Detection of Fraudulent Credit Card Transactions Using Deep Neural Network

The usage of credit cards for online purchases has increased exponentially and so has fraud. In recent years, detecting fraud in credit card transactions has become significantly more difficult. As a result, having effective and accurate methods for detecting fraud in credit card transactions is vital. Although supervised learning algorithms have been shown to be effective in detecting credit card fraud, they have not generated substantial results. Hence, a Deep Neural Network (DNN)-based technique is proposed. The sequential model is used to construct the proposed DNN. However, the parameters of the model, such as the number of nodes and the activation function in the hidden layers, can also have an impact on its output. The proposed technique was evaluated on a credit card transaction dataset that comprised fraudulent and genuine transactions. This technique obtained the accuracy, area under curve (AUC), and precision of 99.93%, 99.99%, and 99.89%, respectively. This technique was compared with various models such as Optimized LightGBM, Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM).