ARDL-VECM ANALYSIS OF ARTIFICIAL INTELLIGENCE ADOPTION AND TAX REVENUE COLLECTION EFFICIENCY IN NIGERIA (2015–2025)

This study examines the influence of artificial intelligence (AI) adoption on tax revenue collection efficiency in Nigeria, drawing on quarterly time-series data spanning 2015 to 2025 (44 observations). Motivated by Nigeria's persistent fiscal challenges — including volatile revenue growth, systemic leakages, elevated collection costs, and fragmented government data infrastructure — the study employs an Auto-Regressive Distributed Lag (ARDL) bounds testing framework and a Vector Error Correction Model (VECM), augmented by Johansen cointegration restrictions and Block Exogeneity Wald tests. Government AI Readiness Index Score was used as proxy for AI adoption while Revenue Collection Efficiency is decomposed into six dimensions: Tax Revenue Growth Rate, Financial Leakage Reduction, Operational Efficiency Ratio, Data Governance Score, Taxpayer Compliance, and Transparency and Accountability. Empirical results reject all six null hypotheses, confirming a highly significant long-run equilibrium network among the variables. The error correction term of -1.5885 (p < 0.001) demonstrates an oscillatory over-correction speed of adjustment of 158.8% per period. Long-run Johansen normalization restrictions confirm that transparency and accountability (β = +32.95, p = 0.008) and financial leakage reduction (β = +0.068, p = 0.004) are significant positive drivers of fiscal expansion, while the operational efficiency ratio reveals a massive cost-compressing impact (β = -57.42, p < 0.001). A locked 1-to-1 dynamic equilibrium between data governance and taxpayer compliance confirms that data integrity directly determines behavioral compliance. In the short run, a temporary friction lag accompanies AI deployment before long-term benefits materialize. The study concludes that AI adoption has catalyzed a structural transformation of Nigeria's revenue administration, recommending aggressive scaleup of machine-learning forensic auditing, mandatory cross-institutional database synchronization, and automated predictive risk profiling to maximize sustainable fiscal performance.