Presentation of a Two Stages Model Based on Data Mining for Evaluation of Common Customers of Bank and Insurance Companies

Subject Areas : Financial Knowledge of Securities Analysis

Hamidreza  Amir hasankhani ¹ (PhD Candidate of IT Management Department, Emirates Branch, Islamic Azad University, Dubai, UAE)
abbass  toloie ² (Professor of Industrial Management Department, School of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran. (Corresponding Author):)
Alireza  poorebrahimi ³ (Assistant Professor of Management Department, School of Management, Karaj Branch, Islamic Azad University, Karaj, Iran)
reza  radfar ⁴ (Professor of Industrial Management Department, School of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran. (Corresponding Author):)

Received: 2021-09-25 Accepted : 2021-09-25 Published : 2021-08-23

Keywords: Customer Relationship Manageme, data mining, Bank, insurance,

Abstract :

Exploration of knowledge from database and data mining is one of the most important tools for customer relationship management Which can help the organization to find useful information or their interesting knowledge. Today, banks and insurance companies have numerous and extensive databases that contain information about exchanges and other details related to their customers. Valuable business information can be retrieved from these data warehouses. However, support for such analyzes and decision-making will not be possible using traditional reporting languages. Therefore, considering the importance of the common customer’s information of the bank and insurance, they should be analyzed as carefully as possible. In this research, by collecting and analyzing the information of joint customers of the bank and insurance, a methodology based on the data mining is presented to evaluate customers according to their functional indicators in the field of banking and insurance. We will also predict the behavior of new customers by analyzing historical customer behavior using a two-step approach based on unsupervised learning and supervised learning.

References:

*      [4]. مینایی، بهروز؛ اصغری، فاطمه؛ "بکارگیری داده کاوی برای کشف مدل امتیازبندی و تحلیل رفتاری مشتریان بانک"، دومین کنفرانس داده کاوی ایران، دانشگاه صنعتی امیرکبیر، 1387.

*      [1]. Filippidou, D., Keane, J.A., Svinterikou, S. and Murray, J., Data Mining for Business Improvement: Applying the HyperBank Approach, PADD’98 - 2nd Int.Conf., on the Practical Application of Knowledge Discoveryand Data Mining, Practical Application Company, 1998, pp.1-10.

*      [2]. Fayyad, U., Shapiro, G.S. and Smyth, P., From Data Mining to Knowledge Discovery in Databases, AI Magazine, 1996, pp.37-54.

*      [3]. Wiz, K., The Hidden World of Data Mining, Think Analytics Corporation, 2002, pp.1-21.

*       [5]. Antonios Chorianopoulos and Konstantinos Tsiptsis, Data Mining Techniques in CRM: John Wiley and Sons, 2009.

*      [6]. Lindgreen Adam and Antioco Michael, "Customer relaitonship management: the case of a European bank, "Marketing Intelligence & Planning. Vol. 23, no. 2, pp. 136-154, 2005.

*      [7]. Nie G, Rowe. W, Zhang. L, Tian. Y, and Shi. Y, "Credit card churm forcasting by logistic regression and decision tree, "Expert Systems with Applications, 2011.

*      [8]. Koh Hian Chye and Chan Kin Leong Gerry, Data mining and customer relationship marketing in the banking industry.: Singapore Management Review, 2000.

*      [9]. Hung. C, Jiau. H Huang. Y, "Evaluation of neural networks and data mining methods on credit assessment task for class imbalance problem," Nonlinear Analysis: Real World Application, pp. pp 720-747, 2006.

*      [10]. Jamshidi, M.B.; Gorjiankhanzad, M.; Lalbakhsh, A.; Roshani, S. A Novel Multiobjective Approach for Detecting Money Laundering with a Neuro-Fuzzy Technique. In Proceedings of the 2019 IEEE 16^th International Conference on Networking, Sensing and Control (ICNSC), Ban_, AB, Canada, 9–11 May 2019; pp. 454–458.

*      [11]. Magomedov, G.S.; Dobrotvorsky, A.S.; Khrestina, M.P.; Pavelyev, S.A.; Yusubaliev, T.R. 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. 2018, 7, 76–79.

*      [12]. Dorofeev, D.; Khrestina, M.; Usubaliev, T.; Dobrotvorskiy, A.; Filatov, S. Application of Machine Analysis Algorithms to Automate Implementation of Tasks of Combating Criminal Money Laundering. In Digital Transformation and Global Society, DTGS 2018, Communications in Computer and Information Science; Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O., Eds.; Springer: Cham, Switzerland, 2018.

*      [13]. Plaksiy, K.; Nikiforov, A.; Miloslavskaya, N. Applying Big Data Technologies to Detect Cases of Money Laundering and Counter Financing of Terrorism. In Proceedings of the 2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), Barcelona, Spain, 6–8 August 2018; pp. 70–77.

*      [14]. Sobreira Leite, Gleidson, Adriano Bessa Albuquerque, and Plácido Rogerio Pinheiro. "Application of Technological Solutions in the Fight Against Money Laundering—A Systematic Literature Review." Applied Sciences 9.22 (2019): 4800.

*      [15]. Ryder, Nicholas. "Money laundering: A review of the UKs anti-money laundering policy." (2020).

*      [16]. Tiwari, Milind, Adrian Gepp, and Kuldeep Kumar. "A review of money laundering literature: the state of research in key areas." Pacific Accounting Review (2020).

*      [17]. Kamath, D., Pavithra, K., & Pujari, K. (2019). Data mining techniques applied in banking sector-A review. International Journal of Social and Economic Research, 9(3), 358-365.

*      [18]. Kanchana, M., Chavan, P., & Johari, A. (2020). Detecting Banking Phishing Websites Using Data Mining Classifiers (No. 2855). Easy Chair.

*      [19]. Shahbazi, F. (2020). Using Decision Tree Classification Algorithm to Design and Construct the Credit Rating Model for Banking Customers. IOSR Journal of Business and Management, 21(3, Series 2), 24-28.

*      [20]. Dinçer, H., Yüksel, S., Canbolat, Z. N., & Pınarbaşı, F. (2020). Data Mining-Based Evaluating the Customer Satisfaction for the Mobile Applications: An Analysis on Turkish Banking Sector by Using IT2 Fuzzy DEMATEL. In Tools and Techniques for Implementing International E-Trading Tactics for Competitive Advantage (pp. 320-339). IGI Global.

*      [21]. Kaur, M., Bhaddal, P., & Singh, G. (2019). Calculation of client credit risk prediction in banking sector using data mining.

*      [22]. Hassani, H., Huang, X., & Silva, E. (2018). Digitalization and big data mining in banking. Big Data and Cognitive Computing, 2(3), 18.