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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/8883
Title: Application of Data Mining Techniques for Effective Customer Relationship Management of Microfinances
Other Titles: The Case of Wisdom Microfinance
???metadata.dc.contributor.*???: Dr. Million Meshesha
Dibaba, Wakgari
Keywords: Wisdom Microfinance;Data Mining Techniques
Issue Date: Aug-2009
Publisher: AAU
Abstract: The proliferation of information and communication technologies enabled companies to deal with large quantities of data. Microfinances are one of such institutions that collect, process and store huge amounts of records from time to time and therefore deal with voluminous amount of data. On the other hand, Microfinances are facing problems in customer handling; the proportion of customers staying intact with the same microfinance as a customer is very less compared to potential customers. The WISDOM microfinance is facing such problem where most customers are churning/ shifting to other competitors after using the loan service once or few times only. The existing past and historic data could be actionable and usable for decision making process that improves customer relationship management with the help of data mining techniques. One of the various applications of data mining is in support of customer relationship management through pattern mining and uncovering regularities. This paper reports the study of application of data mining in microfinance that helps build a classification model which supports in prediction of a new borrowers status (highly privileged, moderately privileged or less privileged) during the loan decision making in the organization. A classification model is built based on the borrowers’ corpus data obtained from the WISDOM microfinance. Essential preprocessing activities have been applied to clean and make it ready for the Experimentation. Then experiments using J48 decision tree classifier of the WEKA 3.7.0 software have been conducted using the preprocessed dataset with different attributes and parameters setting in order to arrive at the optimal model. The classification model with the best accuracy level (78.502%) and relatively less number of leaves and tree size is constructed to predict the new customer class label (highly privileged, moderately privileged or less privileged).
URI: http://hdl.handle.net/123456789/8883
Appears in Collections:Thesis - Information Science

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