[Home ] [Archive]   [ فارسی ]  
Main Menu
Home
Journal Information
Aims& Scopes
Editorial Board
About the Journal
Journal News
Articles archive
All Issues
Current Issue
Browse by Authors
Browse by Keywords
For Authors
Call for Papers
Submission Instruction
Submission Form
For Reviewers
Reviewers Section
Registration
Registration Information
Registration Form
Contact us
Contact Information
Contact us
Site Facilities
Site map
Search contents
FAQ
Top 10 contents
Inform to friends
::
MBRI Journals

Journal of Money & Economy

AWT IMAGE

(رتبه علمی-پژوهشی)

..
Related Journals

Journal of Islamic Finance Research

AWT IMAGE

(Biannual)

..
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: year 17, Issue 60 (9-2024) ::
JMBR 2024, 17(60): 233-259 Back to browse issues page
Providing a solution to detect fraud in bank transactions despite concept drift and imbalanced data
Saeideh Roshanfekr *1 , Ali Golzadeh
Abstract:   (138 Views)
With the increase in the number of bank users worldwide, the challenges of using bank cards, including theft of card details and fraud, have also increased. Real-time fraud detection in bank card transactions is challenging due to the inherent characteristics of transactions, such as imbalanced data and concept drift. If the two main challenges of imbalanced data and concept drift happen together, it will be much harder to detect fraud. In this paper, a hybrid classification algorithm based on a support vector machine with dynamic update is proposed as a solution to address such issues in bank card transaction data. Compared to other existing algorithms, this algorithm has several advantages: (1) It does not need past data chunks to learn new data chunks. (2) by using the proposed algorithm, it emphasizes the wrongly classified data in order to correct their classification. (3) It can be matched with the transfer conditions of minority and majority classes. (4) It maintains a limited number of classifications with higher performance and not necessarily all of them. In order to evaluate the proposed algorithm, a real bank dataset has been used and the results have been compared with a number of algorithms. The results show an increase in fraud detection accuracy and efficiency of the proposed algorithm compared to the compared algorithms.
Article number: 3
Full-Text [PDF 1612 kb]   (48 Downloads)    
Type of Study: Empirical Study | Subject: Corporate Finance and Governance (G3)
Received: 2023/12/8 | Accepted: 2024/12/10 | Published: 2025/01/19
References
1. رفرنس های متنی مثل خروجی کراس رف را در اینجا وارد کرده و تایید کنید -------- Ade, R. R., & Deshmukh, P. R. (2013). Methods for incremental learning: A survey. International Journal of Data Mining & Knowledge Management Process, 3(4), 113. [DOI:10.5121/ijdkp.2013.3408]
2. Baena-García, M., del Campo-Ávila, J., & Fidalgo, R. (2006). Early drift detection method. In Fourth International Workshop on Knowledge Discovery from Data Streams (Vol. 6, pp. 77-86).
3. Barros, R. S., Cabral, D. R., & Gonçalves Jr, P. M. (2017). RDDM: Reactive drift detection method. Expert Systems with Applications, 90, 344-355. [DOI:10.1016/j.eswa.2017.08.023]
4. Bifet, A. (2009). Adaptive learning and mining for data streams and frequent patterns. ACM SIGKDD Explorations Newsletter, 11(1), 55-56. [DOI:10.1145/1656274.1656287]
5. Brzezinski, D., & Stefanowski, J. (2015). Prequential AUC for classifier evaluation and drift detection in evolving data streams. In New Frontiers in Mining Complex Patterns: Third International Workshop, NFMCP 2014, Held in Conjunction with ECML-PKDD 2014. [DOI:10.1007/978-3-319-17876-9_6]
6. Cano, A., & Krawczyk, B. (2022). ROSE: Robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams. Machine Learning, 111(77), 2561-2599. [DOI:10.1007/s10994-022-06168-x]
7. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357. [DOI:10.1613/jair.953]
8. Chen, S., & He, H. (2009). SERA: Selectively recursive approach towards nonstationary imbalanced stream data mining. In 2009 International Joint Conference on Neural Networks (pp. 522-529). [DOI:10.1109/IJCNN.2009.5178874]
9. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297. https://doi.org/10.1007/BF00994018 [DOI:10.1023/A:1022627411411]
10. Ditzler, G., & Polikar, R. (2012). Incremental learning of concept drift from streaming imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 25(10), 2283-2301. [DOI:10.1109/TKDE.2012.136]
11. Elwell, R., & Polikar, R. (2011). Incremental learning of concept drift in nonstationary environments. IEEE Transactions on Neural Networks, 22(10), 1517-1531. [DOI:10.1109/TNN.2011.2160459] [PMID]
12. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139. [DOI:10.1006/jcss.1997.1504]
13. Gomes, H. M., Barddal, J. P., Enembreck, F., & Bifet, A. (2017). A survey on ensemble learning for data stream classification. ACM Computing Surveys (CSUR), 50(2), 1-36. [DOI:10.1145/3054925]
14. Han, M., Zhang, X., Chen, Z., Wu, H., & Li, M. (2023). Dynamic ensemble selection classification algorithm based on window over imbalanced drift data stream. Knowledge and Information Systems, 65(3), 1105-1128. [DOI:10.1007/s10115-022-01791-5]
15. Han, M., Zhang, X., Chen, Z., Wu, H., & Li, M. (2023). Dynamic ensemble selection classification algorithm based on window over imbalanced drift data stream. Knowledge and Information Systems, 65(3), 1105-1128. [DOI:10.1007/s10115-022-01791-5]
16. Jain, M., Kaur, G., & Saxena, V. (2022). A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection. Expert Systems with Applications, 193, 116510. [DOI:10.1016/j.eswa.2022.116510]
17. Jiang, C., Song, J., Liu, G., Zheng, L., & Luan, W. (2018). Credit card fraud detection: A novel approach using aggregation strategy and feedback mechanism. IEEE Internet of Things Journal, 5(5), 3637-3647. [DOI:10.1109/JIOT.2018.2816007]
18. Jiao, B., Guo, Y., Gong, D., & Chen, Q. (2022). Dynamic ensemble selection for imbalanced data streams with concept drift. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 1278-1291. [DOI:10.1109/TNNLS.2022.3183120] [PMID]
19. Kaggle. (n.d.). Credit card fraud dataset. Retrieved from https://www.kaggle.com/mlg-ulb/creditcardfraud
20. Kolter, J. Z., & Maloof, M. A. (2007). Dynamic weighted majority: An ensemble method for drifting concepts. The Journal of Machine Learning Research, 8, 2755-2790.
21. Krawczyk, B., Minku, L. L., Gama, J., & Stefanowski, J. (2017). Ensemble learning for data stream analysis: A survey. Information Fusion, 37, 132-156. [DOI:10.1016/j.inffus.2017.02.004]
22. Kulkarni, P., & Ade, R. (2014). Incremental learning from unbalanced data with concept class, concept drift and missing features: A review. International Journal of Data Mining & Knowledge Management Process, 4(6), 15. [DOI:10.5121/ijdkp.2014.4602]
23. Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zha, J. (2018). Learning under concept drift: A review. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2346-2363. [DOI:10.1109/TKDE.2018.2876857]
24. Lu, Y., Cheung, Y. M., & Tang, Y. Y. (2017). Dynamic weighted majority for incremental learning of imbalanced data streams with concept drift. IJCAI (pp. 2393-2399). [DOI:10.24963/ijcai.2017/333]
25. Lu, Y., Cheung, Y. M., & Tang, Y. Y. (2019). Adaptive chunk-based dynamic weighted majority for imbalanced data streams with concept drift. IEEE Transactions on Neural Networks and Learning Systems, 31(8), 2764-2778. [DOI:10.1109/TNNLS.2019.2951814] [PMID]
26. Medianama. (2017, December). India 33.87m credit cards 826.3m debit cards October 2017. Retrieved from https://www.medianama.com/2017/12/223-india-33-87m-creditcards-826-3m-debit-cards-october-2017/
