Comparative Study of AI-Based and Traditional Student Loan Risk Assessment Models in Federal Polytechnic, Bida, Niger State
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Background of the Study
Student loans have become an essential part of financing higher education, but they also come with the risk of non-repayment, which can negatively impact the sustainability of loan programs. Traditional loan risk assessment models rely on credit scores, financial history, and sometimes subjective evaluations. In contrast, AI-based models can analyze a wider range of data, such as academic performance, socio-economic background, and behavioral patterns, to predict loan repayment behavior. This study will compare AI-based loan risk assessment models with traditional methods in assessing the risk of student loan default at Federal Polytechnic, Bida, Niger State.
Statement of the Problem
Traditional loan risk assessment models may not fully capture the complex factors that influence a student's ability to repay loans. AI-based models, on the other hand, can process large datasets and consider multiple variables that may be more predictive of a student’s future loan repayment capability. This study will assess the potential of AI to improve the accuracy and reliability of student loan risk assessments.
Objectives of the Study
1. To compare the accuracy of AI-based loan risk assessment models with traditional methods.
2. To evaluate the efficiency of AI-based models in predicting loan default.
3. To assess the feasibility of implementing AI-based risk assessment models at Federal Polytechnic, Bida.
Research Questions
1. How accurate are AI-based loan risk assessment models in predicting loan repayment behavior compared to traditional models?
2. What are the strengths and weaknesses of AI-based models compared to traditional risk assessment methods?
3. What impact will the adoption of AI-based loan risk assessment have on loan default rates?
Research Hypotheses
1. AI-based loan risk assessment models will outperform traditional models in predicting student loan repayment behavior.
2. The adoption of AI-based risk assessment will lead to a reduction in loan default rates at Federal Polytechnic, Bida.
3. AI-based models will identify risk factors that traditional models fail to recognize.
Significance of the Study
This study will provide valuable insights into how AI can improve student loan risk assessments and inform better decision-making for financial institutions and educational institutions offering student loans. It will also help to reduce loan default rates by improving predictive accuracy.
Scope and Limitations of the Study
The study will focus on the comparison of AI-based and traditional loan risk assessment models for students at Federal Polytechnic, Bida. Limitations include data access and privacy concerns, as well as the challenge of implementing AI models in real-world financial environments.
Definitions of Terms
• AI-Based Loan Risk Assessment: The use of artificial intelligence to analyze multiple factors that affect a student’s ability to repay a loan, providing more accurate predictions of loan default.
• Traditional Loan Risk Assessment: The conventional methods for evaluating loan repayment risk, often relying on financial history and credit scores.
• Loan Default: The failure to repay a loan as agreed, which can negatively impact the borrower’s credit score and the lender's financial health.
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