Effectiveness Undersampling Method and Feature Reduction in Credit Card Fraud Detection

Abstract

Credit card fraud is an issue that has affected Indonesia payment system over a decade. Sometimes, the result of the fraud used for terrorism and other crimes. Financial loss is not the only problem that is affected caused by credit card fraud but also Indonesia images in international trade, e-commerce, and the merchant. Currently, a trusted and secured banking payment system is crucial for both customers and banks. The problem from credit card fraud dataset is the data have many features and imbalanced class, this problem leads the paper to propose undersampling technique and feature reduction methods. In this paper we proposed two stage-feature reduction technique because a stage feature reduction could not find the optimal features. On the other hands, we are also applied Instance Hardness Threshold sampling and Random undersampling to deal with imbalance data. The two-stage feature reduction is chosen to eliminate the ineffective feature that cannot eliminate using only one feature reduction. The model from the implemented machine learning methods is evaluated using accuracy, specificity, recall, and Matthews Correlation Coefficient. We implemented our proposed approaches in the ULB credit card fraud detection dataset. According to the result, the undersampling gives a boost in performances which improve the recall and MCC score, the IHT undersampling provide goods results, and in some cases, the result can predict all the test set correctly. However, the two-stages feature reduction fails to improve the accuracy, precision, recall, and MCC score. In one case, the method reduced the accuracy score to 0.302.

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