Customer Transaction Fraud Detection Using Xgboost Model

Customer transaction fraud detection is an important application for both the public and banks and it is becoming a heated topic in research and industries. Many data mining techniques have been utilized in financial sys-tem to save consumers millions of dollars per year. In this study, we presented a Xgboost-based transaction fraud detection model with some feature engineering and visualization. The dataset is from IEEECIS Fraud Detection Competition on Kaggle, which is a well-informed data science organization. The study indicated that xgboost based model outperformed the other three methods including Support Vector Machine, Random Forest and Logistic Regression. As to two feature selection methods, Xgboost performed better. Our best model achieved 95.2% roc auc score on leader-board and defeated other 98 percent participants.