Detecting credit card fraud by decision trees and support vector machines

Abstract

With the developments in the Information Technology and improvements in the communication channels, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS terminals or mail orders. As a result, fraud detection is the essential tool and probably the best way to stop such fraud types. In this study, classification models based on decision trees and support vector machines (SVM) are developed and applied on credit card fraud detection problem. This study is one of the firsts to compare the performance of SVM and decision tree methods in credit card fraud detection with a real data set.