International Research Journal of Education and Technology

This study explores the use of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for loan approval prediction. By analyzing key applicant data such as credit score, income, and employment status, the models predict loan eligibility with improved accuracy. ANN leverages multiple hidden layers and activation functions like ReLU, while SVM applies hyperplane-based classification for structured data. Data preprocessing techniques enhance model performance, and evaluation metrics such as accuracy, precision, and recall are used for assessment. Results highlight ANN's strength in handling complex data patterns and SVM's efficiency with smaller datasets, aiding financial institutions in risk assessment.

Key Words: Loan Prediction, Artificial Neural Network (ANN), Support Vector Machine (SVM), Deep Learning, Credit Risk Assessment, Financial Decision-Making