ONLINE SHOPPING TREND & FRAUD ATTACK - E-COMMERCE

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, AliExpress.com). It is also observed 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 develop 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. 2020-21 has been marked by significant global commerce changes due to the overwhelming impact of the outbreak of Coronavirus (COVID-19). Online consumer volumes have fluctuated considerably, impacting how merchants approach their business practices and effecting how and where fraud and abuse occur. Fraud Attack Index captures in e-commerce transactions and the ability to identify unique users across a robust network. This report tracks and explains these changes amid an unprecedented time of global commerce.