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Abstract
Advances in computer hardware and data mining software have made data mining accessible and affordable to many businesses. Hence, it is no surprise that data mining has gained widespread attention and increasing popularity in the commercial world in recent years. Data mining provides the technology to analyse mass volume of data and/or detect hidden patterns in data to convert raw data into valuable information. This paper discusses the potential usefulness of data mining for customer relationship management (CRM) in the banking industry. First, the paper introduces the CRM concept and summarises the data mining methodology and tools. Second, it discusses the data mining literature, particularly its applications in banks. Third, it illustrates a possible CRM application of data mining in banking. Finally, it suggests other potential data mining banking applications and highlights some of the limitations of data mining.
Introduction
Since the mid-1990s, three new interrelated areas that emphasised obtaining more information from data have emerged strongly in information systems and information technology. They are data warehousing, knowledge management, and data mining. Coupled with advances in both computer hardware and software, many applications are more accessible and affordable to businesses than before. This paper focuses on data mining, which aims to identify valid, novel, potentially useful and understandable correlations and patterns in data (Chung and Gray, 1999). In particular, the paper explores the potential usefulness of data mining in banks in the area of customer relationship management (CRM). Although the paper focuses mainly on the banking industry, the issues and applications discussed are applicable to other industries as well. See, for example, Koh and Low (2001) on data mining applications in the insurance industry and Koh and Leong (2001) on data mining applications in the healthcare industry.
The remainder of the paper is organised into five sections. The first section introduces customer relationship management in general. The second section discusses the data mining methodology and tools, and the data mining literature. The third section illustrates possible banking applications and examples of data mining in the literature, both in the context of customer relationship management. The fourth section gives an illustration of how data mining can be applied to chum modelling (that is, the prediction of customer turnover) in banks. Finally, the...