Artificial Intelligence and Budgeting in Nigeria: Evaluating Fiscal Efficiency, Governance Challenges, and Strategic Development Implications in the Fourth Industrial Revolution
Jul 2026· Science Journal of Business and Management· 0 citations· 35 references
TL;DR
AI possesses substantial potential to transform public financial management by promoting fiscal discipline, strengthening accountability, and enhancing the effectiveness of budgeting systems, and is recommended for increased investment in digital infrastructure, human capacity development, data governance frameworks, cybersecurity measures, and ethical AI policies.
Abstract
This study examines the role of Artificial Intelligence (AI) in enhancing public financial management and budgeting systems, with particular emphasis on its implications for efficiency, transparency, accountability, and fiscal governance. The increasing complexity of government financial operations and the growing demand for evidence-based decision-making have created the need for innovative technological solutions capable of improving budgeting processes and resource allocation. The study adopts a qualitative approach based on an extensive review of scholarly literature, policy documents, institutional reports, and empirical studies relating to AI applications in public finance. The theoretical foundation of the study is anchored on the Technology Acceptance Model (TAM) and Public Choice Theory. TAM explains the factors influencing the adoption of AI technologies by public officials, while Public Choice Theory highlights how AI can reduce inefficiencies, corruption, and waste through automated monitoring and transparent decision-making mechanisms. Findings reveal that AI significantly improves budget forecasting, expenditure monitoring, fraud detection, financial reporting, and policy evaluation through the use of machine learning, predictive analytics, and intelligent automation. The study also identifies challenges such as inadequate digital infrastructure, cybersecurity risks, poor data quality, limited technical expertise, and resistance to organisational change. The study concludes that AI possesses substantial potential to transform public financial management by promoting fiscal discipline, strengthening accountability, and enhancing the effectiveness of budgeting systems. It recommends increased investment in digital infrastructure, human capacity development, data governance frameworks, cybersecurity measures, and ethical AI policies to ensure successful implementation. The study contributes to the growing body of knowledge on digital governance and provides practical insights for policymakers, public administrators, and development practitioners seeking to leverage AI for improved financial management and sustainable public sector performance.
This study examined the effect of Artificial Intelligence (AI) on public sector budgeting efficiency using selected government ministries in Nigeria as evidence. The study was motivated by the persistent challenges of budget delays, weak expenditure control, corruption, poor transparency, and inefficient financial management within the Nigerian public sector. The study specifically investigated the effect of AI on budget planning, expenditure control, transparency, accountability, and overall public financial management efficiency. A descriptive survey research design was adopted, while both primary and secondary data sources were utilized. Data were obtained through structured questionnaires, government financial reports, and relevant digital budgeting sources. Descriptive and inferential statistical tools, including correlation and regression analysis, were employed using the Statistical Package for Social Sciences (SPSS). The study was anchored on the Technology Acceptance Model (TAM) and Diffusion of Innovation Theory (DOI). Findings from the generated dataset and trend analysis revealed a strong positive relationship between Artificial Intelligence adoption and Public Financial Management Efficiency among selected ministries. Ministries with higher AI adoption levels consistently recorded better budget implementation accuracy, improved expenditure control, enhanced transparency, stronger accountability, and greater operational efficiency. The findings further indicated that AI-driven technologies such as predictive analytics, automated budgeting systems, intelligent auditing tools, and digital financial management platforms significantly improve budgeting processes by reducing human errors, strengthening fraud detection, improving decision-making accuracy, and enhancing financial monitoring systems. The study concluded that Artificial Intelligence serves as a strategic instrument for improving budgeting efficiency and public financial management performance in Nigeria. The study therefore recommended increased government investment in digital infrastructure, AI-based budgeting systems, staff training, cybersecurity frameworks, and policy support to promote effective AI integration within Nigerian public sector institutions.
Olasuyi O. Abdullahi, Odediran Samuel B, Akinsaya Aderonke S et al.· International Journal of Soc...· 0 citations
The study concludes that Nigeria can maximize the benefits of AI by strengthening legal and institutional frameworks, investing in digital infrastructure and human capital, promoting ethical AI practices, and fostering collaboration among government, academia, and the private sector to achieve sustainable national development.
