Online Payment Fraud Detection Using Machine Learning

Online payment fraud detection is a critical problem in the financial sector due to the increasing volume of digital transactions. This paper proposes a machine learning-based fraud detection system using CatBoost, XGBoost, and a soft voting ensemble model. Principal Component Analysis (PCA) is applied for dimensionality reduction, and SMOTE is used to address class imbalance. The models are evaluated using precision, recall, F1score, and AUC. Experimental results show that the ensemble model outperforms individual models with improved accuracy and robustness. A real-time fraud detection system is also developed using Streamlit to support both single and batch predictions. The proposed system demonstrates high efficiency and scalability for practical applications.

Fraud Detection, Machine Learning, CatBoost, XGBoost, PCA, SMOTE, Ensemble Learning

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