A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance

Rajora, Shantanu; Li, Donglin; Jha, Chandan Kumar; Bharill, Neha; Patel, Om Prakash; Joshi, S. K.; Puthal, Deepak; Prasad, Mukesh

doi:10.1109/ssci.2018.8628930

Cited by 50 publications

(20 citation statements)

References 20 publications

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“…In Table 3, a comparison analysis was conducted between the algorithms proposed in this research and existing ML-based credit card fraud detection frameworks. The results showed that the XGB-AdaBoost and the ET-AdaBoost achieved fraud detection ACs that are 5.08% higher than the RF presented in [11] and 6.78% higher than the KNN presented in [11]. In comparison to the work presented in [13], the XGB-AdaBoost obtained an AC that is 8.74% higher.…”

Section: F Experiments Results and Discussionmentioning

confidence: 72%

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Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost

Ileberi¹,

Sun

Wang

2021

IEEE Access

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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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Section: F Experiments Results and Discussionmentioning

confidence: 72%

“…Rajora et al [11] conducted a comparative research of ML methods for credit card fraud detection using the European cardholders dataset. Some of the methods that were investigated include the RF and the kNN methods.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost

Ileberi¹,

Sun

Wang

2021

IEEE Access

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“…Parallel investigation of machine learning approaches for the problems of fraudulent activity detection with credit cards was performed by Rajora et al [50] on the European cardholders dataset. Noteworthy utilized methods from the work are k-nearest neighbors (kNN) and random forest (RF).…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection

Petrovic¹,

Antonijevic²,

Strumberger³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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The recent pandemic had a major impact on online transactions. With this trend, credit card fraud increased. For the solution to this problem the authors explore existing solutions and propose an optimized solution. The solution is based on an extreme gradient boosting algorithm (XGBoost) and a teaching-learning-based-optimization algorithm. The dataset optimizes the hyperparameters of the XGBoost which is utilized as the main driver for the solution. The evaluation was performed among other similar techniques that have solved this problem successfully in the past. Standard performance metrics were applied which are accuracy, recall, precision, Matthews correlation coefficient, and area under the curve. The result of this research presents a dominant solution that was proposed and successfully outperformed all other compared solutions overall.

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“…Bagging consists of a group of "weak learners" to form "strong learners" and uses majority voting to identify the predicted class by selecting the class with the highest vote assigned by the base learners. [19][20][21] The researcher conducted experiments on the credit card fraud dataset (CCFD) and investigated the performance of these classifiers through recall, precision, and precision-recall curve (PRC) area rates; PRC was chosen as the main indicator for the study. PRC measures the overall ability to distinguish between binary classes to predict whether a transaction is normal or fraudulent.…”

Section: Related Workmentioning

confidence: 99%

Mitigating unbalanced and overlapped classes in credit card fraud data with enhanced stacking classifiers system

Ishak

Tong

et al. 2022

F1000Res

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Background: Credit cards remain the preferred payment method by many people nowadays. If not handled carefully, people may face severe consequences such as credit card frauds. Credit card frauds involve the illegal use of credit cards without the owner’s knowledge. Credit card fraud was estimated to exceed a $35.5 billion loss globally in 2020, and results in direct or indirect financial loss to the owners. Hence, a detection system capable of analysing and identifying fraudulent behaviour in credit card activities is highly desirable. Credit card data are not easy to handle due to their inherited problems: (i) unbalanced class distributions and (ii) overlapping classes. General learning algorithms may not be able to address and handle the problems well. Methods: This study addresses these problems using an Enhanced Stacking Classifiers System (ESCS) that comprises two sequential levels. The first level is an excellent classifier for detecting normal credit card transactions (the majority class), while the second level contains stacking classifiers that distinguish credit card frauds (the minority class). The ESCS can improve the fraud detection via the second level, which contains sensitive classifiers to identify the misclassified fraud transactions as normal transactions from the first classifier. The meta-classifier then combines the decisions of the base classifiers from the levels to produce the final detections. Results: We evaluated the ESCS using the benchmark credit card fraud dataset (CCFD) that exhibits the two problems. The highest true positive rate (TPR) for detecting credit card frauds was 0.8841, which outperformed the single classifiers, bagging, boosting, and other researchers’ works. Conclusions: This study proves that the ESCS, with an additional level added to the stacking classifiers, can improve fraud detection on credit card data.

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A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance

Cited by 50 publications

References 20 publications

Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost

Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost

The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection

Mitigating unbalanced and overlapped classes in credit card fraud data with enhanced stacking classifiers system

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