{"ID":2869401,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15044","arxiv_id":"2509.15044","title":"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.","short_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,...","url_abs":"https://arxiv.org/abs/2509.15044","url_pdf":"https://arxiv.org/pdf/2509.15044v1","authors":"[\"Iva Popova\",\"Hamza A. A. Gardi\"]","published":"2025-09-18T15:08:14Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}