Volume 21, Issue 1 (3-2026)
J. Mon. Ec. 2026, 21(1): 81-120 |
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Nademi Y, Hoseini S M, Ebtia M, Ahmadi F. Hybrid PCA–SVM Approach to Credit Card Fraud Detection: Enhancing Payment System Oversight and Financial Stability. J. Mon. Ec. 2026; 21 (1) :81-120
URL: http://jme.mbri.ac.ir/article-1-736-en.html
URL: http://jme.mbri.ac.ir/article-1-736-en.html
1- Department of Economics, Faculty of Humanities, Ayatollah Boroujerdi University, Boroujerd, Iran
2- Gahar Artificial Intelligence Research Group, Ayatollah Boroujerdi University, Boroujerd, Iran
3- Department of Computer Engineering, Faculty of Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran
2- Gahar Artificial Intelligence Research Group, Ayatollah Boroujerdi University, Boroujerd, Iran
3- Department of Computer Engineering, Faculty of Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran
Abstract: (544 Views)
| Credit card fraud remains a significant threat to financial institutions and the integrity of digital payment systems, posing challenges for both operational risk management and regulatory oversight. This paper presents a novel hybrid machine learning framework for credit card fraud detection that combines Principal Component Analysis (PCA) for feature extraction with a Support Vector Machine (SVM) based feature selection mechanism. The aim is to reduce dimensionality while retaining the most informative features, thereby improving detection performance on highly imbalanced transaction datasets. The approach is evaluated on a large credit card transactions dataset, where PCA is first used to transform the input variables into principal components capturing the majority of variance, and an SVM with recursive feature elimination is then employed to identify and retain the most relevant components. Experimental results demonstrate that the proposed PCA–SVM pipeline significantly outperforms baseline models lacking this hybrid feature engineering: for example, it achieves a higher fraud recall (detection rate) by several percentage points while maintaining high precision, leading to improved F1-scores and overall accuracy. These findings indicate that the hybrid method effectively mitigates class imbalance issues and eliminates redundant features, yielding a more compact and robust fraud detection model. By enhancing the identification of rare fraudulent transactions without excessive false alarms, our study contributes to central bank objectives in fraud risk management. The proposed framework can strengthen the resilience of digital payment infrastructures and support payment system oversight, ultimately helping to safeguard financial stability and public trust in electronic payment channels. |
Keywords: Credit card fraud detection, Principal Component Analysis, Support Vector Machine, Feature selection, Hybrid machine learning, Feature Extraction
Type of Study: Original Research - Empirical |
Subject:
Monetary Economics
Received: 5 Sep 2025 | Accepted: 1 Feb 2026 | Published: 29 Mar 2026
Received: 5 Sep 2025 | Accepted: 1 Feb 2026 | Published: 29 Mar 2026
References
1. Adhao, R., & Pachghare, V. (2020). Feature selection using principal component analysis and genetic algorithm. Journal of discrete mathematical sciences and cryptography, 23(2), 595-602. [DOI:10.1080/09720529.2020.1729507]
2. 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. [DOI:10.1109/ACCESS.2022.3166891]
3. Alhaj, T. A., Siraj, M. M., Zainal, A., Elshoush, H. T., & Elhaj, F. (2016). Feature selection using information gain for improved structural-based alert correlation. PloS one, 11(11), e0166017. [DOI:10.1371/journal.pone.0166017]
4. Alfaiz, N. S., & Fati, S. M. (2022). Enhanced credit card fraud detection model using machine learning. Electronics, 11(4), 662. [DOI:10.3390/electronics11040662]
5. Alamri, M. A., & Ykhlef, M. A. (2023, February). A machine learning-based framework for detecting credit card anomalies and fraud. In 2023 27th International Conference on Information Technology (IT) (pp. 1-7). IEEE. [DOI:10.1109/IT57431.2023.10078528]
6. Bhanusri, A., Valli, K. R. S., Jyothi, P., Sai, G. V., & Rohith, R. (2020). Credit card fraud detection using machine learning algorithms. Journal of Research in Humanities and Social Science, 8(2), 04-11.
