An Ensemble Learning Framework for Credit Card Fraud Detection Using Machine Learning and Deep Learning | ||
| Journal of Data Analytics and Intelligent Decision-making | ||
| دوره 1، شماره 3، آذر 2025، صفحه 31-55 اصل مقاله (1.11 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jdaid.2025.14411.1016 | ||
| نویسندگان | ||
| Ehsan Hajizadeh* ؛ Zahra Davoodian | ||
| Amirkabir University of Technology, No. 350, Hafez Ave, Valiasr Square, Tehran, Iran | ||
| چکیده | ||
| The rapid growth of digital payment systems has heightened the need for accurate and scalable methods to detect credit card fraud. This study evaluates a range of machine learning and deep learning algorithms, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), XGBoost, Convolutional Neural Networks (CNN), Baseline MLP (Multi-Layer Perceptron), and Long Short-Term Memory (LSTM), to identify effective approaches for detecting fraudulent transactions. Based on comparative analysis, Random Forest and LSTM achieved the strongest individual performance, with accuracies exceeding 96%. Building on these findings, a stacking ensemble model was constructed by integrating Random Forest and LSTM as base learners and Logistic Regression as the meta-classifier. The framework incorporates Convolutional Autoencoder (CAE) for feature extraction and Random Undersampling (RUS) with three resampling ratios (1:1, 1:5, and 1:10) to address class imbalance. Experimental results show that the ensemble model provides improved predictive accuracy compared with individual algorithms, achieving an accuracy of 99.98%, precision of 99.86%, and recall of 99.89% under a 1:10 resampling ratio. Rather than proposing a new algorithmic architecture, this study contributes a systematic and unified evaluation of widely used ML and DL approaches and demonstrates the effectiveness of integrating CAE, RUS, and a Random Forest–LSTM stacking ensemble in enhancing fraud detection performance. | ||
| کلیدواژهها | ||
| Fraud Detection؛ Ensemble Learning؛ Credit Cards؛ Machine Learning؛ Deep Learning | ||
| مراجع | ||
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Abdul Rehman Khalid, N. O., Omair Uthmani, M., Ashawa, M., Osamor, J., & Adejoh, J. (2024). Enhancing credit card fraud detection: An ensemble machine learning approach. Big Data and Cognitive Computing, 8(1), 27. https://doi.org/10.3390/bdcc8010006 Adewumi, A. O., & Akinyelu, A. A. (2017). A survey of machine-learning and nature-inspired based credit card fraud detection techniques. International Journal of System Assurance Engineering and Management, 8, 937–953. Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owired, E. O., Ayeh, S. A., & Eshun, J. (2023). A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal, 6, 100163. Akazue, M. I., Debekeme, I. A., Edje, A. E., Asuai, C., & Osame, U. J. (2023). Unmasking fraudsters: ensemble features selection to enhance random forest fraud detection. Journal of Computing Theories and Applications, 1(2), 201-211. Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed, M. (2022). Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms. IEEE Access, 10, 39700–39715. Alfaiz, N. S., & Fati, S. M. (2022). Enhanced credit card fraud detection model using machine learning. Electronics, 11(4), 662. Ali, A., Razak, S. A., Othman, S. H., Abdalla Elfadil Eisa, T., Al-Dhaqm, A., Nasser, M., Elhassan, T., Elshafie, H., & Saif, A. (2022). Financial fraud detection based on machine learning: A systematic literature review. Applied Sciences, 12, 9637. Almazroi, A. A., & Ayub, N. (2023). Online payment fraud detection model using machine learning techniques. Ieee Access, 11, 137188-137203. Asha, R., & Kr, S. K. (2021). Credit card fraud detection using artificial neural network. Global Transitions Proceedings, 2, 35–41. Ashtiani, M. N., & Raahemi, B. (2021). Intelligent fraud detection in financial statements using machine learning and data mining: a systematic literature review. Ieee