Artificial Intelligence and Financial Reporting Quality in the Nigerian Public Sector

This study examined the effect of AI-enabled financial management systems, proxied by Government Integrated Financial Management Information System (GIFMIS) adoption, on financial reporting quality in the Nigerian public sector. Specifically, the study assessed the effect of GIFMIS adoption on the timeliness and completeness of financial reporting among federal Ministries, Departments and Agencies (MDAs). The study adopted a quantitative longitudinal panel research design using secondary data obtained from the annual financial reports of selected federal MDAs for the period 2014–2024. A purposive sample of 50 federal MDAs, yielding 550 MDA-year observations, was analyzed using panel regression techniques. Fixed Effects and Random Effects estimators were considered, while the Hausman specification test was used to select the appropriate model. Institutional size and budget size were included as control variables, with standard errors clustered at the MDA level. The findings revealed thatGIFMIS adoption had a statistically significant negative effect on reporting lag (β = −18.42, p < 0.01), indicating that adoption was associated with an average reduction of approximately 18.42 days in the time required to prepare and release financial reports. GIFMIS adoption also had a statistically significant positive effect on the completeness of financial reports (β = 0.142, p < 0.01), suggesting improved inclusion of required financial statement components and disclosures. The study concludes that technology-enabled financial management through GIFMIS can enhance important dimensions of financial reporting quality in the Nigerian public sector. It recommends sustained GIFMIS implementation, improved system integration, continuous technical support, and standardized reporting procedures across federal MDAs.