Governance-Centered AI Framework for Public-Sector Budgetary Decision-Making and Financial Risk Management

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.