Over the course of the last several years, the development and proliferation of information technology has impacted most industries. As the result of new technology, organizations are now able to collect and analyze data for the development and implementation of new products and services. Nowhere is this process more evident than in the banking industry. In the last several years, organizations in the banking industry have widely adopted technologies that allow for both data mining and warehousing. For organizations that have undertaken these changes, the results have been quite positive overall.
[...] As such, data mining clearly has implications for the development and financial success of the banking organization. In an effort to elucidate the myriad of ways in which data mining in banking can be used for marketing, Wallace (1997) notes the case of San Diego-based Advanta Mortgage Corp. As noted by this author, Advanta Mortgage has been able to incorporate data mining for the purposes of cross- selling other products and services to its credit card customers. By identifying specific trends in customer behavior, Advanta has been able to better identify specific customers that might be interested in other programs offered by the organization. [...]
[...] For instance Alexander (1997) in his examination of applying both data mining procedures and OLAP to the banking organization notes that, “Using data mining, you may come up with a model to find who are the most profitable customers. Then you may do more traditional OLAP analysis of that subset of data to see what the impact would be if you lost those customers, how it would affect your bottom line” (p. 61). What this effectively demonstrates is that OLAP provides the necessary tools to further analyze data for the purposes of extracting critical information about operations and customers. [...]
[...] As noted by this author, Lloyds TSB was able to save more than £20m (approximately $ 35.6 million USD) in 2003 by using data mining as a means to identify fraudulent credit card applications and users. Huber argues that increases in credit card fraud have prompted Lloyds TSB to develop data mining procedures that identify specific characteristics of “typical card fraudsters.” Using this data, the organization was able to compare transaction patterns and activity of credit card holders to determine if their activities were indeed fraudulent. [...]
[...] Bank Technology News, 18(9) This article examines the use of data mining to improve CRM at Providence Bank. Alexander, S. (1997). Users find tangible rewards digging into data mines. InfoWorld, 19(27), 61-62. This article provides an overview of data mining and some specific examples of its use. Chye, K.H., & Gerry, C.K.L. (2002). Data mining and customer relationship marketing in the banking industry. Singapore Management Review, 27. This article provides a review of data mining through the use of CRM to improve outcomes for banking organizations. Hormozi, A.M., & Giles, S. (2004). [...]
[...] Rather, all the organization had to do was examine the data that had been previously stored and aggregated by the organization to find the specific trends in data that would allow the organization to make necessary changes and improvements. Overall, Tully notes that the Royal Bank of Canada had considerable success with its efforts to utilize historical data from the database of the organization. While it is quite evident that data warehouses can provide organizations with invaluable information for understanding both clients and operations, research on the development of data warehouses seems to suggest that this process can be quite daunting for many banking organizations. [...]
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