Financial Fraud Detection for Credit Card Using XGBoost & SMOTE

The area of fraud detection has been traditionally correlated with data mining and text mining. Even before the "big data" phenomena started in 2008, text mining and data mining were used as instruments of fraud detection. However, the limited technological capabilities of the pre-big data technologies made it very difficult for researchers to run fraud detection algorithms on large amounts of data. This paper reviews various existing learning methods for financial fraud detection of credit card fraud across different areas and find out their strengths and weaknesses. This paper has developed a credit card fraud detection system using XGBoost (eXtreme Gradient Boosting) method. In the proposed system, data imbalance problem has also been resolved by using SMOTE. Results are evaluated and compared with many states of art methods & found that the presented method performs well in achieving more accurate results after resolving data imbalance.