Presenting a Conceptual Framework to Increase the Return and Reduce Risk (A case study: customers of Mellat Bank of Arak)
Subject Areas : Financial and Economic ModellingMohammad Moradi 1 , Mohammad Sadegh Horri 2 , iraj Nouri 3
1 - Department of Management, Arak Branch, Islamic Azad University, Arak, Iran
2 - Department of Management, Arak Branch, Islamic Azad University, Arak, Iran
3 - Department of Management, Arak Branch, Islamic Azad University, Arak, Iran
Keywords: RFM Model, Credit Risk, Customer, profitability,
Abstract :
The objective of this study is to present a framework to increase the return and profitability and reduce credit risk of Mellat Bank customers by developing the RFM model. In this study, which was conducted as a case study in Mellat Bank of Iran, first the variables of RFM model were identified. In the next step, relevant weights of RFM variables were calculated using AHP technique. In the next step, using the K-means algorithm, customers were clustered based on weighted RFM and extended RFM. The result included customer clusters. The results indicated that the three clusters 5, 1, and 7 obtained the highest scores for receiving facilities and the coefficients for receiving facilities were equal to 0.271, 0.173, and 0.556, respectively. By determining the facility coefficient for the cluster and consequently for the customers presented in these top groups, granting facility becomes more transparent and more purposeful, and therefore, it will help the company increase profitability, reduce the churn among high-efficiency customers, and create value for customers. This research demonstrates a systematic method for granting facilities to recognize the true value based on the capability and prevention of arbitrary acts
[1] Acharya, V.V., Naqvi, H., The Seeds of a Crisis: A Theory of Bank-Liquidity and Risk-Taking over the Business Cycle, Journal of Financial Economics, 2016, 106(2), P. 349-366. Doi: 10.1016/j.jfineco.2012.05.014.
[2] Alfansi, L., and sergeant, A., Market Segmentation in the Indonesian Banking Sector, International journal of Bank Marketing, 2017, 18(2), P. 64-74. Doi:10.1108/02652320010322976.
[3] Arani, Sh., Innovation in Financial Instruments in Islamic Banking, Proceedings of the 11th Islamic Banking Conference, Tehran, 2011, (in Persian).
[4] Bakhtiyari A., Davoodi, Ch.A., Sayyed Mohammad, R., Abdolbaghi Ataabadi, A., Designing and Evaluating the Profitability of Linear Trading System Based on the Technical Analysis and Correctional Property, Advances in Mathematical Finance and Applications, 2022, 7(1), P. 49-63. Doi: 10.22034/AMFA.2021.1906285.1474.
[5] Baral, K.J., Health checkup of commercial banks in the framework of CAMEL: a case study of joint venture banks in Nepal, The Journal of Nepalese Business Studies, 2015, 1(2), P. 231-241.
[6] Baran, R., Galka, R., and Strunk, D., Principles of Customer Relationship Managenent, Thomson South- Western Pub, 2018, P. 244.
[7] Cheng, C., Chan, Y., Classifying the segmentation of customer value via RFM model and RS theory, Journal of Expert systems with Applications, 2009, 36, P. 4176-4184. Doi: 10.1016/j.eswa.2008.04.003.
[8] Chris Rygielski J-CW. and Davi, C., Data mining techniques for customer Relationship Management, Technology in Society, 2002, 11(3), P. 483-502.
[9] Dunham, M., Data Mining Introductory and Advanced Topics, Upper Saddle River, Prentice Hall Pub, 2002.
[10] Eshghi, K., Presenting a Logistic Planning Model for Improvement in Earthquake Response Phase, International Quarterly Journal of Industrial Engineering and Production Management, 2012, 4(23) (in Persian).
[11] Gorinchas, L.K., Mining the customer credit using classifcation and regression tree and multivariate adaptive regression splines, Computational Statistics and Data Analysis, 2016, 50, P. 1113-1130.
[12] Gwyn, p. Hausman, p., The commercial use of segmentation and predictive modeling techniques for data base marketing in the Netherlands, Journal of Decision support systems, 1996, 34, P. 471-481.
[13] Izadikhah, M., DEA Approaches for Financial Evaluation - A Literature Review, Advances in Mathematical Finance and Applications, 2022, 7(1), P.1-36. Doi: 10.22034/AMFA.2021.1942092.1639.
[14] Keaton, A., Segmentation of bank customers by expected benefits and attitudes, International Journal of Bank Marketing, 1999, 19, P. 6-17. Doi: 10.22059/ijms.2021.305952.674132.
[15] Kim, S., Soojung, T., Ho Suh, E. and Seok Hwang, H., Customer segmentation and strategy development based on Customer live time Value, Journal of Expert Systems with Applications, 2006, 31, P. 101-107.
[16] Kraft, R. and Jankov, R., Visualizing RFM Segmentation. April 22.2004 SIAM: Society for Industrial and Applied Mathematics, 2005.
[17] Mc carty, J. and Hastak, M., segmentation approaches in data-mining, journal of Business Research, 2017, 60, P. 656-662. Doi:10.1016/j.jbusres.2006.06.015.
[18] Mousavian, S. A., Kavand, M., Hosseinpour, M., Advocacy Securities; Inadequate tool for financing the petrochemical industry, Islamic Economics Quarterly, 2012, 12(47 (in Persian).
[19] NickPai, A., Risk Management, Qazvin University of Medical Sciences, Winter 2006, (in Persian).
[20] Scala, N., The Handbook of Data Mining, London, Lawrence Erlbaun Association (LEA) Pubishing, 2011.
[21] Taleby, Y., Vatanparast, M., and Azadi, K., Financial Assessment using a Fuzzy Analytical Hierarchical Process Method, Advances in Mathematical Finance and Applications, 2022, 7(1), P. 133-148. Doi: 10.22034/AMFA.2021.1917694.1531.