27. Medianama. (2017, July). India credit cards debit cards May 2017. Retrieved from https://www.medianama.com/2017/07/223-india-credit-cardsdebit-cards-may-2017/
28. Pocock, A., Yiapanis, P., Singer, J., & Luján, M. (2010). Online non-stationary boosting. In Multiple Classifier Systems: 9th International Workshop (pp. 205-214). [DOI:10.1007/978-3-642-12127-2_21]
29. Polikar, R., Upda, S. S., & Honavar, V. (2001). Learn++: An incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 31(4), 497-508. [DOI:10.1109/5326.983933]
30. Somasundaram, A., & Reddy, U. S. (2016, September). Data imbalance: Effects and solutions for classification of large and highly imbalanced data. In International Conference on Research in Engineering, Computers and Technology (ICRECT 2016) (pp. 1-16). [DOI:10.1109/ICCIDS.2017.8272643] []
31. Somasundaram, A., & Reddy, U. S. (2017, June). Modelling a stable classifier for handling large scale data with noise and imbalance. In 2017 International Conference on Computational Intelligence in Data Science (ICCIDS) (pp. 1-6). [DOI:10.1109/ICCIDS.2017.8272643] []
32. Street, W. N., & Kim, Y. (2001). A streaming ensemble algorithm (SEA) for large-scale classification. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 377-382). [DOI:10.1145/502512.502568]
33. Wang, C., Chai, S., & Zhu, H. (2023). OpenDrift: Online evolving fraud detection for open-category and concept-drift transactions. In 2023 IEEE International Conference on Web Services (ICWS) (pp. 605-614). [DOI:10.1109/ICWS60048.2023.00079]
34. Wang, G., & Ma, J. (2012). A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine. Expert Systems with Applications, 39(5), 5325-5331. [DOI:10.1016/j.eswa.2011.11.003]
35. Wang, H., Fan, W., Yu, P. S., & Han, J. (2003). Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 226-235). [DOI:10.1145/956750.956778]
36. Wang, S., Minku, L. L., & Yao, X. (2013). A learning framework for online class imbalance learning. In 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL) (pp. 36-45). [DOI:10.1109/CIEL.2013.6613138]
37. Wang, S., Minku, L. L., & Yao, X. (2014). Resampling-based ensemble methods for online class imbalance learning. IEEE Transactions on Knowledge and Data Engineering, 27(5), 1356-1368. [DOI:10.1109/TKDE.2014.2345380]
38. Wang, S., Minku, L. L., & Yao, X. (2018). A systematic study of online class imbalance learning with concept drift. IEEE Transactions on Neural Networks and Learning Systems, 29(10), 4802-4821. [DOI:10.1109/TNNLS.2017.2771290] [PMID]
39. Wang, S., Minku, L. L., & Yao, X. (2018). A systematic study of online class imbalance learning with concept drift. IEEE Transactions on Neural Networks and Learning Systems, 29(10), 4802-4821. [DOI:10.1109/TNNLS.2017.2771290] [PMID]
40. Wang, S., Minku, L. L., Ghezzi, D., & Caltabiano, D. (2013). Concept drift detection for online class imbalance learning. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). [DOI:10.1109/IJCNN.2013.6706768]
41. Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1), 69-101. https://doi.org/10.1007/BF00116900 [DOI:10.1023/A:1018046501280]
42. Wu, K., Edwards, A., Fan, W., Gao, J., & Zhang, K. (2014). Classifying imbalanced data streams via dynamic feature group weighting with importance sampling. In Proceedings of the 2014 SIAM International Conference on Data Mining (pp. 722-730). [DOI:10.1137/1.9781611973440.83] [PMID] []
Send email to the article author



XML   Persian Abstract   Print



Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
year 17, Issue 60 (9-2024) Back to browse issues page
فصلنامه پژوهش‌های پولی-بانکی Journal of Monetary & Banking Research
Persian site map - English site map - Created in 0.06 seconds with 37 queries by YEKTAWEB 4710