Credit Card Fraud Detection Scheme Using Machine Learning and Synthetic Minority Oversampling Technique (SMOTE)

Abstract

Credit Card Fraud Detection is one of the vital issues nowadays which needs to be tackled urgently. In today's world, everyone is shifting to an online and cashless world for easiness in the transaction. However, a colossal fraud scheme is running on the other side of this easiness. Daily, many people fall into this trap. This research work is a little contribution to solving this issue. This academic study uses data from the real world to find fraudulent transactions using Machine Learning techniques such as Decision Trees, Logistics Regression, and Random Forest. Furthermore, Synthetic Minority Oversampling Technique is employed to solve the dataset's imbalance issue. Following that, the effectiveness of machine learning methods is compared by using the "With SMOTE" and "Without SMOTE" techniques.