Presenting a Hybrid Model based on the Machine Learning for the Classification of Banking and Insurance Industry Common Customers

Subject Areas : Business Management

Hamidreza Amirhassankhani ¹ , Abbas Toloie Eshlaghy ² , Reza Radfar ³ , Alireza pourebrahimi ⁴

1 - Ph.D. Candidate of Information Technology Management Group, UAE Branch, Islamic Azad University, Dubai, UAE
2 - Professor, Department of Industrial Management, Science and Research Unit, Islamic Azad University, Tehran, Iran
3 - Professor, Department of Industrial Management, Science and Research Unit, Islamic Azad University, Tehran, Iran
4 - Assistant Professor, Department of Management, Karaj Branch, Islamic Azad University, Karaj, Iran

Received: 2022-09-03 Accepted : 2023-02-08 Published : 2024-01-22

Keywords: Genetic Algorithm, Classification, Support vector machine, Insurance, Bank,

Abstract :

Global competition, dynamic markets, and rapidly shrinking innovation and technology cycles, all have imposed significant challenges on the financial, banking, and insurance industries and the need to data analysis for improving decision-making processes in these organizations has become increasingly important. In this regard, the data stored in the databases of these organizations are considered as valuable sources of information and knowledge needed for organizational decisions. In the present research, the researchers focus on the common customers of the bank and insurance industry. The purpose is to provide a methodology to predict the performance of new customers based on the behavior of previous customers. To this end, a hybrid model based on support vector machine and genetic algorithm is used. The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. The results obtained from customer classification using the proposed model in this research led to customer classification with a high accuracy of 99%.

References:

Abdou, H., Pointon, J., & El-Masry, A. (2008). Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems with Applications, 35(3), 1275-1292. doi:10.1016/j.eswa.2007.08.030
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision support systems, 50(3), 602-613. doi:10.1016/j.dss.2010.08.008
Boyacioglu, M. A., Kara, Y., & Baykan, Ö. K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36(2), 3355-3366. doi:10.1016/j.eswa.2008.01.003
Chen, F. L., & Li, F. C. (2010). Combination of feature selection approaches with SVM in credit scoring. Expert systems with applications, 37(7), 4902-4909. doi:10.1016/j.eswa.2009.12.025
Chu, B. H., Tsai, M. S., & Ho, C. S. (2007). Toward a hybrid data mining model for customer retention. Knowledge-Based Systems, 20(8), 703-718. do:10.1016/j.knosys.2006.10.003
Dorofeev, D., Khrestina, M., Usubaliev, T., Dobrotvorskiy, A., & Filatov, S. (2018, May). Application of machine analysis algorithms to automate implementation of tasks of combating criminal money laundering. In International Conference on Digital Transformation and Global Society (pp. 375-385). Springer, Cham.
Duman, E., & Ozcelik, M. H. (2011). Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications, 38(10), 13057-13063. doi:10.1016/j.eswa.2011.04.110
Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, 33(4), 847-856. doi:10.1016/j.eswa.2006.07.007
Huang, Y. M., Hung, C. M., & Jiau, H. C. (2006). Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem. Nonlinear Analysis: Real World Applications, 7(4), 720-747. doi:10.1016/j.nonrwa.2005.04.006
Jamshidi, M. B., Gorjiankhanzad, M., Lalbakhsh, A., & Roshani, S. (2019, May). A novel multiobjective approach for detecting money laundering with a neuro-fuzzy technique. In 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC) (pp. 454-458). IEEE. doi:10.1109/ICNSC.2019.8743234
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert systems with applications, 32(4), 995-1003. doi:10.1016/j.eswa.2006.02.016
Lee, B., Cho, H., Chae, M., & Shim, S. (2010). Empirical analysis of online auction fraud: Credit card phantom transactions. Expert Systems with Applications, 37(4), 2991-2999. doi:10.1016/j.eswa.2009.09.034
Lee, T. S., Chiu, C. C., Chou, Y. C., & Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50(4), 1113-1130. doi:10.1016/j.csda.2004.11.006
Lin, C. S., Tzeng, G. H., & Chin, Y. C. (2011). Combined rough set theory and flow network graph to predict customer churn in credit card accounts. Expert Systems with Applications, 38(1),8-15. doi:10.1016/j.eswa.2010.05.039
Lin, S. W., Shiue, Y. R., Chen, S. C., & Cheng, H. M. (2009). Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks. Expert Systems with Applications, 36(9), 11543-11551. doi:10.1016/j.eswa.2009.03.029
Luo, S. T., Cheng, B. W., & Hsieh, C. H. (2009). Prediction model building with clustering-launched classification and support vector machines in credit scoring. Expert Systems with Applications, 36(4), 7562-7566. doi:10.1016/j.eswa.2008.09.028
Magomedov, G. S., Dobrotvorsky, A. S., Khrestina, M. P., Pavelyev, S. A., & Yusubaliev, T. R. (2018). Application of Artificial Intelligence Technologies for the Monitoring of Transactions in AML-Systems Using the Example of the Developed Classification Algorithm. Int. J. Eng. Technol, 7, 76-79.
Nie, G., Rowe, W., Zhang, L., Tian, Y., & Shi, Y. (2011). Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38(12), 15273-15285. doi:10.1016/j.eswa.2011. 06.028
Paasch, C. A. (2008). Credit card fraud detection using artificial neural networks tuned by genetic algorithms. Hong Kong University of Science and Technology (Hong Kong), 1-1112.
Plaksiy, K., Nikiforov, A., & Miloslavskaya, N. (2018, August). Applying big data technologies to detect cases of money laundering and counter financing of terrorism. In 2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW) (pp. 70-77). IEEE. 10.1109/W-FiCloud.2018.00017
Sobreira Leite, G., Bessa Albuquerque, A., & Rogerio Pinheiro, P. (2019). Application of technological solutions in the fight against money laundering—A systematic literature review. Applied Sciences, 9(22), 1-29. doi:10.3390/app9224800
Quah, J. T., & Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert systems with applications, 35(4), 1721-1732. doi:10.1016/j.eswa.2007.08.093
Sánchez, D., Vila, M. A., Cerda, L., & Serrano, J. M. (2009). Association rules applied to credit card fraud detection. Expert systems with applications, 36(2), 3630-3640. doi:10.1016/j.eswa.2008.02.001
Šušteršič, M., Mramor, D., & Zupan, J. (2009). Consumer credit scoring models with limited data. Expert Systems with Applications, 36(3), 4736-4744. doi:10.1016/j.eswa.2008.06.016
Tiwari, M., Gepp, A., & Kumar, K. (2020). A review of money laundering literature: the state of research in key areas. Pacific Accounting Review, Vol. 32 No. 2, pp. 271-303. doi:10.1108/PAR-06-2019-0065
Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2009). Customer churn prediction using improved balanced random forests. Expert Systems with Applications, 36(3), 5445-5449. doi:10.1016/j.eswa.2008.06.121
Yap, B. W., Ong, S. H., & Husain, N. H. M. (2011). Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications, 38(10), 13274-13283. doi:10.1016/j.eswa.2011.04.147
Zhao, H., Sinha, A. P., & Ge, W. (2009). Effects of feature construction on classification performance: An empirical study in bank failure prediction. Expert Systems with Applications, 36(2), 2633-2644. doi:10.1016/j.eswa.2008.01.053