[01] Malihe Rostami, Department of finance and accounting, Electronic Branch, Islamic Azad University, Tehran, Iran. [02] Ahmad Goudarzi, Department of finance and accounting, Electronic Branch, Islamic Azad University, Tehran, Iran. [03] Mahdi Madanchi Zaj, Department of finance and accounting, Electronic Branch, Islamic Azad University, Tehran, Iran.

This study has been conducted to define Balanced Scorecard model as one of evaluation system in bank. Financial institutions and banks are trying to increase their competitive advantage, so find a comprehensive evaluation model for the performance that is a main key to survive and get competitive position. There are several theories and methods of assessment that can be employed depending on the size and type of organization. Balanced Scorecard (BSC) is one of the measurement systems that cover short and long term plans and strategies and also, internal as well as external control. BSC consider aspects of the financial, customer, internal processes and learning and growth. In this article, aspects of Balanced Scorecard and the importance of each aspect and related indicators are examined. To achieve the research objective Fuzzy Analytical Hierarchy Process (FAHP) is used. At the first step of study, 56 indicators were found based on prior studies and literature which were scrutinized by expert opinions through administering a questionnaire. Ultimately 9 indicators were extracted. In the second step of study, the weight of each indicator is investigated using pair comparison questionnaire based on FAHP approach. According to research, the first priority is customer aspect, the second priority is the financial aspect, third priority is internal processes aspect and the end, learning and growth aspect are the fourth priority. Meanwhile, the “Market rate” and the “Growth rate of customer complaints” and “Customer attract rate” are the most important indicators of customer aspect. “Revenues”, “P/E ratio” and “leverage” are the most important indicators in the financial aspects, the “Electronic transactions share”, “Performance management” and “Research and development costs” are the most important indicators in internal processes aspect and “Employee stability”, “Loan per capita” and “Present reduction in disciplinary matters” are the most important indicators in growing and learning aspect.

Balanced Scorecard (BSC), Customer Aspect, Financial Aspect, Internal Processes Aspect, Learning and Growth Aspect, FAHP

