Investigation into the Use of Predictive Analytics in Student Loan Default Prediction: A Case Study of Benue State University, Makurdi (Makurdi LGA, Benue State)
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Background of the Study
Student loan systems play an important role in financing higher education, particularly in developing countries where many students rely on loans to complete their degrees. However, a growing concern is the increasing rate of loan defaults, which negatively impacts both students and financial institutions. Predicting which students are most likely to default on their loans can help in reducing this problem by enabling timely interventions and providing students with the necessary support to manage their financial responsibilities.
Benue State University in Makurdi, located in Makurdi LGA, Benue State, has a large population of students relying on government and institutional loans to fund their education. However, there has been a noticeable increase in loan defaults, which is straining the university’s financial aid system. The application of predictive analytics using historical student data can help identify patterns and factors contributing to loan defaults. Predictive models can also help forecast which students are at higher risk of default, allowing the university to take proactive measures to assist them before they default on their loans.
Statement of the Problem
The growing rate of student loan defaults at Benue State University is a significant concern, affecting both the students' ability to continue their education and the university’s financial sustainability. Current loan management practices do not incorporate predictive analytics, which limits the institution’s ability to identify high-risk borrowers early on and intervene effectively. This calls for the application of predictive analytics to enhance the management and repayment rates of student loans.
Objectives of the Study
1. To apply predictive analytics to identify students at risk of defaulting on loans at Benue State University.
2. To evaluate the effectiveness of predictive models in improving loan repayment rates at Benue State University.
3. To assess the potential of predictive analytics to support better loan management strategies at the university.
Research Questions
1. How can predictive analytics be used to identify students at risk of defaulting on their loans at Benue State University?
2. What impact can predictive analytics have on improving student loan repayment rates at Benue State University?
3. How can the findings from predictive analytics be applied to create more effective loan management strategies?
Research Hypotheses
1. Predictive analytics will be effective in identifying students at high risk of loan default at Benue State University.
2. The use of predictive analytics will significantly improve loan repayment rates at Benue State University.
3. Predictive analytics will enable the development of more effective strategies for managing student loans at the university.
Significance of the Study
This study will demonstrate how predictive analytics can be applied to enhance student loan management at Benue State University. The findings will help the university reduce loan defaults, improve financial sustainability, and provide timely support to students, thus improving the overall effectiveness of its financial aid program.
Scope and Limitations of the Study
The study will focus on the application of predictive analytics to student loan management at Benue State University, located in Makurdi LGA, Benue State. It will only cover loans provided to students at this university and will not address loans outside the academic context, such as personal loans or other financial programs.
Definitions of Terms
• Predictive Analytics: The use of statistical techniques, machine learning, and data mining to analyze historical data and predict future outcomes.
• Student Loan Default: The failure of a borrower (student) to repay a student loan according to the terms agreed upon.
• Loan Management: The process of managing and overseeing the distribution, repayment, and administration of loans.
• Machine Learning: A type of AI that allows systems to learn and improve automatically from experience without being explicitly programmed.
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