FRAUDULENT ACTIVITY DETECTION IN ONLINE SHOPPING USING MACHINE LEARNING

Abstract

The volume of internet users is increasingly causing transactions on e-commerce to increase as well. We observe the quantity of fraud on online transactions is increasing too. Fraud prevention in e-commerce shall be developed using machine learning, this work to analyse the suitable machine learning algorithm, the algorithm to be used is the Decision Tree, Naive Bayes, Random Forest, and ensembled algorithms. Large amounts of money are often handled on e-commerce websites. And when large amounts of money are moved, there is a high risk that users will engage in fraudulent activities, e.g. Eg Use of stolen credit cards, money laundering, etc.