Fraud Analysis and Prevention in e-Commerce Transactions

Abstract

The volume of electronic transactions has raised significantly in last years, mainly due to the popularization of electronic commerce (e-commerce), such as online retailers (e.g., Amazon.com, eBay, Ali Express.com). We also observe a significant increase in the number of fraud cases, resulting in billions of dollars losses each year worldwide. Therefore it is important and necessary to developed and apply techniques that can assist in fraud detection and prevention, which motivates our research. This work aims to apply and evaluate computational intelligence techniques (e.g., Data mining and machine learning) to identify fraud in electronic transactions, more specifically in credit card operations performed by Web payment gateways. In order to evaluate the techniques, we apply and evaluate them in an actual dataset of the most popular Brazilian electronic payment service. Our results show good performance in fraud detection, presenting gains up to 43 percent of an economic metric, when compared to the actual scenario of the company.