The International Arab Journal of Information Technology (IAJIT)


Credit-card Fraud Detection System using Neural Networks

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.

[1] Bahnsen A., Aouada D., Stojanovic A., and Ottersten B., “Feature Engineering Strategies for Credit Card Fraud Detection,” Expert Systems with Applications, vol. 51, pp. 134-142, 2016.

[2] Bhattacharyya S., Jha S., Tharakunnel K., and Westland J., “Data Mining for Credit Card Fraud: A Comparative Study,” Decision Support Systems, vol. 50, no. 3, p. 602-613, 2011.

[3] Buonaguidi B., Mira A., Bucheli H., and Vitanis V., “Bayesian Quickest Detection of Credit Card Fraud,” Bayesian Analysis, vol. 17, no.1, pp. 1- 30, 2021.

[4] Cardholders E., “Credit-card Fraud Detection Dataset

[cited; Available from: ulb/creditcardfraud, Last Visited, 2021.

[5] Chen J., Shen Y., and Ali R., “Credit Card Fraud Detection Using Sparse Autoencoder and Generative Adversarial Network,” in Proceedings of IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, 2018.

[6] Dal Pozzolo A., Caelen O., Le Borgne Y., Waterschoot S., and Bontempi G., “Learned Lessons in Credit Card Fraud Detection from A Practitioner Perspective,” Expert Systems with applications, vol. 41, no.10, pp. 4915-4928, 2014.

[7] Dubey S., Mundhe K., and Kadam A., “Credit Card Fraud Detection using Artificial Neural Network and BackPropagation,” in Proceedings of 4th International Conference on Intelligent Computing and Control Systems, Madurai, 2020.

[8] Fiore U., De Santis A., Perla F., Zanetti P., and Palmieri, F., “Using Generative Adversarial Networks for Improving Classification Effectiveness in Credit Card Fraud Detection,” Information Sciences, vol. 479, pp. 448-455, 2019.

[9] Gholamalinezhad H. and Khosravi H., “Pooling Methods in Deep Neural Networks, a Review,” arXiv preprint arXiv:2009.07485, 2020.

[10] Hussain F., Abbas S., Husnain M., Fayyaz U., Shahzad F., and Shah G., “IoT DoS and DDoS Attack Detection using ResNet,” in Proceedings of IEEE 23rd International Multitopic Conference, Bahawalpur, 2020.

[11] Kazemi Z. and Zarrabi H., “Using Deep Networks for Fraud Detection in The Credit Card Transactions,” in Proceedings of IEEE 4th International Conference on Knowledge-Based Engineering and Innovation, Tehran, 2017.

[12] Lakshmi S. and Kavilla S., “Machine Learning for Credit Card Fraud Detection System,” International Journal of Applied Engineering Research, vol. 13, no. 24, pp. 16819-16824, 2018.

[13] Lebichot B., Le Borgne Y., He-Guelton L., Oblé F., and Bontempi G., “Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Credit-card Fraud Detection System using Neural Networks 241 Detection,” in Proceedings of INNS Big Data and Deep Learning Conference, Genova, 2019.

[14] Lucas Y., “Credit Card Fraud Detection Using Machine Learning With Integration of Contextual Knowledge, Thesis Université de Lyon; Universität Passau (Deutscheland), 2019.

[15] Maes S., Tuyls K., Vanschoenwinkel B., and Manderick B., “Credit card fraud Detection Using Bayesian and Neural Networks, in Proceedings of the 1st International Naiso Congress on Neuro Fuzzy Technologies, Havana, 2002.

[16] Mubarek A. and Adalı E., “Multilayer Perceptron Neural Network Technique for Fraud Detection,” in Proceedings of International Conference on Computer Science and Engineering, Antalya, 2017.

[17] Ngai E., Hu Y., Wong Y., Chen Y., and Sun X., “The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of Literature,” Decision Support Systems, vol. 50, no. 3, p. 559-569, 2011.

[18] Pillai T., Hashem I., Brohi S., Kaur S., and Marjani M., “Credit Card Fraud Detection Using Deep Learning Technique,” in Proceedings of 4th International Conference on Advances in Computing, Communication and Automation, Subang Jaya, 2018.

[19] Pokkuluri K., Nedunuri S., and Devi U., “Crop Disease Prediction with Convolution Neural Network (CNN) Augmented with Cellular Automata,” The International Arab Journal of Information Technology, vol. 19, no. 5, pp. 765- 773, 2022.

[20] Raghavan P. and El Gayar N., “Fraud Detection Using Machine Learning And Deep Learning,” in Proceedings of International Conference on Computational Intelligence and Knowledge Economy, Dubai, 2019.

[21] Sahin Y. and Duman E., “Detecting Credit Card Fraud by ANN and Logistic Regression,” in Proceedings of International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, 2011.

[22] Shenvi P., Samant N., Kumar S., Kulkarni V., “Credit Card Fraud Detection Using Deep Learning,” in Proceedings of IEEE 5th International Conference for Convergence in Technology, Bombay, 2019.

[23] Wang S., Liu G., Li Z., Xuan S., Yan C., and Jiang C., “Credit Card Fraud Detection Using Capsule Network,” in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Miyazaki, 2018.

[24] Zamini M. and Montazer G., “Credit Card Fraud Detection Using Autoencoder Based Clustering,” in Proceedings of 9th International Symposium on Telecommunications, Tehran, 2018. Salwa Al Balawi, I have a Bachelor’s degree in Computer Science from the University of Tabuk, Saudi Arabia, in 2015, and I obtained a master’s degree in information security from the University of Tabuk, Saudi Arabia, in 2020. I worked as a lecturer at Unaizah College in Qassim in the Department of Cybersecurity from 2021 to in 2022, my research interests include machine learning, deep learning, and information steganography. Njood Aljohani, I obtained a Ph.D. in artificial intelligence and I am currently working as an associate professor at the Faculty of Computer at the University of Tabuk. And I am currently working as a dean of the Faculty of Computer at the University of Tabuk