ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING TAX PERFORMANCE IN NIGERIA

Abstract

This study investigated the transformative role of Artificial Intelligence (AI) in enhancing tax administration in Nigeria, with a particular focus on the Federal Inland Revenue Service (FIRS). Motivated by Nigeria’s persistently low tax-to-GDP ratio, high levels of tax evasion, administrative inefficiencies, and widespread public mistrust, the research examined how AI-driven technologies can improve fraud detection, compliance monitoring, taxpayer engagement, and overall revenue performance. Anchored on the Dynamic Capabilities Theory, the study explored four core AI components: machine learning (ML), predictive analytics, robotic process automation (RPA), and natural language processing (NLP). Employing a mixed-methods research design, the study integrated quantitative analysis of administrative and performance data from FIRS with qualitative insights from policymakers, IT specialists, tax consultants, and taxpayers. Quantitative data were analyzed using regression techniques and correlation models to measure the effect of AI adoption on fraud detection, operational efficiency, and compliance outcomes. Qualitative data, drawn from interviews and document reviews, provided deeper insights into implementation challenges, ethical considerations, and institutional readiness. The study covered the period 2015 - 2025, reflecting a decade of Nigeria’s digital reform trajectory. Findings revealed that machine learning significantly enhances the accuracy of fraud detection and risk-based auditing, reducing revenue leakages. Predictive analytics was found to improve revenue forecasting and enable strategic planning by anticipating taxpayer behavior. Robotic process automation streamlined repetitive administrative tasks, thereby improving operational efficiency, reducing corruption opportunities, and freeing staff for strategic functions. Furthermore, natural language processing improved taxpayer support service delivery through automated, real-time interactions, thereby enhancing transparency, trust, and compliance rates. However, challenges such as poor data quality, inadequate digital infrastructure, limited technical capacity, and gaps in regulatory frameworks were identified as barriers to effective AI adoption. The study concludes that AI has significant potential to transform tax administration in Nigeria by improving efficiency, accountability, and compliance. It recommends investment in modern data infrastructure, capacity building for tax officials, development of robust governance and regulatory frameworks, and inter-agency collaboration to ensure effective AI integration