Banking Loan Fraud Prediction of using XGBoosing Machine Learning Technique

Abstract

Banking institutions face significant financial losses due to fraudulent loan activities, making early and accurate fraud detection a critical challenge. Traditional rule-based systems often fail to identify complex and evolving fraud patterns. This research proposes an intelligent loan fraud prediction framework using the Extreme Gradient Boosting (XGBoost) machine learning technique. The proposed model analyzes historical loan application data to classify transactions as legitimate or fraudulent. Key features such as applicant income, credit history, loan amount, employment status, and repayment behavior are used for model training. The XGBoost algorithm is selected due to its high predictive accuracy, robustness to overfitting, and ability to handle imbalanced datasets. Experimental results demonstrate that the proposed approach achieves superior performance compared to conventional machine learning models in terms of accuracy, precision, recall, and F1-score. The findings highlight the effectiveness of XGBoost in enhancing fraud detection systems and reducing financial risk in banking operations.