Advanced fraud detection in Card-Based financial systems using a bidirectional Lstm-Gru ensemble model

• Autorzy: Toufik Ghrib , Khaldi Yacine , Pandey Purnendu Shekhar , Abusal Yusef Awad

Identyfikatory

DOI

10.35784/acs-2024-28

• Języki publikacji: EN

Abstrakty

EN

This article addresses the challenges of fraud in card-based financial systems and proposes effective detection and prevention strategies. By leveraging recent data analytics and real-time monitoring, the study aims to enhance transaction security and integrity. The authors review existing fraud detection methodologies, emerging trends, and the evolving tactics of fraudsters, emphasizing the importance of collaboration among financial institutions, regulatory agencies, and technology providers. Our proposed solution is an ensemble model combining Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks, designed to capture complex transactional patterns more effectively. Comparative analysis of six machine learning classifiers—AdaBoost, Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, and Voting—demonstrates that our BiLSTM-BiGRU ensemble model outperforms traditional methods, achieving a fraud detection performance score of 89.22%. This highlights the advanced deep learning model's superior ability to enhance the robustness and reliability of fraud detection systems.

Słowa kluczowe

EN

fraud detection; card-based financial systems; BiGru; BiLST; ensemble models; Machine Learning

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 3
• Strony: 51--66
• Opis fizyczny: Bibliogr. 25 poz., fig., tab.

