Ensemble Techniques for Credit Card Fraud Detection

Abstract

Credit card fraud is a problem that has grown by great danger and has a huge impact on the financial sector. The challenges of credit card fraud are the availability of public data, high imbalance in data, and volatility of the fraud nature. Over the years ensemble learning has gained more importance and proved to give better performance. Here we try to do a comparative study of various ensemble approaches using various learning algorithms on the credit card fraud data and to understand multiple models based on various evaluation and performance metrics using the SMOTE balancing technique.