Applications of Big Data Analytics in Tax Compliance Monitoring: A Case Study of Rwanda’s Value-Added Tax

Abstract Most tax administrations have struggled with tax under-reporting, which has cost them greatly financially and hampered economic growth overall. For most countries, value-added tax (VAT) is the main source of domestic revenue. VAT is the major contributor to total tax revenue in Rwanda, so even a small increase in its collection can raise overall significant revenue. Researchers have attempted to address the issue of under-reporting using various techniques. The purpose of this paper is to use machine learning models to detect and predict the VAT under-reporting in Rwanda. Several evaluation criteria are used to compare different supervised machine learning models. A number of factors are shown to be more influential on VAT under-reporting than others, including cross-border businesses, taxpayers with fewer years of experience, and taxpayers in sectors such as wholesale and retail trade as well as construction. Leveraging such approaches can increase revenue mobilization as tax administrations will have a quick and innovative method of predicting VAT under-reporting in advance and identify high-risk cases for audit.