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Online Transaction Detection Method Using Catboost Model

The blossom of online transaction has given rise to fraud, especially credit card fraud. Thus, Fraud Detection algorithm has become a critical issue, and several attempts have been made to detect transaction fraud using data mining methods. However, these methods suffer from lack of data or inadequate feature engineering. In this paper, we applied sufficient feature engineering and proposed a fraud detection algorithm based on Catboost. The experimental result indicates that our model outperforms other classical models, such as Logistic Regression, Support Vector Machine, and Random Forest. Moreover, we also illustrate the feature importance, which is valuable for feature selection and performance tuning.