Credit-card Fraud Detection System using Neural Networks
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Salwa Al Balawi Faculty of Computers and Information Technology, University of Tabuk, Saudi Arabia This email address is being protected from spambots. You need JavaScript enabled to view it. |
Njood Aljohani Faculty of Computers and Information Technology, University of Tabuk, Saudi Arabia This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: Recently, with the development of online transactions, the credit-card transactions begun to be the most prevalent online payment methods. Credit-card fraud refers to the use fake Credit-Cards to purchase goods without paying. With the fast research and development in the area of information technology and data mining methods including the neural networks and decision trees, to advanced machine learning and deep learning methods, researchers have proposed a wide range of antifraud systems. Mainly, the Machine Learning (ML) and Deep Learning (DL) methods are employed to perform the fraud detection task. This paper aims to explore the existing credit-card fraud detection methods, and categorize them into two main categories. In addition, we investigated the deployment of neural network models with credit-card fraud detection problem, since we employed the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). ANN and CNN models are implemented and assessed using a credit-card dataset. The main contribution of this paper focuses on increasing the fraud-detection classification accuracy through developing an efficient deep neural network model.
Keywords: Credit-card, fraud detection, machine learning, deep learning, neural networks, classifications.
Received December 3, 2020; accepted August 31, 2021