Credit Card Fraud Detection

Abstract

Credit card fraud remains a significant challenge due to class imbalance and fraudsters mimicking legitimate behavior. This study evaluates five machine learning models - Logistic Regression, Random Forest, XGBoost, K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) on a real-world dataset using undersampling, SMOTE, and a hybrid approach. Our models are evaluated on the original imbalanced test set to better reflect real-world performance. Results show that the hybrid method achieves the best balance between recall and precision, especially improving MLP and KNN performance.