Ubokuloh Okon· International Journal of Pub...· 0 citations
The increasing adoption of artificial intelligence (AI) in public-sector financial management has raised significant concerns regarding interpretability, accountability, governance alignment, and institutional transparency. Existing AI-based fiscal analytical approaches frequently emphasize predictive capability while providing limited integration with formal governance structures and public-sector oversight requirements. This study proposes a governance-centered AI consultancy framework that embeds AI-assisted fiscal analysis directly within institutional budgeting, accountability, and governance-oriented decision-support workflows. Rather than treating AI as an isolated predictive or automation technology, the proposed framework operationalizes analytical intelligence within governance-aware consultancy structures emphasizing interpretability, auditability, traceability, and institutional usability. The framework was evaluated using authentic longitudinal public-sector fiscal records obtained from the official Ministry of Finance budget performance reports of Saudi Arabia for fiscal year 2023. The experimental evaluation incorporated temporal fiscal monitoring, robustness analysis under heterogeneous budgetary conditions, and comparative assessment against conventional descriptive budgetary analysis and standalone AI-based fiscal analytical procedures. The experiments utilized quarterly governmental fiscal indicators including revenues, expenditures, deficit progression, debt accumulation, expenditure volatility, and oil and non-oil revenue behavior across multiple reporting intervals. The findings demonstrate that the proposed governance-centered framework preserves strong temporal analytical consistency (81.9%) while achieving an algorithmically computed interpretability-support score of 4.6/5, a Governance Alignment Index of 0.94, and an operational Decision Usability Index of 4.7/5 relative to the conventional statistical baseline (Logistic Regression) and standalone AI-based analytical approaches. Improvements over conventional descriptive budgetary analysis are reported separately through the governance-oriented institutional comparison. Additional validation studies showed that the Decision Usability Index and Governance Alignment Index provided the strongest predictive contributions, while Traceability Index and Temporal Support Index exhibited the strongest construct-level statistical validity evidence. Robustness analysis further showed stable governance-aware analytical behavior across heterogeneous fiscal conditions involving expenditure volatility, debt progression, and changing revenue structures. Additional statistical validation demonstrated empirical support for the Traceability Index and Temporal Support Index, while other governance metrics exhibited weaker evidence and should be interpreted primarily as governance-support indicators rather than primary predictive drivers. The findings suggest that governance-aware analytical operationalization can provide measurable value beyond standalone AI models for formula-based operational fiscal-risk categorization when supported by reproducible governance-oriented analytical procedures. Because the supervised labels represent deterministic operational fiscal-risk categories rather than independently verified fiscal anomalies, the reported results should not be interpreted as direct validation of real-world fiscal anomaly detection or financial misconduct identification.
Purpose – This systematic review examines how artificial intelligence (AI) can support strategic decision-making in private universities, the organizational conditions shaping its value, and the governance and implementation risks that constrain responsible use.Methodology – Searches were conducted in the Web of Science Core Collection, Scopus, and Google Scholar between March and May 2026, with the final update on May 31, 2026. After duplicate removal, screening, full-text assessment, and evidence appraisal, 46 substantive sources published between 1955 and 2025 were included in this review. Four additional methodological references supported the review reporting and synthesis. Because the evidence base was heterogeneous, narrative thematic synthesis was applied while distinguishing direct private university evidence from evidence transferred from general higher education, organizational decision research, and AI governance.Findings – The synthesis identifies four interconnected roles of AI: environmental intelligence, decision augmentation, strategic execution, and governance infrastructure. AI can strengthen institutional sensing, the comparison of strategic alternatives, implementation coordination, and decision traceability. However, direct empirical evidence specific to private universities is limited. Strategic value depends on data quality, organizational learning, analytical capability, decision ownership, auditability, governance capacity, strategic fit, and alignment with the institutional mission. Therefore, AI is best understood as a human-led decision-support capability rather than a substitute for institutional judgment.Research limitations – The heterogeneous corpus prevents statistical estimation of a common institutional effect, while the review is restricted to English-language sources from three search platforms. Therefore, the four-part architecture should be treated as an evidence-organizing framework rather than a validated causal model.Originality – This review integrates higher education, organizational decision-making, strategic management, and AI governance evidence into an institution-level capability architecture for responsible AI-supported strategic decision-making.
Yun-Dong Wu, Wei-Jian Kong· Artificial Intelligence in E...· 0 citations
The global rise of Artificial Intelligence (AI) and digital assets is reshaping governance systems and creating new avenues for improving public administration and fostering good governance. This study examines how AI and digital assets can strengthen public administration and enhance governance outcomes in Nigeria. Using a qualitative research approach, the study synthesizes scholarly articles, government publications, policy documents, and reports from international organizations. Grounded in the Technology Acceptance Model (TAM), the study investigates the ways in which AI technologies and digital assets improve administrative efficiency, transparency, accountability, citizen engagement, and evidence-based policymaking. Findings indicate that AI tools — such as machine learning, predictive analytics, intelligent automation, and digital decision-support systems — hold considerable promise for enhancing public service delivery and policy execution. Similarly, digital assets, including electronic records, digital identity systems, blockchain technologies, cloud computing, and e-government platforms, play a crucial role in promoting transparency, accountability, and institutional effectiveness. However, significant implementation challenges remain in Nigeria, including insufficient digital infrastructure, cybersecurity risks, a shortage of technical expertise, resistance to change within organizations, and weak regulatory frameworks. The study concludes that the effective integration of AI and digital assets can substantially strengthen public administration and good governance in Nigeria, and recommends increased investment in digital infrastructure, capacity building, cybersecurity, regulatory reform, and institutional modernization.
Charity Ngozika Nwabueze, Ogala David Polycarp· International Journal of Soc...· 0 citations
A strong positive relationship was found between the use of AI and the four dimensions of corporate administration that were investigated, thereby suggesting that the proposed integrated framework could serve as a basis for developing a theory and organizational practice for the future.
Tulika Dutta Roy· International Journal of Mod...· 0 citations
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