Credit card fraud detection using gaussian mixture model: a probabilistic approach for enhanced classification

Barakat Saad Ibrahim; Baydaa Hadi Saoudi; Yaqdhan Mahmood Hussein; Tabarek Alwan Tuib

Author(s)

Barakat Saad Ibrahim College of Medicine, Al Muthanna University,Samawah,Iraq

Baydaa Hadi Saoudi Department of ICT, Al-Samawa Technical Institute, Al-Furat Al-Awsat Technical University,Samawa,Iraq

Yaqdhan Mahmood Hussein Electronics and Communication Department, College of Engineering, Al Muthanna University, Samawah, Iraq;College of Engineering, Al Ayen University, Nasiriyah, Iraq

Tabarek Alwan Tuib Electronics and Communication Department, College of Engineering, Al Muthanna University,Samawah,Iraq

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.

Publication Details

Page(s) 1462-1472

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 20, Issue: 17, Year: 2025

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Keywords

probabilistic classification, fraudulent transactions, Gaussian Mixture Model, credit card fraud detection, Imbalanced Dataset, Machine learning

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