Credit card fraud detection using Gaussian Mixture Model: A probabilistic approach for enhanced classification
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Author |
Barakat Saad Ibrahim, Baydaa Hadi Saoudi, Yaqdhan Mahmood Hussein and Tabarek Alwan Tuib
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e-ISSN |
1819-6608 |
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On Pages
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1462-1472
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Volume No. |
20
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Issue No. |
17
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Issue Date |
December 15, 2025
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DOI |
https://doi.org/10.59018/0925166
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Keywords |
credit card fraud detection, gaussian mixture model, probabilistic classification, machine learning, fraudulent transactions, imbalanced dataset.
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Abstract
Detection of credit card fraud has lately been considered a critical task due to the highly imbalanced nature of financial transaction databases. On the other hand, the traditional classification algorithms have been poor at detecting fraudulent activities with an acceptable false-positive rate. Hence, this work contributes to a GMM based approach for fraud detection, which benefits from GMM probabilistic-based classification power for better classification results. The database used in this study is publicly available and was obtained from the Kaggle Credit Card Fraud Detection (CCFD) database. The dataset has 284,807 transactions, and only 0.17% of the cases represent fraud. This paper plans to scale the features, train the GMM technique with different numbers of Gaussian components (i.e., 2, 4, 6, 8, and 10), and evaluate their performance with several evaluation metrics. Compared with other traditional classifiers (logistic regression (92.4%), K-nearest neighbors (93.68%), decision tree (88.16%), and support vector machine (94.21%)), the proposed GMM algorithm obtains the highest accuracy of 94.53%. The proposed method, despite its high accuracy, has limitations under high-dimensional feature dependencies and optimal component selection. From the results obtained over the experimentation process, the GMM proves to be a probable, yet flexible and subservient framework for a complex modelling of probabilities for the detection of fraud.
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