An Efficient Credit Card Fraud Detection System using Deep-learning based Approaches

Abstract

During the past few years, use of e-commerce has been grown to a large scale. Due to which, the use of credit card has also been increased. Many people now use credit cards for online shopping, e-billing, and other online payments. This frequent use of credit cards is pushing the organizations and banks to implement credit card fraud detection systems to distinguish between illicit and legitimate transactions. These systems have been trained in pre-existed datasets and then applied to the new transactions. Many techniques are used to detect fraudulent transactions, such as Genetic Algorithm (GA), Support Vector Machine (SVM), and Artificial Immune System (AIS). In most of the techniques, the classification results are biased towards the majority class due to this biasness False Positive Rate (FPR) and False Negative Rate (FNR) are maximized. To overcome this problem, we have implemented three techniques, i.e., Naive Bayes (NB), Generative Adversarial Networks (GAN), and Neural Networks (NN). The final results are then compared in terms of accuracy, precision, recall, and f-measure. Our main objectives are to minimize the FPR and FNR, which ultimately improves the identification of fraudulent and legitimate transactions. The results show that NN outperforms NB and GAN in term of accuracy, precision, and f-measure.