AI-Driven Data Automation and Financial Reporting Timeliness and Accuracy: Evidence from Nigeria's Federal Public Sector

Late and error-prone government accounts weaken legislative oversight and citizen scrutiny. This study examines whether documented adoption of artificial intelligence (AI)-driven data automation is associated with the timeliness and accuracy of financial reports in Nigeria's federal public sector. Using a ten-year balanced panel (2015–2024) of five purposively selected 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, the study scores documentary evidence from annual reports, financial statements and official AI-implementation documents on a five-point (0–4) rubric and estimates pooled, fixed-effects, trend-adjusted, first-difference and lagged panel regressions, interpreted through agency theory. AI-driven data automation is positively and significantly associated with the composite measure of reporting timeliness and accuracy (pooled β = 0.717, R² = 0.845; within-institution β = 0.706, within R² = 0.818). The association remains positive and significant after controlling for a common time trend (β = 0.641), in first differences (β = 0.761) and when automation is lagged one year (β = 0.631). A one-point higher automation score is therefore associated with roughly 0.6 to 0.8 points higher reporting quality on the five-point scale. 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 targeted investment in AI-enabled data capture, validation and reconciliation, accompanied by governance arrangements, human review and skills development.