Leveraging Machine Learning for Telecom Banking Card Fraud Detection: A Comparative Analysis of Logistic Regression, Random Forest, and XGBoost Models

Abstract

In recent years, telecommunication bank card fraud has become a major threat to financial security, so it is necessary to develop robust detection mechanisms for telecommunication bank card fraud. This study examines the application of machine learning techniques (specifically logistic regression, random forests, and XGBoost) in identifying fraudulent telecom bank transactions. Using a dataset consisting of one million transaction records from the 2024 National Student Data Statistics and Analytics Competition, we implemented and evaluated these models based on key performance metrics such as accuracy, precision, recall, F1 score and ROC-AUC. The results show that XGBoost outperforms the other models, achieving superior accuracy and robustness in fraud detection, while Random Forest also performs well, achieving almost perfect classification accuracy. Logistic regression, while effective, lagged behind in terms of handling the complexity of the data. The analyses in this paper further highlight the critical role of features such as transaction amount ratios and online transaction status in predicting fraud. These findings suggest that advanced machine learning models, especially ensemble methods such as XGBoost, are highly effective in combating telecom banking fraud and should be integrated into existing detection systems to enhance their predictive capabilities.