SMOTE Based Credit Card Fraud Detection Using Convolutional Neural Network

Abstract

Nowadays, fraud correlated with credit cards became very prevalent since a lot of people use credit cards for buying goods and services. Because of e-commerce and technological advancement, most transactions are happening online, which is increasing the risk of fraudulent transactions and resulting in huge losses financially. Therefore, an effective detection technique, as the quickest prediction option, should be developed to deter fraud from propagating. This paper targeted to develop a deep learning (DL)-based model on SMOTE oversampling technique to predict the fraudulent transactions of credit cards. The system used three popular DL algorithms: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory Recurrent Neural Network (LSTM RNN), and measured the best performer in terms of evaluation metrics. However, the results confirm that the CNN algorithm outperformed both ANN and LSTM RNN. Additionally, compared to previous studies, our CNN fraud detection program recorded high rates of accuracy in identifying fraudulent activity. The system achieved an accuracy of 99.97%, precision of 99.94%, recall of 99.99%, and F1-Score of 99.96%. This proposed scheme can help to reduce financial loss by detecting credit card scams or frauds globally.