Effectiveness of Data mining in Banking Industry: An empirical study

Md Rashid Farooqi, Naiyar Iqbal


Data mining is becoming important area for many corporate firms including banking industry. It is a process of analyzing the data from numerous perspective and finally summarize it into meaningful information, so data mining assist the bankers to take concrete decision. This paper is an attempt to analyse the data mining technique and its useful application in banking industry like marketing and retail management, CRM, risk management and fraud detection.


Data mining, Knowledge Discovery in database, customer relationship management, banking

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Aburrous, M. R., Hossain, A., Dahal, K., & Thabatah, F. (2009, September). Modelling intelligent phishing detection system for e-banking using fuzzy data mining. In CyberWorlds, 2009. CW'09. International Conference on (pp. 265-272). IEEE.

Aburrous, M., Hossain, M. A., Dahal, K., & Thabtah, F. (2010). Intelligent phishing detection system for e-banking using fuzzy data mining. Expert systems with applications, 37(12), 7913-7921.

Bhambri, V. (2011). Application of data mining in banking sector. IJCST, 2(2), 199-202.

Bhasin, M. L. (2006). Data mining: A competitive tool in the banking and retail industries. The Chartered Accountant, 588-594.

Bhattacharyya, S. (2000, August). Evolutionary algorithms in data mining: Multi-objective performance modeling for direct marketing. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 465-473). ACM.

Chun, S. H., & Kim, S. H. (2004). Data mining for financial prediction and trading: application to single and multiple markets. Expert Systems with Applications, 26(2), 131-139.

Dass, R. (2006). Data mining in banking and finance: A note for bankers. Indian Institute of Management Ahmadabad.

Elkan, C. (2001, August). Magical thinking in data mining: lessons from CoIL challenge 2000. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-431). ACM.

Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys (CSUR), 38(3), 9.

He, J., Zhang, Y., Shi, Y., & Huang, G. (2010). Domain-driven classification based on multiple criteria and multiple constraint-level programming for intelligent credit scoring. IEEE Transactions on Knowledge and Data Engineering, 22(6), 826-838.

Hormozi, A. M., & Giles, S. (2004). Data mining: A competitive weapon for banking and retail industries. Information systems management, 21(2), 62-71.

Iqbal, N. & Islam, M. (2016).From Big Data to Big Hope: An outlook on recent trends and challenges. Journal of Applied Computing, 1(1):1-12.

Scott, R. I., Svinterikou, S., Tjortjis, C., & Keane, J. A. (1998). Experiences of using Data Mining in a Banking Application. In Proc. 2nd WSES/IEEE/IMACS-Int'l Conf. Circuits, Systems Computers ((IMACS CSC'98), 1998, pp. 343-346. doi: 10.1. 1.55. 2093.

Sharma, A., & Panigrahi, P. K. (2013). A review of financial accounting fraud detection based on data mining techniques. arXiv preprint arXiv:1309.3944.

Sudhakar, M., & Reddy, C. V. K. (2014). Application Areas of Data Mining in Indian Retail Banking Sector. Global Journal of Computer Science and Technology, 14(5-C), 11.

Sundari, P., & Thangadurai, K. (2010). An empirical study on data mining applications. Global Journal of Computer Science and Technology, 10(5).

DOI: https://doi.org/10.26483/ijarcs.v8i5.3441


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