Development of a Predictive Model for Student Loan Default Using Big Data in University of Maiduguri, Borno State
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Background of the Study
Student loan default poses significant financial challenges to higher education institutions and can adversely affect the economic stability of students. At the University of Maiduguri in Borno State, leveraging big data analytics to develop a predictive model for student loan default offers a proactive solution to mitigate these risks. By analyzing historical data on loan repayment, academic performance, employment outcomes, and socioeconomic factors, predictive models can identify patterns that signal a higher risk of default (Ibrahim, 2023). Advanced machine learning techniques, such as decision trees, neural networks, and ensemble methods, are capable of processing large volumes of heterogeneous data to forecast default probabilities with high accuracy. The integration of big data analytics into loan management systems allows for continuous monitoring and real-time risk assessment, enabling financial institutions and university administrators to implement targeted interventions to support at-risk borrowers (Chinwe, 2024). This data-driven approach not only enhances the accuracy of predictions but also facilitates personalized financial counseling and remediation strategies. Moreover, predictive models can help optimize resource allocation by identifying trends and enabling proactive adjustments to loan policies. Despite these advantages, challenges such as data privacy concerns, integration of disparate data sources, and the need for advanced computational resources persist. This study aims to develop a robust predictive model that leverages big data to forecast student loan default, thereby informing strategies to reduce default rates and improve overall financial management within the university (Olufemi, 2025).
Statement of the Problem
The current student loan management system at the University of Maiduguri is limited by its reliance on historical trends and manual monitoring processes, which are insufficient for accurately predicting loan defaults. This reactive approach results in delayed interventions and contributes to higher default rates, thereby straining financial resources and undermining institutional credibility (Adebola, 2023). Inadequate integration of various data sources—such as academic records, employment statistics, and socioeconomic indicators—further hampers the ability to develop a comprehensive risk profile for borrowers. As a result, early warning signs of financial distress among students often go undetected until it is too late to implement remedial measures. The absence of an advanced predictive model means that loan management strategies are not optimized, leading to inefficiencies and increased financial risk. Additionally, data quality issues and privacy concerns present significant barriers to the effective implementation of predictive analytics in this context. This study seeks to address these challenges by developing a big data-driven predictive model that identifies students at high risk of loan default. By utilizing advanced machine learning techniques to analyze a wide array of data, the research aims to provide early warnings and actionable insights that enable timely interventions. The goal is to reduce default rates and improve the overall financial health of the student loan program at the University of Maiduguri.
Objectives of the Study:
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To develop a predictive model for student loan default using big data analytics.
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To evaluate the model’s predictive accuracy and its effectiveness in early detection of default risk.
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To propose recommendations for integrating the model into the university’s loan management system.
Research Questions:
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How accurately can the predictive model forecast student loan default?
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What are the key predictors of loan default in the university’s dataset?
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How can the model be integrated into existing financial management processes to mitigate risk?
Significance of the Study
This study is significant as it applies big data analytics to develop a predictive model for student loan default, offering a proactive tool for financial risk management at the University of Maiduguri. The insights gained will enable targeted interventions, reduce default rates, and improve the sustainability of the student loan program. These findings provide valuable guidance for educational administrators and financial institutions aiming to enhance loan management processes through data-driven strategies (Chinwe, 2023).
Scope and Limitations of the Study:
The study is limited to the development and evaluation of a predictive model for student loan default at the University of Maiduguri, Borno State, and does not extend to other financial products or institutions.
Definitions of Terms:
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Predictive Model: A statistical model that forecasts future outcomes based on historical data.
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Student Loan Default: The failure to repay student loan obligations as agreed.
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Big Data Analytics: The analysis of large, complex datasets to derive actionable insights.
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