Credit Card Fraud Detection based on Deep Learning Models

Authors

El-Sayed M. El-Kenawy, School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, BahrainFollow
Ahmed Mohamed Zaki, Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
Wei Hong Lim, Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia.
Abdelhameed Ibrahim, School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain
Marwa M. Eid, Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt.
Ahmed Osman Osman, Department of Information Systems, Faculty of Computers and Information, Suez University, P.O.Box:43221, Suez, Egypt.
Ahmed M. Elshewey, Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.Box:43221, Suez, Egypt.

Document Type

Article

Keywords

fraud detection, Deep Learning, Recurrent Neural Network (RNN), Credit card fraud detection

Abstract

Credit card fraud detection (FD) protects consumers and financial institutions by identifying suspicious or unauthorized transactions. To improve security and reduce false positives, fraud detection systems can analyze transaction data patterns in real time using advanced machine learning (ML) and deep learning (DL). This paper exploits DL models to detects transactional data which includes anomalies through Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) to verify data and mitigate fraud. The models used precision, recall, F1-score, and AUC on a balanced shared 559856-record Kaggle repository dataset. The RNN model detected anomalies with 99.39% accuracy, 0.9939 F1-score, and excellent recall. RNN shows promise as a real-time anomaly detection method with high performance and low computational cost.

How to Cite This Article

El-Kenawy, El-Sayed M.; Zaki, Ahmed Mohamed; Lim, Wei Hong; Ibrahim, Abdelhameed; Eid, Marwa M.; Osman, Ahmed Osman; and Elshewey, Ahmed M. (2024) "Credit Card Fraud Detection based on Deep Learning Models," Mesopotamian Journal of Computer Science: Vol. 4: Iss. 1, Article 15.
DOI: https://doi.org/10.58496/MJCSC/2024/016
Available at: https://map.researchcommons.org/mjcsc/vol4/iss1/15