A Case Study of Predicting Banking Customers Behaviour by Using Data Mining
Paper
Paper/Presentation Title | A Case Study of Predicting Banking Customers Behaviour by Using Data Mining |
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Presentation Type | Paper |
Authors | Zhou, Xujuan (Author), Bargshady, Ghazal (Author), Abdar, Moloud (Author), Tao, Xiaohui (Author), Gururajan, Raj (Author) and Chan, K. C. (Author) |
Journal or Proceedings Title | Proceedings of the 6th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2019) |
Article Number | 8963436 |
Number of Pages | 6 |
Year | 2019 |
Place of Publication | United States |
ISBN | 9781728147628 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/BESC48373.2019.8963436 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8963436 |
Conference/Event | 6th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2019) |
Event Details | 6th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2019) Parent International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Event Date 28 to end of 30 Oct 2019 Event Location Beijing, China |
Abstract | 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. |
Keywords | customer relationship management, customer knowledge management, data mining, neural networks, association rules |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | School of Management and Enterprise |
University of Montreal, Canada | |
Faculty of Health, Engineering and Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q56y8/a-case-study-of-predicting-banking-customers-behaviour-by-using-data-mining
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