BUDGET AND FINANCIAL MANAGEMENT INFORMATION SYSTEM FOR PUBLIC ELEMENTARY SCHOOLS: ANALYTICS AND PREDICTIVE INSIGHTS FOR MOOE ALLOCATION USING LINEAR REGRESSION

Public elementary schools in the Philippines face persistent challenges in managing their financial resources, primarily due to reliance on manual processes that are prone to errors, inefficiencies, and a lack of transparency. To address these concerns, this study developed a Budget and Financial Management Information System (BFMIS) tailored to the operational needs of public elementary schools. The system integrates Artificial Intelligence (AI) and linear regression analytics to automate budget planning, optimize the allocation of Maintenance and Other Operating Expenses (MOOE), and enhance financial reporting. Using Agile methodology, the system was designed with a user-centered approach and developed through iterative cycles involving continuous stakeholder feedback. Core features include modules for budget allocation, expenditure tracking, predictive budgeting, and automated report generation in compliance with the Department of Education (DepEd) financial policies. The system underwent black-box testing and was evaluated against ISO/IEC 25010 (software quality) and ISO 27001 (information security) standards. Furthermore, User Acceptance Testing (UAT) was conducted using the Technology Acceptance Model (TAM) to assess perceived usefulness, ease of use, and user satisfaction among school administrators, financial officers, and DepEd auditors. Results showed that BFMIS significantly improved accuracy in budget forecasting, enhanced accountability through audit trails and real-time dashboards, and facilitated informed financial decision-making. The integration of predictive analytics reduced human error and supported proactive planning and equitable resource utilization. This research contributes a scalable, AI-driven solution for improving financial management processes in the Philippine public education sector.