Design of a Predictive Analytics Model for Student Loan Repayment Default Detection: A Case Study of Benue State University, Makurdi (Makurdi LGA, Benue State)
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
- Reference Style:
- Recommended for :
- NGN 5000
Background of the Study
Student loans have become a popular way for students to finance their education, especially in countries where public education is underfunded or expensive. However, the challenge of loan repayment is significant, with many students failing to repay their loans on time. This leads to financial strain on universities and a rising debt burden for students. Predictive analytics, which involves using statistical techniques and machine learning models to analyze historical data and predict future outcomes, offers a promising solution for detecting potential loan repayment defaults before they occur.
In the context of Benue State University, Makurdi, a growing number of students rely on financial aid programs such as student loans to fund their studies. However, the university faces challenges in monitoring loan repayment behavior and detecting at-risk students who may default on their payments. A predictive analytics model could help the university identify high-risk students based on historical data, such as loan repayment history, academic performance, and financial status, and intervene early to prevent defaults.
This study aims to design a predictive analytics model for detecting student loan repayment defaults at Benue State University, focusing on the factors that contribute to defaults and how they can be used to predict future repayment behavior.
Statement of the Problem
Benue State University, Makurdi, faces challenges in managing student loans, particularly in identifying students who are at risk of defaulting on their loan repayments. The lack of an early warning system means that many loan defaults are not detected until they become a serious issue. As a result, the university suffers financial losses, and students are burdened with unpaid debts. This study aims to design a predictive analytics model to help the university proactively address the issue of loan repayment defaults.
Objectives of the Study
1. To design a predictive analytics model for detecting potential student loan repayment defaults at Benue State University.
2. To identify the key factors influencing loan repayment defaults among students at the university.
3. To evaluate the accuracy and effectiveness of the predictive model in forecasting loan repayment behavior.
Research Questions
1. What are the key factors influencing student loan repayment defaults at Benue State University?
2. How effective is the predictive analytics model in detecting students at risk of loan repayment defaults?
3. What interventions can be developed based on the model’s predictions to reduce loan defaults at the university?
Research Hypotheses
1. There are specific factors (such as academic performance and financial status) that significantly influence student loan repayment defaults at Benue State University.
2. The predictive analytics model will accurately identify students at high risk of defaulting on their loans.
3. Interventions based on predictive analytics can reduce the rate of student loan defaults at Benue State University.
Significance of the Study
This study will help Benue State University develop a proactive approach to managing student loan repayments, potentially reducing default rates and improving financial sustainability. The predictive model can also be adapted by other institutions to enhance their loan management systems. The research will contribute to the growing body of knowledge on using data analytics in higher education finance.
Scope and Limitations of the Study
The study will focus on the development and evaluation of a predictive analytics model for student loan repayment defaults at Benue State University, Makurdi, located in Makurdi LGA, Benue State. It will assess the model’s effectiveness in identifying high-risk students. Limitations include potential data quality issues, such as incomplete or inaccurate student financial records, which may affect the accuracy of the predictive model.
Definitions of Terms
• Predictive Analytics: The use of data analysis techniques and machine learning algorithms to predict future events based on historical data.
• Loan Repayment Default: The failure of a borrower to repay a loan according to the agreed-upon terms.
• Financial Sustainability: The ability of an institution to manage its financial resources effectively and ensure long-term financial stability.
Related Project Materials
An examination of the challenges facing adult education among Fulani herders in Kano Municipal Local Government Area, Kano State
Background of the Study
The dynamics of adult education among Fulani herders in Kano Municipal are shaped by a complex int...
USE OF INTERNET TOOLS FOR TEACHING AND LEARNING COMPUTER STUDIES
BACKGROUND OF THE STUDY
Because of the widely acknowledged potential of technology to enhance education...
An Appraisal of Semantic Change in Nigerian Political Discourse: A Study of Election Manifestos
Background of the study
Political discourse in Nigeria is dynamic, with election manifestos playing a central role in shaping public opin...
The Impact of Financial Training on Principals’ Administrative Efficiency in Secondary Schools in Ondo State
Background of the Study
Effective financial management is crucial for the success of s...
INTERNET AS A RESEARCH TOOLS FOR STUDENTS OF TERTIARY INSTITUTIONS IN NIGERIA
Background of the Study
The internet stated in august 3rd 1962 by J.C.R Lichider of MIT USA as just an ordina...
Comparative Study of Traditional and Next-Generation Firewalls in Federal University, Gashua, Yobe State
Background of the Study
Firewalls play a critical role in securing university networks by monitoring and controlling incomi...
Assessing the Role of Innovation Hubs in Driving Economic Development in Nigeria
Background of the Study
Innovation hubs have become focal points for fostering creativity, entrepreneurship, and technolog...
An Assessment of the Impact of Historical Governance Structures on Nigeria’s Fiscal Policy
Background of the Study (400 words)
Historical governance structures in Nigeria have played a formative role in shaping the...
The effect of livelihood empowerment programs on food security in Sokoto State
Background of the Study
Livelihood empowerment programs (LEPs) are designed to improve the economic stability and resilience of vulnerabl...
An appraisal of gender differences in the adoption of online learning in tertiary institutions in Bauchi East LGA, Bauchi State
Background of the Study :
The increasing integration of online learning in tertiary education has brought to light various...
