Credit Card Fraud Detection using SMOTE and Ensemble Methods

Abstract We focused on the study of using math modeling and machine learning to do big data analysis, therefore to detect Credit card fraud, which is one of the serious issues in real life. In order to detect credit card fraud, after reviewed many recent research, we chose the most popular models among credit card fraud detection, which are Random Forest (RF), and ANN with multi-layers (DNN). We evaluated the accuracy and recall of these models in detecting credit card fraud with or without SMOTE, and found out that there is no significant improvement in the accuracy of these models with or without SMOTE training, but RF with SOMTE has a little bit vantage than others. There is a significant improvement in recall of these three models with SMOTE training. Especially, with SMOTE training, ANN or DNN is of better performance in the recall than RF. Therefore, we combine RF and DNN to generate a hybrid model so that it produces better stability in accuracy and recall. The study discovered that neural network models have greater potential for finding abnormal data in the big data stream. This has important guiding significance for what mathematical model that credit card companies use to monitor the cash flow and remind customers of the possible risk of credit card fraud.

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Engineering Journal: Credit Card Fraud Detection using SMOTE and Ensemble Methods

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