Leveraging AI and Machine Learning for Fraud Detection and Compliance

Abstract

This research investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in modern taxation systems, emphasizing their role in fraud detection, compliance monitoring, and revenue optimization. By leveraging predictive models, anomaly detection techniques, and natural language processing, tax authorities can analyze large and complex datasets to uncover hidden patterns, identify suspicious activities, and forecast taxpayer behavior. The study highlights practical use cases from global tax administrations, discusses implementation frameworks for integrating AI/ML into taxation, and explores emerging trends such as blockchain and IoT-driven tax systems. While the integration of AI and ML promises improved efficiency, transparency, and fairness, challenges such as data privacy, infrastructure scalability, and ethical considerations remain critical. This paper provides an in-depth analysis of how AI and ML are reshaping tax compliance and fraud prevention, offering insights for policymakers and practitioners.