Access, 10, 72504-72525. Bagga, S., Goyal, A., Gupta, N., & Goyal, A. (2020). Credit card fraud detection using pipelining and ensemble learning. Procedia Computer Science, 173, 104–112. Banerjee, R., Chen, S., Kashyap, M., & Purohit, S. (2018). Comparative analysis of machine learning algorithms through credit card fraud detection. In 2018 IEEE MIT Undergraduate Research Technology Conference (URTC) (pp. 1–5). Cambridge, MA: IEEE. Benchaji, I., Douzi, S., & El Ouahidi, B. (2021). Credit card fraud detection model based on LSTM recurrent neural networks. Journal of Advances in Information Technology, 12. Benchaji, I., Douzi, S., El Ouahidi, B., & Jaafari, J. (2021). Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. Journal of Big Data, 8(1), 151. Bin Sulaiman, R., Schetinin, V., & Sant, P. (2022). Review of machine learning approach on credit card fraud detection. Human-Centric Intelligent Systems, 2(1), 55-68. Błaszczyński, J., De Almeida Filho, A. T., Matuszyk, A., Szeląg, M., & Słowiński, R. (2021). Auto loan fraud detection using dominance-based rough set approach versus machine learning methods. Expert Systems with Applications, 163, 113740. Bounab, R., Zarour, K., Guelib, B., & Khlifa, N. (2024). Enhancing medicare fraud detection through machine learning: Addressing class imbalance with SMOTE-ENN. IEEE Access, 12, 54382–54396. Brown, G. (2010). Ensemble learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning (pp. 1-5). Springer. Charizanos, G., Demirhan, H., & İcen, D. (2024). An online fuzzy fraud detection framework for credit card transactions. Expert Systems with Applications, 252, 124127. Cheng, D., Wang, X., Zhang, Y., & Zhang, L. (2020). Graph neural network for fraud detection via spatial-temporal attention. IEEE Transactions on Knowledge and Data Engineering, 34, 3800–3813. Choudhury, T., Dangi, G., Singh, T. P., Chauhan, A., & Aggarwal, A. (2018). An efficient way to detect credit card fraud using machine learning methodologies. In 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT) (pp. 591–597). IEEE. Dhankhad, S., Mohammed, E., & Far, B. (2018, July). Supervised machine learning algorithms for credit card fraudulent transaction detection: a comparative study. In 2018 IEEE international conference on information reuse and integration (IRI) (pp. 122-125). IEEE. Domor Mienye, I., & Sun, Y. (2023). A deep learning ensemble with data resampling for credit card fraud detection. IEEE Access, 11, 30628–30638. Dornadula, V. N., & Geetha, S. (2019). Credit card fraud detection using machine learning algorithms. Procedia computer science, 165, 631-641. Esenogho, I. D. M., Swart, T. G., Aruleba, K., & Obaido, G. (2022). A neural network ensemble with feature engineering for improved credit card fraud detection. IEEE Access, 10, 16400–16407. Faraji, Z. (2022). A review of machine learning applications for credit card fraud detection with a case study. SEISENSE Journal of Management, 5, 49–59. Feng, X., & Kim, S. K. (2024). Novel machine learning based credit card fraud detection systems. Mathematics, 12(12), 1869. Flondor, E., Donath, L., & Neamtu, M. (2024). Automatic card fraud detection based on decision tree algorithm. Applied Artificial Intelligence, 38, 2385249. Forough, J., & Momtazi, S. (2021). Ensemble of deep sequential models for credit card fraud detection. Applied Soft Computing, 99, 106883. Great Learning Editorial Team (2024, December 3). Introduction to autoencoders? What are autoencoders applications and types? Mygreatlearning. https://www.mygreatlearning.com/blog/autoencoder/ Gupta, P., Varshney, A., Khan, M. R., Ahmed, R., Shuaib, M., & Alam, S. (2023). Unbalanced credit card fraud detection data: A machine learning-oriented comparative study of balancing techniques. Procedia Computer Science, 218, 2575-2584. Ileberi, E., Sun, Y., & Wang, Z. (2021). Performance evaluation of machine learning methods for credit card fraud detection using SMOTE and AdaBoost. IEEE Access, 9, 165286–165294. Itoo, F., Meenakshi, & Singh, S. (2021). Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. International Journal of Information Technology, 13, 1503–1511. Jan, C.-L. (2021). Detection of financial statement fraud using deep learning for sustainable development of capital markets under information asymmetry. Sustainability, 13, 20. Khaled Alarfaj, F., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed, M. (2022). Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms. IEEE Access, 10, 39700–39715. Khalid, A. R., Owoh, N., Uthmani, O., Ashawa, M., Osamor, J., & Adejoh, J. (2024). Enhancing credit card fraud detection: An ensemble machine learning approach. Big Data and Cognitive Computing, 8, 6. Madan, S., Sofat, S., & Bansal, D. (2022). Tools and techniques for collection and analysis of Internet-of-Things malware: A systematic state-of-art review. Journal of King Saud University-Computer and Information Sciences, 34, 9867–9888. Madhurya, M. J., Gururaj, H. L., Soundarya, B. C., Vidyashree, K. P., & Rajendra, A. B. (2022). Exploratory analysis of credit card fraud detection using machine learning techniques. Global Transitions Proceedings, 3, 31–37. Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M.-S., & Zeineddine, H. (2019). An experimental study with imbalanced classification approaches for credit card fraud detection. IEEE Access, 7, 93010–93022. Manjeevan Seera, C. P. L., Ajay Kumar, Lalitha Dhamotharan, & Kim Hua Tan. (2024). An intelligent payment card fraud detection system. Annals of Operations Research, 334, 445–467. Mienye, I. D., & Sun, Y. (2023). A machine learning method with hybrid feature selection for improved credit card fraud detection. Applied Sciences, 13(12), 7254. Ming, R., Abdelrahman, O., Innab, N., & Ibrahim, M. H. K. (2024). Enhancing fraud detection in auto insurance and credit card transactions: A novel approach integrating CNNs and machine learning algorithms. PeerJ Computer Science, 10, e2088. Patil, S., Nemade, V., & Soni, P. K. (2018). Predictive modelling for credit card fraud detection using data analytics. Procedia computer science, 132, 385-395. Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30, 19–50. Puh, M., & Brkić, L. (2019). Detecting credit card fraud using selected machine learning algorithms. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 1250–1255). IEEE. Raghavan, P., & El Gayar, N. (2019). Fraud detection using machine learning and deep learning. In 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 334–339). IEEE. Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE Access, 6, 14277–14284. Riskiyadi, M. (2024). Detecting future financial statement fraud using a machine learning model in Indonesia: a comparative study. Asian Review of Accounting, 32(3), 394-422. Roseline, J. F., Naidu, G., Pandi, V. S., Alias Rajasree, S. A., & Mageswari, N. (2022). Autonomous credit card fraud detection using machine learning approach. Computers and Electrical Engineering, 102, 108132. Salekshahrezaee, Z., Leevy, J. L., & Khoshgoftaar, T. M. (2023). The effect of feature extraction and data sampling on credit card fraud detection. Journal of Big Data, 10(1), 6. Severino, M. K., & Peng, Y. (2021). Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata. Machine Learning with Applications, 5, 100074. Sun, Y., Li, Z., Li, X., & Zhang, J. (2021). Classifier selection and ensemble model for multi-class imbalance learning in education grants prediction. Applied Artificial Intelligence, 35, 290–303. Taha, A. A., & Malebary, S. J. (2020). An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access, 8, 25579–25587. Tehreem Ashfaq, R. K., Yahaya, A. S., Aslam, S., Azar, A. T., Alsafari, S., & Hameed, I. A. (2022). A machine learning and blockchain based efficient fraud detection mechanism. Sensors, 22, 7162. Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38–48 | ||
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