Updating Tax Administration Systems Using Big Data Analytics and Artificial Intelligence to Prevent Tax Avoidance for a Sustainable Nation

Abstract

This research explores how modernizing tax administration systems through Big Data analytics and Artificial Intelligence (AI) can help prevent tax avoidance and foster sustainable national development. The study employs a sequential exploratory mixed-method approach. The qualitative phase involves in-depth interviews with five senior tax consultants and academic experts to identify key mechanisms, challenges, and policy priorities in implementing AI-based tax administration. Insights from this phase inform the quantitative stage, which surveys 98 companies listed on the Jakarta Stock Exchange using structured questionnaires. Data were analyzed using both descriptive and inferential statistical methods. The findings reveal that integrating Big Data and AI substantially improves tax administration by enhancing taxpayer profiling, risk detection, and real-time monitoring, which in turn reduces tax avoidance and promotes higher compliance. However, the study acknowledges certain limitations, including sample size and institutional context, which may affect the generalizability of the results. This research contributes to the fields of tax compliance and public finance by integrating concepts of digital governance and AI-driven analytics. The results provide practical insights for tax authorities in designing intelligent, data-driven systems that improve transparency, fairness, and fiscal sustainability. Overall, this study offers empirical evidence that AI-supported tax administration serves as a strategic tool for minimizing tax avoidance and advancing sustainable national development.