Abstract
This study investigates the effectiveness of support vector machines (SVM) approach in detecting the underlying data pattern for the credit card customer churn analysis. This article introduces a relatively new machine learning technique, SVM, to the customer churning problem in attempt to provide a model with better prediction accuracy. To compare the performance of the proposed model, we used a widely adopted and applied Artificial Intelligence (AI) method, back-propagation neural networks (BPN) as a benchmark. The results demonstrate that SVM outperforms BPN. We also examine the effect of the variability in performance with respect to various values of parameters in SVM.
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Kim, S., Shin, Ks., Park, K. (2005). An Application of Support Vector Machines for Customer Churn Analysis: Credit Card Case. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_91
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DOI: https://doi.org/10.1007/11539117_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
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