Designing a Decision Support System for Prioritizing of Banks’ Branches

Document Type: Research Paper

Authors

1 Assistant Prof., Faculty of Economic and Social Sciences, Alzahra University, Tehran, Iran

2 BSc., Faculty of Economic and Social Sciences, Alzahra University, Tehran, Iran

Abstract

 The banks are the most important symbol of monetary market in any country without exception. As the optimum function of the banks have important role in economic development of the country, creating the ground for qualitative and quantitative promotion of the banks performance in healthy competition can play important role in achieving the goals. One of the methods helping the bank’s branches to identify the competitive position and performance quality is evaluation of their performance from various aspects and their ranking. The aim of present study is to design a decision support system based on Promethee II method as a complete and comprehensive method and by automatic ranking, despite considering the qualitative and quantitative indices, it is done in by low time and costs with high precision. Thus, it is possible to analyze the sensitivities to be sure of the initial selections and changing the indices and values dependent upon the environmental changes are provided for branches evaluators. The system output is the rank associated to each branch based on Promethee II method.

Keywords

Main Subjects


Albadvi, A., Chaharsooghi, S. K., & Esfahanipour, A. (2007). Decision making in stock trading: An application of PROMETHEE. European Journal of Operational Research, 177(2): 673-683.
Alter, S. (2004). A work system view of DSS in its fourth decade. Decision Support Systems, 38(3): 319-327.
Azar, A., Khadivar, A., Aminnaseri, M.R. & Anvarirostami, A.A. (2010). Presenting Architecture of performance-based budgeting system with the approach of intelligent decision support system. Management researches in Iran, 15(3):1-22. (in Persian)
Barns, J.P. (1982). Lingenierie de la decision. Elaboration dinstruments daide a la decision. Method PROMETHEE. In: Nadeau, R., Landry, M. (Eds.), Laide a la Decision: Nature, Instruments et Perspectives Davenir. Presses de Universite Laval, Quebec, Canada, 183–214.
Barros, C. P., Ferreira, C., & Williams, J. (2007). Analysing the determinants of performance of best and worst European banks: A mixed logit approach. Journal of Banking & Finance, 31(7): 2189-2203.
Beheshtinia, M.A. & Farazmand, N. (2015).  A novel decision support system for discrete cost-CO2 emission trade-off in construction projects: the usage of Imitate Genetic Algorithm. Journal of information technology management, 7(1): 23-48. (in Persian)
Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European journal of Operational research, 200(1): 198-215.
Beynon, M. J. & Wells, P. (2008). The lean improvement of the chemical emissions of motor vehicles based on preference ranking: A PROMETHEE uncertainty analysis. Omega, 36(3): 384-394.
Brans, J. P. & Vincke, P. (1985). Note-A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making).Management science, 31(6): 647-656.
Dempster, M. A. H., & Ireland, A. M. (1991). Object-oriented model integration in a financial decision support system. Decision Support Systems, 7(4): 329-340.
Doumpos, M. & Zopounidis, C. (2010). A multicriteria decision support system for bank rating. Decision Support Systems, 50(1): 55-63.
Elahi, S., Khadivar, A. & Hasanzadeh, H. (2012).  Designing a Decision Support Expert System for Supporting the Process of Knowledge Management Strategy Development .Journal of information technology management, 3(8): 43-62. (in Persian)
Eslami , Z., BahramiZonoor. M., Rajabi. M.& Mihani, M. (2011). The Necessity of develop a model of rating the banks and the proposed Model. Center of Investigation and risk  control. (in Persian)
Eslami Bidgoli, G.R. & Kashanipoor, M. (2004). Comparison and evaluation of methods for bank branches performance assessment and provide a suitable model. Journal of the accounting and auditing review, 38(1): 3-27. (in Persian)
Fadaeinejad, M.E., Sadeghi-Sharif, S.J. & Banaeian, H. (2011). Designing a Decision Support System for resource mobilization; (A case study in Agriculture Bank). Journal of information technology management, 3(6): 89-108. (in Persian)
García, F., Guijarro, F., & Moya, I. (2010). Ranking Spanish savings banks: A multicriteria approach. Mathematical and computer modelling, 52(7): 1058-1065.
