<![CDATA[The development of Artificial Intelligence (AI) has transformed tax administration from conventional monitoring systems into more predictive and data-driven risk-based compliance management. However, the complexity of multinational corporations’ activities, including cross-jurisdictional transactions and sophisticated tax planning strategies, has increased the challenges of early detection of tax non-compliance risks. Although previous studies have examined the application of AI in taxation, the existing literature remains fragmented and lacks an integrated understanding of the AI technologies utilized, the factors influencing their effectiveness, and the interrelationships among these factors within tax risk detection systems. This study aims to synthesize the literature on the role of AI in the early detection of tax non-compliance risks among multinational corporations. Using a Systematic Literature Review (SLR) approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 25 articles retrieved from the Scopus and Web of Science databases were analyzed through thematic content analysis. The findings indicate that the dominant AI technologies employed include machine learning, deep learning, natural language processing, predictive analytics, and anomaly detection. Furthermore, the study identifies four key dimensions that determine the effectiveness of AI implementation, namely AI capability, data quality and integration, organizational readiness, and the regulatory and governance environment]]>
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