AI-Enabled Compliance Monitoring and Adherence to Financial Regulations in Nigeria's Federal Public Sector

Compliance with financial regulations is central to public accountability, yet monitoring adherence across ministries, departments and agencies is difficult at the scale and complexity of Nigeria's federal public sector. This study examines whether documented adoption of artificial intelligence (AI)-enabled compliance monitoring is associated with adherence to financial regulations in Nigeria's federal public sector. A balanced panel of five federal institutions, the Federal Ministry of Finance, the Office of the Accountant-General of the Federation, the Federal Inland Revenue Service, the Central Bank of Nigeria and the Bureau of Public Procurement is observed over 2015–2024. Documentary evidence from annual reports, audit-related documents and AI-implementation documents is scored on a five-point (0–4) rubric and analysed with pooled, fixed-effects, trend-adjusted, first-difference and lagged regressions, interpreted through agency theory. AI-enabled compliance monitoring is positively and significantly associated with regulatory compliance (pooled β = 0.719, R² = 0.923; fixed-effects β = 0.742, within R² = 0.858). The association remains positive and significant after controlling for a common time trend (β = 0.629), in first differences (β = 0.554) and when monitoring is lagged one year (β = 0.657). A conservative reading is that one additional point of monitoring adoption is associated with roughly 0.55 to 0.65 points higher regulatory compliance. Because the design is observational, the measures are documentary indices, and the sample is small and purposively selected, the results indicate association rather than proven causation and are interpreted for the five institutions examined rather than for all federal ministries, departments and agencies. The findings support investment in AI-enabled monitoring as one component of a functioning compliance chain, accompanied by data quality, regulatory clarity, staff capacity, governance arrangements and human review.