Customer churning prediction using support vector machines in online auto insurance service
- Authors
- Hur, Y.; Lim, S.
- Issue Date
- May-2005
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS, v.3497, no.II, pp 928 - 933
- Pages
- 6
- Journal Title
- ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS
- Volume
- 3497
- Number
- II
- Start Page
- 928
- End Page
- 933
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53215
- DOI
- 10.1007/11427445_149
- ISSN
- 0302-9743
- Abstract
- Support vector machines (SVMs) are promising methods for the prediction of online auto insurance customer churning because SVMs use a risk minimization principal that consists of the empirical error and the regularized term predicting the switching probability of an insured to other auto insurance company. In addition, this study examines the feasibility of applying SVM in online insurance customer churning by comparing it with other methods such as artificial neural network (ANN) and logit model. This study proves that SVM provides a promising alternative to predict customer churning in auto-insurance service.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Business & Economics > School of Business Administration > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53215)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.