A Data-Driven Intelligent Financial Management Framework for Educational Institutions Using Machine Learning and Agentic AI

Student payments, receipts, bank activity, expenditures, scholarships and accounting records must be collected and verified throughout the academic year in an educational institution. A survey of school and college practices conducted for this project revealed that there were repetitive manual checks, verification and comparisons between payment confirmation, UTR or transaction-ID and bank reconciliation and report preparation. This paper proposes a single financial management workflow, comprising financial transaction processing, student fee management, automatic receipt generation, bank reconciliation, income and expenses records, information on scholarships and reporting in a tally compatible manner. Machine Learning (ML) is proposed for fee-collection forecasting, expense prediction, trend analysis and identification of unusual transactions, and a controlled Agentic AI component is proposed to interpret validated analytical results and assist administrators, with important financial actions kept under human approval. Phase-I of the project is reported in the paper which includes problem identification through institutional interaction, literature review, research gap, needs, objectives, architecture and methodology, evaluation plan. The ML and Agentic AI components have not yet been validated using institutional financial data, nor are there any reported experimental results; implementation and evaluation will be done in the coming phase.