ADVANCED FRAUD DETECTION IN CARD-BASED FINANCIAL SYSTEMS USING A BIDIRECTIONAL LSTM-GRU ENSEMBLE MODEL
Toufik GHRIB
ghrib.toufik@univ-ouargla.dzÉcole Normale Supérieure de Ouargla, Mathemathics department (Algeria)
https://orcid.org/0000-0001-7174-8962
Yacine KHALDI
École Normale Supérieure de Ouargla, Mathemathics department (Algeria)
https://orcid.org/0000-0002-8004-7698
Purnendu Shekhar PANDEY
Bipin Tripathi Kumaon Institute of Technology (India)
https://orcid.org/0000-0003-1276-5388
Yusef Awad ABUSAL
Ufa State Petroleum Technological University (Palestine, State of)
https://orcid.org/0009-0000-3550-6384
Abstract
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.
Keywords:
fraud detection, card-based financial systems, BiGru, BiLST, ensemble models, Machine LearningReferences
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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. DOI:10.1016/j.asoc.2014.06.042
Google Scholar
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
Google Scholar
Machine Learning Group. (2024). Credit card fraud detection. Retrieved May 5, 2024 from https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
Google Scholar
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.
Google Scholar
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
Google Scholar
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).
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Zheng, P., (2020). Dynamic Fraud Detection via Sequential Modeling. Graduate Theses and Dissertations. Retrieved from https://scholarworks.uark.edu/etd/3633
Google Scholar
Authors
Toufik GHRIBghrib.toufik@univ-ouargla.dz
École Normale Supérieure de Ouargla, Mathemathics department Algeria
https://orcid.org/0000-0001-7174-8962
Authors
Yacine KHALDIÉcole Normale Supérieure de Ouargla, Mathemathics department Algeria
https://orcid.org/0000-0002-8004-7698
Authors
Purnendu Shekhar PANDEYBipin Tripathi Kumaon Institute of Technology India
https://orcid.org/0000-0003-1276-5388
Authors
Yusef Awad ABUSALUfa State Petroleum Technological University Palestine, State of
https://orcid.org/0009-0000-3550-6384
Statistics
Abstract views: 255PDF downloads: 79
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
- Sheikh Amir FAYAZ, Majid ZAMAN, Muheet Ahmed BUTT, Sameer KAUL, HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS , Applied Computer Science: Vol. 18 No. 4 (2022)
- Jerzy JÓZWIK, Magdalena ZAWADA-MICHAŁOWSKA, Monika KULISZ, Paweł TOMIŁO, Marcin BARSZCZ, Paweł PIEŚKO, Michał LELEŃ, Kamil CYBUL, MODELING THE OPTIMAL MEASUREMENT TIME WITH A PROBE ON THE MACHINE TOOL USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 20 No. 2 (2024)
- Lubna RIYAZ, Muheet Ahmed BUTT, Majid ZAMAN, IMPROVING CORONARY HEART DISEASE PREDICTION BY OUTLIER ELIMINATION , Applied Computer Science: Vol. 18 No. 1 (2022)
- Mahmoud BAKR, Sayed ABDEL-GABER, Mona NASR, Maryam HAZMAN, TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING , Applied Computer Science: Vol. 18 No. 2 (2022)
- Shahil SHARMA, Rajnesh LAL, Bimal KUMAR, DEVELOPING MACHINE LEARNING APPLICATION FOR EARLY CARDIOVASCULAR DISEASE (CVD) RISK DETECTION IN FIJI: A DESIGN SCIENCE APPROACH , Applied Computer Science: Vol. 20 No. 3 (2024)
- Malek M. AL-NAWASHI , Obaida M. AL-HAZAIMEH, Mutaz Kh. KHAZAALEH , A NEW APPROACH FOR BREAST CANCER DETECTION- BASED MACHINE LEARNING TECHNIQUE , Applied Computer Science: Vol. 20 No. 1 (2024)
- Archana Gunakala, Afzal Hussain Shahid, A COMPARATIVE STUDY ON PERFORMANCE OF BASIC AND ENSEMBLE CLASSIFIERS WITH VARIOUS DATASETS , Applied Computer Science: Vol. 19 No. 1 (2023)
- Hawkar ASAAD, Shavan ASKAR, Ahmed KAKAMIN, Nayla FAIQ, EXPLORING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMANROBOT COOPERATION IN THE CONTEXT OF INDUSTRY 4.0 , Applied Computer Science: Vol. 20 No. 2 (2024)
- Anitha Rani PALAKAYALA, Kuppusamy P, A QUALITATIVE AND QUANTITATIVE APPROACH USING MACHINE LEARNING AND NON-MOTOR SYMPTOMS FOR PARKINSON’S DISEASE CLASSIFICATION. A HIERARCHICAL STUDY , Applied Computer Science: Vol. 20 No. 3 (2024)
You may also start an advanced similarity search for this article.
