Credit card fraudulence detection using salient feature extraction technique with adaptive synthetic oversampling models

Abstract

Credit card fraudulence is a federal offense that takes place frequently in recent times. The phenomenon where an imposter or a scammer tries to make an illegal purchase or transfer of money from one account to another using a credit card that does not belong to him/her, is coined as Credit Card Fraudulence. In modern world, credit card fraud or any type of payment card fraud is a very common but serious crime that occurs both offline and online. But with the help of machine learning algorithms and Salient Feature Extraction Technique (SFET) we can easily detect such offense and help in further investigations. From time to time many data scientists, data analysts, machine learning engineers and other researchers have designed many algorithms to detect credit card frauds. By extracting the most relevant and important features of a transaction, it is quite possible to detect credit card fraud very quickly efficiently. In this paper, we have shown such an improved way by using Adaptive Synthetic oversampling (ADASYN) model with five notable supervised machine learning models namely Random Forest, Support Vector Machine, Naive Bayes, Logistics Regression and K-Nearest Neighbour. Out of these five machine learning models, K-Nearest Neighbour has shown the best precision, recall, specificity accuracy. The performance accuracy of Random Forest, Logistic Regression, K-Nearest Neighbour, Naive Bayes Support Vector Machines are 96.04%, 81.31%, 96.22%, 79.22% 50.06% respectively.