| Abstract |
Banking transactions, such as online transactions, credit card transactions and the mobile wallet, are gaining popularity. People are shopping more and more using credit cards. Credit cards have become a necessity, to the virtual world, for digitized and paperless transactions. Millions of online transactions take place every day and all of these transactions are subject to various types of fraud. There are many techniques developed to analyze, detect and prevent credit card fraud, these techniques are no longer sufficient for current needs. In recent years, several studies have used machine Learning techniques to find solutions to this problem. In this paper, we present a comparative study of different techniques of machine learning, especially neural network, applied to the same dataset. Our analysis aims to propose a comprehensive guide to choose the best techniques for credit card fraud detection. © 2019 Association for Computing Machinery. |
