Credit Card Fraud Detection Using Gaussian Mixture Model: A Probabilistic Approach for Enhanced Classification

Detection of credit card fraud has lately been considered as 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 range. Hence, this work contributes in 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 GMM technique with different number 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 a highest accuracy of 94.53%. The proposed method, despite of 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 to a complex modelling of probabilities for detection of frauds