7. Cai, D., Zhang, C., & He, X. (2010). Unsupervised feature selection for multi-cluster data. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 333-342). [DOI:10.1145/1835804.1835848]
8. Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28. [DOI:10.1016/j.compeleceng.2013.11.024]
9. Chang, V., Ali, B., Golightly, L., Ganatra, M. A., & Mohamed, M. (2024). Investigating credit card payment fraud with detection methods using advanced machine learning. Information, 15(8), 478. [DOI:10.3390/info15080478]
10. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. [DOI:10.1023/A:1022627411411]
11. Dornadula, V. N., & Geetha, S. (2019). Credit card fraud detection using machine learning algorithms. Procedia Computer Science, 165, 631-641. [DOI:10.1016/j.procs.2020.01.057]
12. Funatsu, N., & Kuroki, Y. (2010). Fast parallel processing using GPU in computing L1-PCA bases. In TENCON 2010 - IEEE Region 10 Conference (pp. 2087-2090). IEEE. [DOI:10.1109/TENCON.2010.5686614]
13. Goh, W. W. B., & Wong, L. (2016). Evaluating feature-selection stability in next-generation proteomics. Journal of Bioinformatics and Computational Biology, 14(05), 1650029. [DOI:10.1142/S0219720016500293]
14. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.
15. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1), 389-422. [DOI:10.1023/A:1012487302797]
16. Gupta, R. K., Hassan, A., Majhi, S. K., Parveen, N., Zamani, A. T., Anitha, R., … Muduli, D. (2025). Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach. Results in Engineering, 26, 105084. [DOI:10.1016/j.rineng.2025.105084]
17. Halko, N., Martinsson, P. G., & Tropp, J. A. (2011). Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review, 53(2), 217-288. [DOI:10.1137/090771806]
18. Han, J., Kamber, M., & Pei, J. (2012). Data mining (3rd ed.). Morgan Kaufmann.
19. Han, J., Pei, J., & Tong, H. (2022). Data mining: Concepts and techniques (4th ed.). Morgan Kaufmann.
20. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer [DOI:10.1007/978-0-387-84858-7]
21. Hoseini, S. M., Ebtia, M., & Dehgardi, M. (2025). A hybrid feature selection technique leveraging principal component analysis and support vector machines. Journal of AI and Data Mining, 13(2), 159-173.
22. Jabri, M. A. (1998). High performance principal component analysis with ParAL. Neuro-morphic LLC. https://www.researchgate.net/publication/2333665_High_Performance_Principal_Component_Analysis_with_ParAL
23. Jolliffe, I. (2005). Principal component analysis. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science. [DOI:10.1002/0470013192.bsa501]
24. Kamkar, I., Gupta, S. K., Phung, D., & Venkatesh, S. (2015). Exploiting feature relationships towards stable feature selection. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10). IEEE. [DOI:10.1109/DSAA.2015.7344859]
25. Kennedy, R. K., Villanustre, F., & Khoshgoftaar, T. M. (2025). Unsupervised feature selection and class labeling for credit card fraud. Journal of Big Data, 12(1), 111. [DOI:10.1186/s40537-025-01154-1]
26. 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(1), 6. [DOI:10.3390/bdcc8010006]
27. Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1), 273-324. [DOI:10.1016/S0004-3702(97)00043-X]
28. Machine Learning Group. (2023). Credit Card Fraud Detection Dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
29. Naik, G. R. (Ed.). (2018). Advances in principal component analysis: Research and development. Springer. [DOI:10.1007/978-981-10-6704-4]
30. Ogundile, O., Babalola, O., Ogunbanwo, A., Ogundile, O., & Balyan, V. (2024). Credit card fraud: Analysis of feature extraction techniques for ensemble hidden Markov model prediction approach. Applied Sciences, 14(16), 7389. [DOI:10.3390/app14167389]
31. Omuya, E. O., Okeyo, G. O., & Kimwele, M. W. (2021). Feature selection for classification using principal component analysis and information gain. Expert Systems with Applications, 174, 114765. [DOI:10.1016/j.eswa.2021.114765]
32. Raghavendra, S., & Indiramma, M. (2016). Hybrid data mining model for the classification and prediction of medical datasets. International Journal of Knowledge Engineering and Soft Data Paradigms, 5(3/4), 262. [DOI:10.1504/IJKESDP.2016.084603]
