Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study

Abstract

The goal of data analytics is to delineate hidden patterns and use them to support informed decisions in a variety of situations. Credit card fraud is escalating significantly with the advancement of the modernized technology and become an easy target for fraudulent. Credit card fraud is a severe problem in the financial service and costs billions of a dollar every year. The design of fraud detection algorithm is a challenging task with the lack of real-world transaction dataset because of confidentiality and the highly imbalanced publicly available datasets. In this paper, we apply different supervised machine learning algorithms to detect credit card fraudulent transaction using a real-world dataset. Furthermore, we employ these algorithms to implement a super classifier using ensemble learning methods. We identify the most important variables that may lead to higher accuracy in credit card fraudulent transaction detection. Additionally, we compare and discuss the performance of various supervised machine learning algorithms exist in literature against the super classifier that we implemented in this paper.