Data-Driven Detection of Tax Evasion: Integrating AI, Machine Learning, and Analytics for Improved Compliance

Abstract

This study examines advanced tech to detect and prevent tax evasion in Jordan. It focuses on Artificial Intelligence (AI), Machine Learning (ML), and Big Data Analytics. Jordan faces significant challenges in tax compliance. It has a complex tax system and widespread evasion tactics. The research aims to assess the adoption of tech in Jordanian auditing firms. It will also evaluate its effectiveness in fighting tax evasion. A quantitative research method was used. It surveyed 200 auditors from 10 firms. The goal was to assess their views on tech adoption and its effect on tax fraud detection. The results show that AI and ML improve irregularity detection. However, Big Data Analytics has low adoption. Institutional pressure and firm size were identified as critical factors influencing technology adoption. The study concludes that Jordan must invest in three areas. It needs to improve its tax compliance framework. First, it should upgrade its tech infrastructure. Second, it should train auditors. Third, it should cooperate with tax authorities.