A new method to detect deception in electronic banking using the algorithm bagging and behavior patterns abnormal users


  • Maryam Hassanpour Faculty of Electrical and Computer, Institute Higher Eduction ACECR Khuzestan, Iran
  • Ali Harounabadi Islamic Azad University Central Tehran Branch, Iran
  • Mohammad Ali Naizari Faculty of Electrical and Computer, Institute Higher Eduction ACECR Khuzestan, Iran


Electronic banking, Fraud detection, Clustering, Decision tree, Bagging algorrithm


Nowadays, large volumes of money transfers done in electronically channel and daily increasing grow in these services and transactions, on the one hand, and anonymity of offenders in the Internet on the other hand, encourage the fraudsters to enter to this field. One of the main obstacles in the use of internet banking is lack of security in transactions and some of abuses in the way of the financial exchanges. For this reason, prevent from unauthorized penetration and detection of crime is an important issue in financial institutions and banks. In the meantime, the necessity of applying fraud detection techniques in order to prevent from fraudulent activities in banking systems, especially electronic banking systems, is inevitable. In this paper, design and implementation system that recognizes suspicious and unusual behavior of bank users in the electronic banking systems. In this paper, we use data mining techniques to detect fraud in electronic banking. For this purpose, we use from a multi-stage hybrid method include: Clustering to separate customers and improve rankings and category for fraud detection. In the clustering method used from k center method and in the category method used from classification of C4.5 decision tree and also bagging's collective method of classification. Finally, the results indicate the high potential of the proposed method. The proposed method in compared with the previous method in the benchmark of accuracy 3.22 percent, in the benchmark of correctness 3.27 percent and in the benchmark of convocation 4.32 percent and in the benchmark of F1 3.81 been improved.


Adeyiga, J.A., Ezike, J.O., Omotosho, A., Amakulor, W., 2012. A Neural network based model for detecting irregularities in e-banking transactions. Afr. J. Comput. ICT Ref., 4(3,2), 7-12.

Ameri, F., Walden couples, M.J., 2007. The various techniques unsupervised clustering method. Geomatics 86, Tehran, national mapping agency, 1-10.

Bahador, H., Kazemi, A., 2010. A model for the identification of bank customers in terms of bank robbery with the use of data mining fuzzy.The fourth Iran Data Mining Conference, Tehran, Sharif University of Technology, 1-12.

Ghiyasi, F., Nezafati, N., Shokohyar, S., 2015. Clustering users of marine data by using data mining techniques. Letters processing and management, 30(4), 1037-1039.

Hatami Rad, A., Shahriari, H.R., 2010. Methods and strategies to detect fraud in electronic banking. Economic News, 134, 219-225.

Hossin, M., Sulaiman, M.N., 2015. A review on evaluation metrics for data classification evaluations. Int. J. Data. Min. Knowl. Manag. Process (IJDKP), 5(2), 01-11.

Kashani, S., 2014. Detect fraud in electronic banking using data mining. National Conference on Computer Engineering and Information Technology Management, University of Sistan and Baluchestan, 1-12.

Kovach, S., 2011. Online banking fraud detection based on local and global behavior. ICDS: The Fifth International Conference on Digital Society, 166-171.

Majidi pour, M., 2011. Evolution and common methods of electronic banking. Management magazine, 30, 37-41.

Michalak, K., Korczak, J., 2011. Graph mining approach to suspicious transaction detection. Proceedings of the Twenty-Ninth Federated Conference on Computer Science and Information Systems, IEEE, 69-75.

Polikar, R., 2006. Ensemble based systems in decision making. Circ. Syst. Mag., 6(3), 21-45.

Reza, S., Haider, S., 2011. Suspicious activity reporting using dynamic bayesian networks. Procedia. Comput. Sci., (3), 987-991.

Syarif, I., Zaluska, E., Prugel-Bennett, A., Wills, G., 2012. Application of bagging, boosting and stacking to intrusion detection. 8th International Conference on Machine Learning and Data Mining in Pattern Recognition, Springer, Berlin Heidelberg, 7376, 593-602.

Vadoodparast, M., Hamdan, A.R., Sarim, H.M., 2015. Ftaudulent electronic transaction detection using dynamic KDA model. (IJCSIS) Int. J. Comput. Sci. Inform. Secur., 13(2), 1-8.

Velmurugan, T., Santhanam, T., 2010. Computational complexity between K- Means and K-Medo ids clustering algorithms for normal and uniform distributions of data points. J. Comput. Sci., 6(3), 363-368.

Wang, G., 2011. A comparative assessment of ensemble learning for credit scoring. Exp. Syst. Appl., 38(1), 223-230.



How to Cite

Hassanpour, M. ., Harounabadi, A. ., & Naizari, M. A. . (2017). A new method to detect deception in electronic banking using the algorithm bagging and behavior patterns abnormal users. Scientific Journal of Pure and Applied Sciences, 6(1), 544-555. Retrieved from http://www.sjournals.com/index.php/sjpas/article/view/164



Computer and Information Science