Credit Card Fraud Detection
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914045952524288 |
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| author | Popova, Iva Gardi, Hamza A. A. |
| author_facet | Popova, Iva Gardi, Hamza A. A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Credit Card Fraud Detection Popova, Iva Gardi, Hamza A. A. Machine Learning Artificial Intelligence 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. |
| title | Credit Card Fraud Detection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.15044 |