Evaluation Framework For Core Banking Modules Based On Business Intelligence Criteria

Document Type : Research Paper

Authors

1 Assistant Prof., Faculty of Management Department, Tehran University, Tehran, Iran

2 MSc., in Information Technology Management, Mehr Alborz University, Tehran, Iran

Abstract

This research attempted to evaluate Business Intelligence criteria accelerating further development of Core Banking System with creating meaningful analysis, decision support environment and optimizing the investments. By reviewing the related literature, Business Intelligence criteria were determined and the importance and priority of each criterion specified on the basis of questionnaire and doing Friedman and Binomial tests. This research presented an evaluation model based on the fuzzy multi-criteria decision making (Fuzzy TOPSIS) method. In the fuzzy-based method, weight of each criterion and results of assignable intelligence were described using linguistic terms, which can also be expressed as triangular fuzzy numbers. According to the findings, Risk Management System ranked as the first module with the largest distance from the negative ideal which indicated that this system had appropriate Business Intelligence capabilities to fortify decision support environment.
 

Keywords

Main Subjects


Awasthi, A., Chauhan, S. S. & Goyal, S. (2010). A fuzzy multicriteria approach for evaluating environmental performance of suppliers. International Journal of Production Economics, 126(2): 370–378.
Bose, R. (2009). Advanced analytics: opportunities and challenges. Industrial Management & Data Systems, 109(2): 155-172.
Cao, L., Zhang, C., Luo, D., Dai, R. (2007). Intelligence metasynthesis in building business intelligence systems. Institute of Automation, Chinese Academy of Science, China, LNAI 4845.
Chou, D. & Bindu Tripuramallu, H. (2005). BI and ERP integration.Department of Computer Information Systems.Information Management computer security, 13(5): 340-349.
Elbashir, M., Collier, P. & Davern, M. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3): 135-153.
Entezar, E. (2011). Analysis and ranking the obstacles and challenges in performing and reforming core banking based on Ferguson model (case study: agricultural bank). Procedia – social and behavioral sciences, 25: 375-383.
Fathian, M. & Zanjani, M. (2005). A modular approach to ERP system selection. The 4th International Conference On Industerial engineering. Tehran, Tarbiyat Modares University.(in Persian)
Ghazanfari, M., Jafari, M., Rouhani, S. (2011). A tool to evaluate the business intelligence of enterprise systems. Scientia Iranica, 18(6): 1579-1590.
Kahraman, C., Ates, N.Y., Çevik, S., Gülbay, M. & Erdogan, S. A. (2007). Hierarchical fuzzy TOPSIS model for selection among logistics information technologies. Journal of Enterprise Information Management, 20(2): 143–168.
Lauria, E. J. & Tayi, G. K. (2005). The Quest for Business Intelligence. Book Chapter In Erfolgsfaktor Innovation (pp. 321-333). Springer Berlin Heidelberg, ISBN: 3540245162.
Lin, Y., Tsai, K., Shiang, W., Kuo, T. & Tsai, C. (2009). Research on using ANP to establish a performance assessment model for business intelligence systems, Expert Systems with Applications, 36(2): 4135-4146.
Lönnqvist, A. & Pirttimäki, V. (2006). The Measurement of Business Intelligence. Information Systems Management, 23(1): 32-40.
Mohaghar, A., Karalux, Hosseini, A. & Monshi, A. (2009). Use of Business Intelligence as a Strategic Information Technology in Banking: Farud Discovery & Detection. The journal of Information Technology Management, 1(1): 105-120. (in Persian)
Nie, L., Lu, J. & Zhang, G. (2007). Cognitive orientation in business intelligence systems. Springer-Verlage Berlin Heidelberg. Studies in computational intelligence (SCI), 117: 55-72.
Olszak, C. M. & Ziemba, E. (2007). Approach to building and implementing business intelligence systems. Interdisciplinary Journal of Information, Knowledge, and Management, 2: 134-148.
Petrini, M. & Pozzebon, M. (2009). Managing sustainability with the support of business intelligence: Integrating socio-environmental indicators and organizational context. Journal of Strategic Information Systemsn, 18(4):178-191.
Ranjan, J. (2008). Business justification with business intelligence. The Journal of Information and Knowledge Management Systems, 38(4): 461–475.
Research and planning center. (2007). Core Banking System. Tehran: The center of Mellat Bank researching and planning. (in Persian)
Rouhani, S. & Ravasan, A. Z. (2014). A practical framework for assessing business intelligence competencies of enterprise systems using fuzzy ANP approach. International Journal of Applied Decision Sciences, 8(1): 52-82.
Rouhani, S., Ghazanfari, M. & Jafari, M. (2012). Evaluation model of business intelligence for enterprise systems using fuzzy topsis. Internatonla journal of expert systems with application, 39(3): 3764-3771.
Sahay, B.S. & Ranjan, J. (2008). Real time business intelligence in supply chain analytics. Institute of Management Technology, Ghaziabad, India. Information Management & Computer Security, 16 (1): 28-48.
Ward, J., Hemingway, C. & Daniel, E. (2005). A framework for addressing the organisational issues of enterprise systems implementation. Journal of Strategic Information Systems, 14(2): 97-119.
Yamini, Z. & Yamini, S. (2009). The role of business intelligence in banking services marketing. The First International conference on Banking Services Marketing, Tehran: December 12-13 2009.
Zhang, W. & Zhu, F. (2012). An Evaluation Model of Software Testing Management in Core Banking System Programme. Physics Procedia, 25: 1857-1862.