Ho, C. T. (2006). Measuring bank operations performance: an approach based on Grey Relation Analysis. Journal of the Operational Research Society, 57(4): 337-349.
Iranzadeh, S. & Barghi, A. (2009). Rating and evaluate the performance of the bank using the technique of principal component analysis. Journal of management, 6(14).
Jebelameli, F. & Rasoulinejad, E. (2010). Using network analysis process model in the ranking of the branches of the bank: A Case Study of Bank Saderat. Journal of Research and Economic Policy, 55(1): 107-124. (in Persian)
Kosmidou, K. & Zopounidis, C. (2008). Measurement of bank performance in Greece. South- Eastern Europe Journal of Economics, 6(1):79–95.
Kumar, S. & Arora, S. (1995). A model for risk classification of banks. Managerial and Decision Economics, 16(2): 155-165.
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.
Manandhar, R. & Tang, J.C.S. (2002). The Evaluation of Bank Branch Performance Using Data Envelopment Analysis: A Framework. The Journal of High Technology Management Research, 13(1): 1-17.
Mareschal, B. & Brans, J. P. (1991). Bank Adviser: An industrial evaluation system. European Journal of Operational Research, 54(3): 318-324.
Mareschal, B. & Mertens, D. (1992). BANKS a Multicriteria, PROMETHEE-based, Decision Support System for the Evaluation of the International Banking Sector. Journal of Decision Systems, 1(2-3): 175-189.
Min, D. M., Kim, J. R., Kim, W. C., Min, D. & Ku, S. (1996). IBRS: Intelligent bank reengineering system. Decision Support Systems, 18(1): 97-105.
Morais, D.C. & de Almeida, A.T. (2006). Group decision model to manage water losses. Pesquisa Operational, 26 (3): 567–584.
Moynihan, G. P., Purushothaman, P., McLeod, R. W. & Nichols, W. G. (2002). DSSALM: a decision support system for asset and liability management. Decision Support Systems, 33(1), 23-38.
Nasiri, H., Alavipanah, S.K., Matinfar, H., Azizi. A. & Hamzeh, M. (2011). Implementation of ecological agriculture model with PROMETHEE II and Fuzzy AHP approach in GIS (Case Study: Marvdasht city). Environmental Studies, 38(3): 109-122.
Neely, M. & Ken, P. (1995). Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management, 15 (4): 80 – 116.
Nunnally, J. C., Bernstein, I. H., & Berge, J. M. T. (1967). Psychometric theory (Vol. 226). New York: McGraw-Hill.
Poorkazemi, M.H. (2007). Grading bank branches. Economic Bulletin, 305-348.
Purcell, D. E., O'Shea, M. G. & Kokot, S. (2007). Role of chemometrics for at-field application of NIR spectroscopy to predict sugarcane clonal performance. Chemometrics and Intelligent Laboratory Systems87(1): 113-124.
Ravi, V., Kurniawan, H., Thai, P. N. K., & Kumar, P. R. (2008). Soft computing system for bank performance prediction. Applied soft computing8(1), 305-315.
Sadrabadi, A. & Asadian Ardakani, F. (2013). Developing a Decision Support System for Allocating Human Resources in Software Projects. Journal of information technology management, 5(3): 203-222. (in Persian)
Saremi, M. & Molaee, H. (2003).  A model for performance evaluation and rating of the bank branches in the bank of refahe kargaran. Management Culture, 1(4): 31-58. (in Persian)
Seçme, N. Y., Bayrakdaroğlu, A. & Kahraman, C. (2009). Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS. Expert Systems with Applications, 36(9): 11699-11709.
Spathis, C., Kosmidou, K., & Doumpos, M. (2002). Assessing profitability factors in the Greek banking system: A multicriteria methodology. International Transactions in operational research, 9(5): 517-530.
Stewart, R. A., & Mohamed, S. (2001). Utilizing the balanced scorecard for IT/IS performance evaluation in construction. Construction innovation,1(3): 147-163.
Taghavifard, M.T. & Pooti, N. (2013). Design and Development of Decision Support System for Ranking Rapid Prototyping Techniques and Selecting the Best Technique in Automobile Industry. Journal of information technology management, 5(2):1-22. (in Persian)
Wu, H. Y., Tzeng, G. H., & Chen, Y. H. (2009). A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard. Expert Systems with Applications, 36(6): 10135-10147.
Zhu, Z., Xu, L., Chen, G., & Li, Y. (2010). Optimization on tribological properties of aramid fibre and CaSO 4 whisker reinforced non-metallic friction material with analytic hierarchy process and preference ranking organization method for enrichment evaluations. Materials & Design, 31(1): 551-555.