A proposed Framework for Loan Default Prediction Using Machine Learning Techniques

Abstract

The accurate prediction of loan defaults is critical for the risk management strategies of financial institutions. Traditional credit assessment approaches have often relied on subjective judgment, leading to inconsistent decisions and heightened financial risk. This study investigates the application of machine learning techniques—namely Random Forest, Decision Tree, and Gradient Boosting—to predict loan defaults using customer data from the Agricultural Bank of Egypt. The research emphasizes the role of feature selection in enhancing model performance, utilizing both embedded and recursive methods to isolate key predictive attributes. Among the evaluated features, loan balance, due amount, and delinquency history emerged as the most influential, while demographic variables like gender and employment status were found to be less significant. The Decision Tree model demonstrated superior performance with an overall accuracy of 88%, a recall of 53%, and a specificity of 89%, making it the most effective among the tested classifiers. The findings highlight the importance of combining robust feature selection with interpretable models to support informed decision-making in banking.