CREDIT CARD FRAUD DETECTION USING TREE CLASSIFIER ALGORITHM

Abstract

In the financial world, new business-making systems have emerged as technology advances. The credit card system is one of them. However, there are many loopholes in this system, which caused a lot of problems with this system as a way of credit card fraud. As a result, both the industry and customers who use credit cards are losing a lot of money. The purpose is to detect fraud in the credit card industry with the use of a machine learning system. In this case, the Decision Tree method is employed for fraud detection. Using some public data as samples, the model's effectiveness may be determined. Following that, we look into a set of real-world credit card data from banking organizations. In addition to this, some clutter is added to the data sample to assist in confirming the robustness of the system. The method's relevance is that it creates a tree for the user's activity and then uses that tree to discover fraud transactions. The findings absolutely show that mainstream selection technologies have achieved considerable accuracy in detecting credit card fraud situations.