33. Rtayli, N., & Enneya, N. (2020). Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization. Journal of Information Security and Applications, 55, 102596. [DOI:10.1016/j.jisa.2020.102596]
34. Ross, D. A., Lim, J., Lin, R. S., & Yang, M. H. (2008). Incremental learning for robust visual tracking. International Journal of Computer Vision, 77 (1), 125-141. [DOI:10.1007/s11263-007-0075-7]
35. Seddighi, A. H., & Sajedinejad, A. (2019). A deep learning approach to fraud detection in financial payment services. Iranian Journal of Information Management, 5(1), 166-182. [In Persian]
36. Shah, S. A., Shabbir, H. M., Rehman, S. U., & Waqas, M. (2020). A comparative study of feature selection approaches: 2016-2020. International Journal of Scientific & Engineering Research, 11(2), 469-478. https://www.researchgate.net/publication/339474097_A_Comparative_Study_of_Feature_Selection_Approaches_2016-2020
37. Siam, A. M., Bhowmik, P., & Uddin, M. P. (2025). Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models. PLOS ONE, 20(7), e0326975. [DOI:10.1371/journal.pone.0326975]
38. Solorio-Fernández, S., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2019). A review of unsupervised feature selection methods. Artificial Intelligence Review, 53(2), 907-948. [DOI:10.1007/s10462-019-09682-y]
39. Song, F., Guo, Z., & Mei, D. (2010). Feature selection using principal component analysis. In 2010 international conference on system science, engineering design and manufacturing informatization (Vol. 1, pp. 27-30). IEEE. [DOI:10.1109/ICSEM.2010.14]
40. Song, K., Zhang, B., Li, W., Yan, L., & Wang, X. (2021). Research on parallel principal component analysis based on ternary optical computer. Optik, 241, 167176. [DOI:10.1016/j.ijleo.2021.167176]
41. Sundaravadivel, P., Isaac, R. A., Elangovan, D., KrishnaRaj, D., Rahul, V. V., & Raja, R. (2025). Optimizing credit card fraud detection with random forests and SMOTE. Scientific Reports, 15(1), 1-12. [DOI:10.1038/s41598-025-33135-y]
42. Talukder, M. A., Hossen, R., Uddin, M. A., Uddin, M. N., & Acharjee, U. K. (2024). Securing transactions: A hybrid dependable ensemble machine learning model using IHT-LR and grid search. Cybersecurity, 7(1), 32. [DOI:10.1186/s42400-024-00221-z]
43. Tang, J., Alelyani, S., & Liu, H. (2014). Feature selection for classification: A review. Data classification: Algorithms and applications, 37.
44. Varun Kumar, K. S., Vijaya Kumar, V. G., Vijay Shankar, A., & Pratibha, K. (2020). Credit card fraud detection using machine learning algorithms. International Journal of Engineering Research & Technology (IJERT), 9(7). [DOI:10.17577/IJERTV9IS070649]
45. Wang, H., Liang, Q., Hancock, J. T., & Khoshgoftaar, T. M. (2024). Feature selection strategies: A comparative analysis of SHAP-value and importance-based methods. Journal of Big Data, 11(1), 44. [DOI:10.1186/s40537-024-00905-w]
46. Xin, B., Hu, L., Wang, Y., & Gao, W. (2015). Stable feature selection from brain sMRI. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 29, No. 1). [DOI:10.1609/aaai.v29i1.9477]
47. Zaffar, Z. (2023). Credit card fraud detection using one-class classification algorithms (Master's thesis, Faculty of Information Technology and Communication Sciences, Tampere University). Tampere University Repository. https://urn.fi/URN:NBN:fi:tuni-202309298554
48. Zhao, Z., Wang, L., & Liu, H. (2010). Efficient spectral feature selection with minimum redundancy. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 24, No. 1, pp. 673-678). [DOI:10.1609/aaai.v24i1.7671]
49. Zheng, W., Eilam-Stock, T., Wu, T., Spagna, A., Chen, C., Hu, B., & Fan, J. (2019). Multi-feature based network revealing the structural abnormalities in autism spectrum disorder. IEEE Transactions on Affective Computing, 12(3), 732-742. [DOI:10.1109/TAFFC.2018.2890597]
50. Zhou, Z. H. (2025). Ensemble methods: Foundations and algorithms. CRC Press. [DOI:10.1201/9781003587774]
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