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arXiv · 2509.15044

Credit Card Fraud Detection

Iva Popova·Hamza A. A. Gardi
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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.

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Iva Popova, Hamza A. A. Gardi. 2025-09-18. Credit Card Fraud Detection. https://arxiv.org/abs/2509.15044

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