Abstract: With the rapid development of the credit card business in financial institutions, the financial institutions have faced a serious problem in Credit Card Fraud. Aiming at the problem of the unbalanced distribution of the credit card data in the financial institutions, the paper adopts six ways such as the oversampling, the down sampling, the SMOTE+ENN, the SMOTE+Tomeklin, the improved SMOTE+Tomeklin and the improved SMOTE+ENN for processing the unbalanced data. At the same time, the processed six data sets are input into various classification algorithm models for experimental comparison. Then the balance data sets are input into a muilty-classification algorithm model to make experimental comparisons. Finally, a new Credit Card Fraud Detection model combining the improved SMOTE+ENN and XGBoost algorithm is proposed. The empirical results of five evaluation indicators show that the detection method not only improves the discrimination of unbalanced data of Credit Card Fraud, but also improves the accuracy and feasibility of Credit Card Fraud detection.

Key words: SMOTE+ENN, XGBoost, unbalanced data, Credit Card Fraud Detection, evaluation indicators