Credit Card Fraud Detection Based on a Hybrid CNN-RNN Deep Learning Model

Ahmed Fahim; Ahmed M. Osman; Zahraa Tarek; Ahmed M. Elshewey

doi:10.48084/etasr.13938

Authors

Ahmed Fahim Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia | Department of Computer Science, Faculty of Computers and Information, Suez University, Suez, Egypt
Ahmed M. Osman Department of Information Systems, Faculty of Computers and Information, Suez University, Suez, Egypt https://orcid.org/0009-0002-0527-533X
Zahraa Tarek Department of Computer Engineering and Information, College of Engineering, Wadi Ad Dwaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia | Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35561, Egypt
Ahmed M. Elshewey Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.Box: 43221, Suez, Egypt | Applied Science Research Center, Applied Science Private University, Amman, Jordan https://orcid.org/0000-0002-3048-1920

Volume: 15 | Issue: 6 | Pages: 28836-28842 | December 2025 | https://doi.org/10.48084/etasr.13938

Received: 7 August 2025 | Revised: 22 August 2025 | Accepted: 7 September 2025 | Online: 8 December 2025

Corresponding author: Ahmed Fahim

Abstract

Credit card fraud detection is essential for protecting financial systems by promptly identifying unauthorized or anomalous transactions. Leveraging the strengths of Deep Learning (DL), this paper explores multiple architectures, including the Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and a hybrid CNN-RNN model, for detecting fraudulent behavior within transactional data. Using a balanced dataset of 559,856 records obtained from a publicly available Kaggle repository, each model was evaluated based on accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results showed that the CNN-RNN hybrid model outperformed all other models, achieving 99.99% accuracy, 0.9971 precision, perfect recall (1.0000), 0.9985 F1-score, and a ROC-AUC of 1.0000. These findings highlight the CNN-RNN model's effectiveness in real-time fraud detection, offering exceptional classification performance with minimal false positives and maximum anomaly coverage.

Keywords:

credit card fraud detection, hybrid CNN-RNN model, financial transaction security, anomaly detection, AI in FinTech

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