Leveraging Artificial Intelligence for enhanced tax fraud detection in modern fiscal systems
1 Federal Inland Revenue Service, Lagos, Nigeria.
2 Department of Economics, Adekunle Ajasin University, Akungba-Akoko, Nigeria.
3 Department of Communications Studies, New Mexico State University, USA.
4 Department of Information Technology Services, Washburn University, Topeka, KS USA.
5 Department of Political Science, Tulane University, New Orleans, Louisiana, USA.
Review Article
GSC Advanced Research and Reviews, 2024, 21(02), 129-137.
Article DOI: 10.30574/gscarr.2024.21.2.0415
Publication history:
Received on 23 September 2024; revised on 02 November 2024; accepted on 04 November 2024
Abstract:
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.
Keywords:
Artificial Intelligence; Tax Fraud Detection; Machine Learning; Fiscal Systems; Pattern Recognition; Risk Assessment
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
