Journal
IEEE ACCESS
Volume 9, Issue -, Pages 165286-165294Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3134330
Keywords
Credit cards; Radio frequency; Support vector machines; Europe; Boosting; Random forests; Training; Credit card fraud; machine learning; predictive modeling
Categories
Funding
- South African National Research Foundation [114911, 137951, 132797]
- Eskom Tertiary Education Support Programme Grants
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The research develops a machine learning framework for credit card fraud detection using real-world imbalanced datasets from European credit cardholders, addressing class imbalance with the Synthetic Minority over-sampling TEchnique (SMOTE). AdaBoost technique improves classification quality, with results showing superior performance compared to existing methods.
The advance in technologies such as e-commerce and financial technology (FinTech) applications have sparked an increase in the number of online card transactions that occur on a daily basis. As a result, there has been a spike in credit card fraud that affects card issuing companies, merchants, and banks. It is therefore essential to develop mechanisms that ensure the security and integrity of credit card transactions. In this research, we implement a machine learning (ML) based framework for credit card fraud detection using a real world imbalanced datasets that were generated from European credit cardholders. To solve the issue of class imbalance, we re-sampled the dataset using the Synthetic Minority over-sampling TEchnique (SMOTE). This framework was evaluated using the following ML methods: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), and Extra Tree (ET). These ML algorithms were coupled with the Adaptive Boosting (AdaBoost) technique to increase their quality of classification. The models were evaluated using the accuracy, the recall, the precision, the Matthews Correlation Coefficient (MCC), and the Area Under the Curve (AUC). Moreover, the proposed framework was implemented on a highly skewed synthetic credit card fraud dataset to further validate the results that were obtained in this research. The experimental outcomes demonstrated that using the AdaBoost has a positive impact on the performance of the proposed methods. Further, the results obtained by the boosted models were superior to existing methods.
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