A Study on Detection of Credit Card Fraud using Machine Learning Techniques

Credit Card Fraud, a fraudulent activity committed by stealing a credit card and using the same without knowledge or permission of the Card owner. Credit Card Fraud is usually committed by a criminal to purchase goods or services utilizing another account but the same card. Machine Learning algorithms can simplify the process of detection of fraudulent transaction. We show how various algorithms can be used to determine if the transaction is legitimate or not. We have split the dataset into test and train data. SMOTE technique has been on the train data for oversampling as the dataset being used is highly imbalanced. Machine Learning Classification techniques such as Gaussian Naive Bayes, Logistic Regression and Random Forest have been compared in this research. Based on the obtained performance measures, we can conclude that all three Machine Learning Models could be used for Fraudulent Transaction Detection.