AI-Based Digital Transformation as a Driver of Individual Taxpayer Compliance - Kamil, Islamiah; Prihanto, Hendi; Yolifiandri, Yolifiandri (Universitas Kristen Maranatha, 2025)
Purpose – This study aims to examine how Artificial Intelligence (AI)-based digital transformation in tax administration contributes to individual taxpayer compliance in Indonesia. The urgency of this research arises from the rapid adoption of AI technologies particularly e-filing and e-billing systems that are designed to enhance efficiency in tax processing, taxpayer monitoring, and the enforcement of tax regulations. Despite this advancement, limited empirical studies have explored the effectiveness of AI-based tools in fostering compliance among individual taxpayers. Design/Methodology/Approach – A quantitative research approach is employed, utilizing survey data collected from individual taxpayers who use digital tax services in the Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek) regions. The study applies statistical analysis techniques to assess the relationship between AI-based digital transformation variables (such as automation, accessibility, accuracy, and user experience) and individual taxpayer compliance indicators, including timeliness, accuracy of reporting, and adherence to tax obligations. Results – Preliminary findings indicate that AI-based digital transformation particularly through the integration of e-filing and e-billing systems significantly enhances taxpayer compliance by reducing human error, improving accessibility, and increasing taxpayers’ trust in the system. The analysis reveals that automation and real-time feedback features play a crucial role in ensuring more consistent and accurate tax reporting. However, challenges such as digital literacy gaps and system reliability still affect adoption rates among older taxpayers and small business owners. Research limitations/Implications – This research is limited to individual taxpayers in urban areas and may not fully capture compliance behaviors in rural regions with lower digital infrastructure. Future studies are encouraged to expand the sample coverage and include comparative analysis across different taxpayer segments. The findings have practical implications for policymakers and the Directorate General of Taxes (DGT) to strengthen AI adoption strategies that promote voluntary compliance while maintaining fairness and transparency. The study contributes to the growing body of literature on digital taxation by providing empirical evidence on the role of AI in shaping more efficient, accountable, and citizen-oriented tax administration in the digital era. Keywords: Artificial Intelligence (AI), E-Filling, E-Billing, Tax Digitalization, Taxpayer Compliance