the potential of using natural language processing (NLP) technologies to extract data from unstructured invoices. The results showed that integrating AI technologies into collection processes can contribute to increasing the net collection rate, reducing processing time, and improving the accuracy of detecting tax evasion cases compared to traditional methods. The research also indicated that the successful implementation of these technologies depends on data quality, system integration, and the provision of a supportive legislative and regulatory environment. The research concludes with a set of recommendations, most notably the establishment of a specialized data analysis unit within the authority, the development of a unified data infrastructure, the adoption of training programs to enhance the capabilities of workers in the field of artificial intelligence, in addition to establishing a governance framework that ensures transparency and privacy protection in the use of tax data.]]>
