THE USE OF ARTIFICIAL INTELLIGENCE IN DETECTING CROSS-BORDER TAX EVASION: REGULATORY GAPS AND CASEBASED EVIDENCE

Abstract

The evolution of tax laws, increased financial transactions, growing data and higher 
expectations from taxpayers for government efficiency and transparency are just some of the 
challenges taxation systems around the world face in the era of advanced technology. The complex 
tax systems are faced with new dilemmas, which are directly connected to digitalization. To 
address these challenges, tax authorities have turned toward the use of Artificial Intelligence (AI). 
In the past five years, we have witnessed the increased use of AI in international tax enforcement. 
Across the world, governments and tax authorities rely on algorithms to detect cross border tax 
fraud more than ever before. AI is also used to improve tax compliance and optimize audit 
targeting. With the vast expansion of economies and complex financial transactions, AI tools offer 
serious benefits in automatization of tax administration, identification of anomalies and 
improvement of transparency. However, the use of AI in detecting cross-border financial crimes 
still remains problematic. Even though the use of AI brings advanced solutions regarding pattern 
recognition and risk analysis across jurisdictions, it also raises legal, ethical, and procedural 
challenges for tax authorities. Cross-border tax evasion has long posed a significant challenge to 
global financial transparency and tax justice. In this context, Artificial Intelligence (AI) can be 
used as a powerful tool in detecting and combating tax evasion. Yet, while AI holds transformative 
potential, its use remains underdeveloped, mostly due to the lack of regulatory frameworks. This 
paper analyses the role of AI in detecting cross-border tax evasion, examines existing regulatory 
gaps, and analyzes key case-based evidence regarding the use of AI in this domain.