Leveraging Artificial Intelligence for enhanced tax fraud detection in modern fiscal systems

The increasing sophistication of tax evasion schemes poses significant challenges to fiscal authorities worldwide, necessitating advanced technological solutions for fraud detection. This comprehensive review examines the integration of artificial intelligence (AI) technologies in modern tax administration systems, focusing on their application in detecting and preventing tax fraud. The paper analyzes various AI methodologies, including machine learning algorithms, deep learning networks, and natural language processing techniques, evaluating their effectiveness in identifying suspicious patterns and anomalies in tax-related data. Our review encompasses both theoretical frameworks and practical implementations across different jurisdictions, highlighting successful case studies and emerging challenges. The findings indicate that AI-powered systems demonstrate superior accuracy in detecting complex fraud patterns compared to traditional rule-based approaches, with some implementations showing up to 85% improvement in fraud detection rates. However, challenges persist regarding data quality, privacy concerns, and the need for continuous model adaptation to evolving fraud tactics. This review also addresses the regulatory implications and ethical considerations of implementing AI in tax administration, providing recommendations for policymakers and tax authorities to optimize their fraud detection capabilities while maintaining fairness and transparency in their operations.