Design of an AI-Based Student Loan Risk Assessment System in Ibrahim Badamasi Babangida University, Lapai, Niger State
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Background of the Study
Access to financial aid is essential for many students, yet the risk of default on student loans poses a significant challenge for universities and lending institutions. At Ibrahim Badamasi Babangida University, Lapai, an AI‐based student loan risk assessment system is proposed to improve the evaluation of loan applicants by leveraging artificial intelligence. Traditional loan assessment methods rely on static criteria and manual evaluations, which may not fully capture the financial reliability and creditworthiness of students (Usman, 2023). AI‐based systems, however, can analyze a wide range of data points—including academic performance, socio-economic background, repayment history, and behavioral patterns—to generate a dynamic risk profile for each applicant (Fatima, 2024).
These systems employ machine learning algorithms that continuously learn and improve their predictive accuracy over time, allowing for a more nuanced assessment of risk. By automating the risk assessment process, the system reduces human bias and enhances decision-making speed. Moreover, the integration of real-time data from various sources enables a comprehensive analysis that adapts to changing economic conditions and individual circumstances (Abubakar, 2025). Despite these advantages, several challenges exist, including ensuring data quality, addressing privacy concerns, and validating the system’s predictive accuracy. The successful design of an AI‐based loan risk assessment system requires not only sophisticated algorithms but also a robust data infrastructure and clear ethical guidelines for data use. This study aims to design an AI‐driven system that provides accurate risk assessments for student loans at Ibrahim Badamasi Babangida University, ultimately improving the management of financial aid and reducing the risk of loan defaults (Usman, 2023; Fatima, 2024; Abubakar, 2025).
Statement of the Problem
The current methods used to assess student loan risk at Ibrahim Badamasi Babangida University are often subjective and lack the predictive power required to accurately forecast default risk. Traditional assessment criteria are typically static and do not account for the dynamic financial situations of students. This limitation results in misclassification of risk, potentially leading to increased default rates and financial losses for lending institutions (Usman, 2023). Additionally, manual evaluation processes are time-consuming and prone to human error, further compromising the reliability of risk assessments. The absence of a data-driven approach means that valuable insights derived from a student’s academic and socio-economic data are underutilized. Furthermore, the integration of new technologies into the loan assessment process faces resistance from stakeholders due to concerns about transparency, data security, and the ethical implications of automated decision-making (Fatima, 2024). These challenges underscore the need for a comprehensive system that leverages AI to analyze multifaceted data and deliver accurate risk assessments. This study is designed to address these issues by developing an AI‐based student loan risk assessment system that minimizes human bias, enhances predictive accuracy, and upholds ethical standards in data usage, thereby supporting more informed financial aid decisions (Abubakar, 2025).
Objectives of the Study:
- To design an AI‐based system for assessing student loan risk.
- To evaluate the predictive accuracy and reliability of the system.
- To recommend ethical guidelines for data usage in loan risk assessment.
Research Questions:
- How can AI improve the accuracy of student loan risk assessment compared to traditional methods?
- What data inputs are most critical for predicting loan default risk?
- How can ethical concerns related to automated risk assessment be addressed?
Significance of the Study
This study is significant as it proposes an innovative AI‐based student loan risk assessment system that enhances the accuracy and fairness of financial aid decisions at Ibrahim Badamasi Babangida University. By providing data‐driven insights into student creditworthiness, the research aims to reduce loan defaults and improve the sustainability of student lending programs, benefiting both the institution and its stakeholders (Usman, 2023).
Scope and Limitations of the Study:
This study is limited to the design and evaluation of an AI‐based student loan risk assessment system at Ibrahim Badamasi Babangida University, Lapai, Niger State, and does not extend to other financial evaluation systems.
Definitions of Terms:
- AI-Based System: A technology solution that uses artificial intelligence to process and analyze data (Usman, 2023).
- Loan Risk Assessment: The process of evaluating the likelihood that a borrower will default on a loan (Fatima, 2024).
- Predictive Accuracy: The degree to which a model accurately forecasts outcomes based on historical data (Abubakar, 2025).
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