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An Application of Support Vector Machines for Customer Churn Analysis: Credit Card Case

Title
An Application of Support Vector Machines for Customer Churn Analysis: Credit Card Case
Authors
김선
Issue Date
2004
Department/Major
대학원 경영학과
Publisher
이화여자대학교 대학원
Degree
Master
Advisors
SHIN, Kyung-Shik
Abstract
본 연구에서는 대표적인 인공지능 기법인 인공신경망 수준의 높은 예측력을 나타내면서 동시에 우수한 설명력을 제공하는 것으로 알려진 Support Vector Machine (SVM)을 신용카드 시장의 고객이탈 분석에 적용하였다. 많은 경영분야 적용 연구에서 뛰어난 성능이 검증되었던 Back-propagation Neural Network (BPN)와 그 성과를 비교하였다. 실험 결과, SVM을 이용할 경우 총 적중률에 있어서 BPN보다 조금 더 높은 예측정확성을 나타내었다. 그리고 SVM 매개변수의 조절에 따른 예측력을 살펴보았다.;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 problem in attempt to provide a model with better prediction accuracy. We used a widely adopted and applied Artificial Intelligence (AI) method, back-propagation neural networks (BPN) as a benchmark. The results demonstrate that SVM has the slightly higher level of prediction accuracies than BPN. We also examine the effect of the variability in performance with respect to various values of parameters in SVM. Keywords: Support vector machines, Credit card customer churn analysis, Back-propagation neural networks
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일반대학원 > 경영학과 > Theses_Master
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