Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/5755
| Title: | A financial data mining model for extracting customer behavior | Authors: | Mak, MKY Ho, TS Ting, SL |
Issue Date: | 2011 | Source: | International journal of engineering business management, 2011, v. 3, no. 3, p. 59-72 | Abstract: | Facing the problem of variation and chaotic behavior of customers, the lack of sufficient information is a challenge to many business organizations. Human analysts lacking an understanding of the hidden patterns in business data, thus, can miss corporate business opportunities. In order to embrace all business opportunities, enhance the competitiveness, discovery of hidden knowledge, unexpected patterns and useful rules from large databases have provided a feasible solution for several decades. While there is a wide range of financial analysis products existing in the financial market, how to customize the investment portfolio for the customer is still a challenge to many financial institutions. This paper aims at developing an intelligent Financial Data Mining Model (FDMM) for extracting customer behavior in the financial industry, so as to increase the availability of decision support data and hence increase customer satisfaction. The proposed financial model first clusters the customers into several sectors, and then finds the correlation among these sectors. It is noted that better customer segmentation can increase the ability to identify targeted customers, therefore extracting useful rules for specific clusters can provide an insight into customers' buying behavior and marketing implications. To validate the feasibility of the proposed model, a simple dataset is collected from a financial company in Hong Kong. The simulation experiments show that the proposed method not only can improve the workflow of a financial company, but also deepen understanding of investment behavior. Thus, a corporation is able to customize the most suitable products and services for customers on the basis of the rules extracted. | Keywords: | Association rules mining Clustering Customer behavior Data mining Financial industry |
Publisher: | SAGE Publications | Journal: | International journal of engineering business management | EISSN: | 1847-9790 | DOI: | 10.5772/50937 | Rights: | The article is licensed under a Creative Commons Attribution 3.0 Unported License <http://creativecommons.org/licenses/by/3.0/> |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Mak_Financial_Data_Mining.pdf | 678.46 kB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
117
Last Week
2
2
Last month
Citations as of May 28, 2023
Downloads
114
Citations as of May 28, 2023
SCOPUSTM
Citations
11
Last Week
0
0
Last month
0
0
Citations as of May 25, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.