Twórcy

autor

Toufik Ghrib

• École Normale Supérieure de Ouargla, Mathemathics department, Algeria

• https://orcid.org/0000-0001-7174-8962

autor

Khaldi Yacine

• École Normale Supérieure de Ouargla, Mathemathics department, Algeria

• https://orcid.org/0000-0002-8004-7698

autor

Pandey Purnendu Shekhar

• Group of institutions, Department of CSE, KIET, India

• https://orcid.org/0000-0003-1276-5388

autor

Abusal Yusef Awad

• Ufa Stat petroleum University, Ufa

• https://orcid.org/0009-0000-3550-6384

Bibliografia

• [1] Aghware, F. O., Ojugo, A. A., Adigwe, W., Odiakaose, C. C., Ojei, E. O., Ashioba, N. C., Okpor, M. D., & Geteloma, V. O. (2024). Enhancing the random forest model via synthetic minority oversampling technique for credit-card fraud detection. Journal of Computing Theories and Applications, 1(4), 407-420. https://doi.org/10.62411/jcta.10323
• [2] Bin Sulaiman, R., Schetinin, V., & Sant, P. (2022). Review of machine learning approach on credit card fraud detection. Human-Centric Intelligent Systems, 2, 55-68. https://doi.org/10.1007/s44230-022-00004-0
• [3] Carneiro, N., Figueira, G., & Costa, M. (2017). A data mining based system for credit-card fraud detection in e-tail. Decision Support Systems, 95, 91-101. https://doi.org/10.1016/j.dss.2017.01.002
• [4] Cherkassky, V., & Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural networks, 17(1), 113-126. https://doi.org/10.1016/S0893-6080(03)00169-2
• [5] Cui, J., Yan, C., & Wang, C. (2021). ReMEMBeR: Ranking metric embedding-based multicontextual behavior profiling for online banking fraud detection. IEEE Transactions on Computational Social Systems, 8(3), 643-654. https://doi.org/10.1109/TCSS.2021.3052950
• [6] Duarte Soares, L., de Souza Queiroz, A., López, G. P., Carreño-Franco, E. M., López-Lezama, J. M., & Muñoz-Galeano, N. (2022). BiGRU-CNN neural network applied to electric energy theft detection. Electronics, 11(5), 693. https://doi.org/10.3390/electronics11050693
• [7] Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448-455. https://doi.org/10.1016/j.ins.2017.12.030
• [8] Gorle, V. L. N., & Panigrahi, S. (2023). A semi-supervised Anti-Fraud model based on integrated XGBoost and BiGRU with self-attention network: an application to internet loan fraud detection. Multimedia Tools and Applications, 83, 56939–56964. https://doi.org/10.1007/s11042-023-17681-z
• [9] GR, J., & P, A. I. (2024). Attention layer integrated BiLSTM for financial fraud prediction. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-18764-1
• [10] Halvaiee, N. S., & Akbari, M. K. (2014). A novel model for credit card fraud detection using Artificial Immune Systems. Applied Soft Computing, 24, 40-49. https://doi.org/10.1016/j.asoc.2014.06.042
• [11] Klusowski, J. M., & Tian, P. M. (2024). Large scale prediction with decision trees. Journal of the American Statistical Association, 119(545), 525-537. https://doi.org/10.1080/01621459.2022.2126782
• [12] Machine Learning Group. (2024). Credit card fraud detection. Retrieved May 5, 2024 from https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
• [13] Patil, S., Somavanshi, H., Gaikwad, J. B., Deshmane, A., & Badgujar, R. (2015). Credit card fraud detection using decision tree induction algorithm. International Journal of Computer Science and Mobile Computing, 4(4), 92-95.
• [14] Poongodi, K., & Kumar, D. (2021). Support vector machine with information gain based classification for credit card fraud detection system. The International Arab Journal of Information Technology, 18(2), 199-207. https://doi.org/10.34028/iajit/18/2/8
• [15] Sahin, Y., & Duman, E. (2011). Detecting credit card fraud by decision trees and support vector machines. International MultiConference of Engineers and Computer Scientists 2011 (IMECS 2011) (pp. 1-6).
• [16] Sorournejad, S., Zojaji, Z., Atani, R. E., & Monadjemi, A. H. (2016) A survey of credit card fraud detection techniques: Data and technique oriented perspective. ArXiv, abs/1611.06439. https://doi.org/10.48550/arXiv.1611.06439
• [17] Stamate, D., Davuloori, P., Logofatu, D., Mercure, E., Addyman, C., & Tomlinson, M. (2024). Ensembles of vidirectional LSTM and GRU neural nets for predicting mother-infant synchrony in videos. In L. Iliadis, I. Maglogiannis, A. Papaleonidas, E. Pimenidis, & C. Jayne (Eds.), Engineering Applications of Neural Networks (Vol. 2141, pp. 329–342). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-62495-7_25
• [18] Sudha, C., & Akila, D. (2021). WITHDRAWN: Majority vote ensemble classifier for accurate detection of credit card frauds. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.01.616
• [19] Teh, B., Islam, M. B., Kumar, N., Islam, M. K., & Eaganathan, U. (2018). Statistical and spending behavior based fraud detection of card-based payment system. 2018 International Conference on Electrical Engineering and Informatics (ICELTICs) (pp. 78-83). IEEE. https://doi.org/DOI:10.1109/ICELTICS.2018.8548878
• [20] Valkenborg, D., Rousseau, A. J., Geubbelmans, M., & Burzykowski, T. (2023). Support vector machines. American Journal of Orthodontics and Dentofacial Orthopedics, 164(5), 754-757. https://doi.org/10.1016/j.ajodo.2023.08.003
• [21] Wang, S. (2024). Intelligent BiLSTM-Attention-IBPNN method for anomaly detection in financial auditing. IEEE Access, 12, 90005-90015. https://doi.org/10.1109/ACCESS.2024.3420243
• [22] Wen, J., Tang, X., & Lu, J. (2024). An imbalanced learning method based on graph tran-smote for fraud detection. Scientific Reports, 14, 16560. https://doi.org/10.1038/s41598-024-67550-4
• [23] Xu, L., Xu, W., Cui, Q., Li, M., Luo, B., & Tang, Y. (2023). Deep heuristic evolutionary regression model based on the fusion of BiGRU and BiLSTM. Cognitive Computation, 15, 1672-1686. https://doi.org/10.1007/s12559-023-10135-6
• [24] Zhang, X., Han, Y., Xu, W., & Wang, Q. (2021). HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Information Sciences, 557, 302-316. https://doi.org/10.1016/j.ins.2019.05.023
• [25] Zheng, P., (2020). Dynamic Fraud Detection via Sequential Modeling. Graduate Theses and Dissertations. Retrieved from https://scholarworks.uark.edu/etd/3633

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).