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A Case Study of Predicting Banking Customers Behaviour by Using Data Mining
conference contribution
posted on 2019-01-01, 00:00 authored by X Zhou, G Bargshady, Moloud Abdar, X Tao, R Gururajan, K C Chan© 2019 IEEE. Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques-Neural Network and Association Rules-are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model.
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Behavioral, Economic, and Socio-Cultural Computing. Conference (2019 : 6th : Beijing, China)Pagination
1 - 6Publisher
IEEELocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2019-10-28End date
2019-10-30ISBN-13
9781728147628Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
BESC 2019 : Proceedings of the 6th International Conference on Behavioral, Economic and Socio-Cultural ComputingUsage metrics
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