Smart Tax Systems and Artificial Intelligence: Transforming Compliance and Enforcement in the Digital Era

Abstract

This study explores the relationship between artificial intelligence, automation, and tax compliance and enforcement, focusing on the development and implementation of smart tax systems in various countries. Faced with persistent challenges such as tax evasion, non-compliance, and operational inefficiencies, many tax administrations worldwide have adopted emerging digital technologies to modernize their systems. The study employs a quantitative research design using secondary data to analyze trends in tax-to-gross domestic product ratios, voluntary compliance rates, and enforcement outcomes before and after the implementation of artificial intelligence. The findings indicate that integrating artificial intelligence and automation into tax processes leads to substantial improvements in compliance behavior, fraud detection, and revenue collection. Countries that have implemented electronic invoicing, predictive analytics, and intelligent audit tools have achieved greater enforcement capacity and improved taxpayer services, particularly where digital infrastructure and effective governance are present. While advanced economies typically lead in digital readiness and transformation, many emerging and developing countries are also making significant progress through automation, expanding their tax base, and increasing transparency. The study concludes that adopting smart tax systems is a crucial step toward more responsive, transparent, and data-driven governance. However, their effectiveness hinges on specific measures such as tailoring implementation strategies to each country’s digital maturity, enacting clear legal frameworks to govern automated decision-making, and ensuring continuous investment in digital infrastructure, cybersecurity, and capacity-building within tax authorities.

Keywords: Artificial Intelligence, Tax Compliance, Smart Tax Systems, Automation, Revenue Collection