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Research on Theapplication of Data Mining Technology in Banking CRM

Author: GuoWei
Tutor: ZhaoShouXiang
School: Beijing Technology and Business University
Course: Management Science and Engineering
Keywords: Customer Relationship Management Customer classification Data Mining Decision tree classification algorithm C4.5 algorithm IC4.5 algorithm WEKA
CLC: F830.49
Type: Master's thesis
Year: 2010
Downloads: 111
Quote: 0
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Abstract


With China's rapid economic development and growth , the banking environment has undergone tremendous changes , in an increasingly competitive , commercial banks gradually customer-centric , and use of customer relationship management , customer knowledge management theories , methods and tools to manage customers, customer service . Taking into account the huge difference between customers of the bank customers , it is necessary to classification , so that the bank can accurately grasp the status of existing customers , and to adopt a different marketing policies for different categories of customers . In view of the important role of the customer classification in the banking CRM , this article uses the combination of theory and application of research methods , the bank 's consumer credit data preprocessing operation , using the very popular data mining decision tree classification algorithm based bank to build CRM customer classification mining model . Firstly, using the C4.5 algorithm (release 8) to build a bank the CRM customer classification model , after an assessment of the classification of the model prediction results , found that the predicted results are unsatisfactory . Then , through the analysis of the characteristics of data on bank customers , and found the C4.5 algorithm insufficient and propose an improved algorithm IC4.5 . IC4.5 algorithm in the following improvements: combined the Fayyad boundary point determination theorem presents a continuous attribute discretization improvement program ; propose an improved discrete attributes splitting technique binary decision tree generated by the technology to some extent reduce the complexity of the decision tree ; study and two after pruning techniques REP and EBP . Finally, the IC4.5 algorithms used in banking CRM, customer classification by the model effects and assessment algorithm constructed IC4.5 customer classification model to predict the effect is very excellent , can provide decision support for bank managers . In this paper, the characteristics of banking data better applied to forecast customer classification data mining techniques can help banks to better recognize customer value , the implementation of differentiated management based on customer classification , to optimize Bank resource allocation .

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CLC: > Economic > Fiscal, monetary > Finance, banking > Finance, banking theory > Banking > Modern management
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