Predicting Student Fee Default in a Ghanaian Private School: A Logistic Regression Approach with Ethical Deployment

Eyram Kodzo Wilson

Keywords: fee default prediction, logistic regression, educational data science, ethical AI, Ghana

Abstract:

Fee default is a critical financial sustainability challenge for private schools in sub-Saharan
Africa, where tuition fees constitute the primary revenue stream. This study develops and
deploys a logistic regression model to predict student fee default at a private basic school in
Ghana's Volta Region, using 2,280 term-level administrative payment records spanning three
academic years (2023--24 to 2025--26). Fourteen predictor variables were identified from
routine school management records, including payment compliance indicators, first payment
behaviour, and student-level characteristics. The model achieved 89\% accuracy and a
ROC-AUC of 0.942, with compliance with the final payment policy emerging as the strongest
protective predictor. To operationalise the findings, an interactive web-based dashboard was
built using Streamlit, backed by a PostgreSQL database, and containerised with Docker,
enabling real-time default risk monitoring by school administrators. All student records were
anonymised prior to deployment in adherence to institutional data protection obligations,
demonstrating that ethical AI principles can be applied in resource-constrained educational
settings. This work shows that standard administrative data, when responsibly modelled, can
transform reactive fee collection into a proactive early warning system accessible to
under-resourced private schools across Ghana and West Africa.

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Submission Number: 9