[01] Feizi, A., & Solukdar, A. (2014), Combined with a balanced scorecard approach to performance assessment of the banking industry - with Fuzzy TOPSIS method, Engineering and management of securities, 20, 57-78 [02] 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. [03] 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. [04] Jiang, L., & Liu, H. (2013). A multi-criteria group decision making model for performance evaluation of commercial banks. In Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on (pp. 940-945). IEEE. [05] Wu, H. Y. (2012). Constructing a strategy map for banking institutions with key performance indicators of the balanced scorecard. Evaluation and Program Planning, 35(3), 303-320. [06] Tabari, M., & Araste, F. (2008), The balanced scorecard approach to performance evaluation, Management journal, 5(12), 12-20 [07] Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43, 30-37. [08] 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. [09] Yalcin, N., Bayrakdaroglu, A., & Kahraman, C. (2012). Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert Systems with Applications, 39(1), 350-364. [10] Ghasemi, A., & Ahmadi, S.H. (2013), Evaluation of higher education institutions with the help of a balanced scorecard and multi-criteria decision methods, Journal of Medical Education, 6,10, 38-49 [11] Braam, G. J., & Nijssen, E. J. (2004). Performance effects of using the balanced scorecard: a note on the Dutch experience. Long range planning, 37(4), 335-349. [12] Chen, T. Y., Chen, C. B., & Peng, S. Y. (2008). Firm operation performance analysis using data envelopment analysis and balanced scorecard: A case study of a credit cooperative bank. International Journal of Productivity and Performance Management, 57(7), 523-539. [13] Kaplan, Robert S; Norton, D. P.(1996) .“Translating Strategy Into action The balanced scorecard” Harvard Business School Press. [14] Keyt, J. C. (2001). Beyond strategic control: Applying the balanced scorecard to a religious organization. Journal of Nonprofit & Public Sector Marketing, 8(4), 91-102. [15] Kaplan, Robert S; Norton, D. P. (1992). "The Balanced Scorecard-Measures That Drive Performance". Harvard Business Review (January–February), pp.71-79. [16] Bentes, A. V., Carneiro, J., da Silva, J. F., & Kimura, H. (2012). Multidimensional assessment of organizational performance: Integrating BSC and AHP. Journal of business research, 65(12), 1790-1799. [17] Hoque, Z. (2014). 20 years of studies on the balanced scorecard: Trends, accomplishments, gaps and opportunities for future research. The British accounting review, 46(1), 33-59. [18] Park, J. A., & Gagnon, G. B. (2006). A causal relationship between the balanced scorecard perspectives. Journal of Human Resources in Hospitality & Tourism, 5(2), 91-116. [19] Sundin, H., Granlund, M., & Brown, D. A. (2010). Balancing multiple competing objectives with a balanced scorecard. European Accounting Review, 19(2), 203-246. [20] Anand, M., Sahay, B. S., & Saha, S. (2005). Balanced scorecard in Indian companies. Vikalpa, 30(2), 11-25. [21] Ardabili, F. S. (2011). New Framework for Modeling Performance Evaluation for Bank Staff Departments. Australian Journal of Basic and Applied Sciences, 5(10), 1037-1043. [22] Alidade, B., & Ghasemi, M. (1393), Ranking the Branches of Bank Sepah of Sistan Baluchistan Using Balanced Score Card and Fuzzy Multi-Attribute Decision Making Methods. Research Journal of Recent Sciences ISSN, 2277, 2502. vol. 4(1), 17-24 [23] Noori, B. (2015). Prioritizing strategic business units in the face of innovation performance: Combining fuzzy AHP and BSC. International Journal of Business and Management, 3(y: 2015: i: 1: p: 36-56), 36-56. [24] Zhang, Q., Wu, C., & Guo, W. (2014, August). Performance evaluation of bank microfinance based on fuzzy mathematics and AHP. In Fuzzy Systems and Knowledge Discovery (FSKD), 2014 11th International Conference on (pp. 153-158). IEEE. [25] Jafari-Eskandari, M., Roudabr, N., & Kamfiroozi, M. H. (2013). Banks' Performance Evaluation Model Based on The Balanced Score Card Approach, Fuzzy DEMATEL and Analytic Network Process. International Journal of Information, Security and Systems Management, 2(2), 191-200. [26] Sedaghat, M., & Sari, I. (2013). A productivity improvement evaluation model by integrating AHP, TOPSIS and VIKOR methods under Fuzzy environment. Economic Computation and Economic Cybernetics Studies and Research, 47(1), 235-258. [27] Shivakumar, U., Ravi, V., & Venkateswaran, T. R. (2013, December). Quantification of Balanced Scorecard Using Crisp and Fuzzy Multi Attribute Decision Making: Application to Banking. In Emerging Trends in Engineering and Technology (ICETET), 2013 6th International Conference on (pp. 164-170). IEEE. [28] AkkoÇ, S., & Vatansever, K. (2013). Fuzzy performance evaluation with AHP and Topsis methods: evidence from turkish banking sector after the global financial crisis. Eurasian Journal of Business and Economics, 6(11), 53-74. [29] Arman, M., & Salehi S.J., & Mojdehi, S., & Nazarali, A, (2012), The amount of inconsistency hierarchical structure and paired comparison matrices in the analytic hierarchy process and fuzzy, Journal of Industrial Management Studies, 10,27, 89-112 [30] Safaii GH. AB., & Aghajani, H., & Dargahi, H. (2012), Proposed approach combines the techniques of fuzzy multi-criteria decision to prioritize strategies to achieve world-class manufacturing, Journal of the Operational Research and Applications, 9,2(33), 81-99 [31] Najafi, E., & Aryanezhad, M. (2011). A BSC-DEA approach to measure the relative efficiency of service industry: A case study of banking sector. International Journal of Industrial Engineering Computations, 2(2), 273-282. [32] Rahmani, K., Bohloli, N., Sadeghzade, B., (2012), Developed a mathematical model of fuzzy quality function deployment approach, beyond management, 5,20, 7-34 [33] Habibi, A., Izadyar, SH., Sarafrazi, A., (2014), Fuzzy multi-criteria decision, Katibe gil, 1, 0-72-5466-600-978 [34] Dincer, H., & Hacioglu, U. (2013). Performance evaluation with fuzzy VIKOR and AHP method based on customer satisfaction in Turkish banking sector. Kybernetes, 42(7), 1072-1085.