Welcome to the InfoSci Platform
Reference Hub9
A Data Mining Model for Risk Assessment and Customer Segmentation in the Insurance Industry

A Data Mining Model for Risk Assessment and Customer Segmentation in the Insurance Industry

Payam Hanafizadeh, Neda Rastkhiz Paydar
Copyright: © 2013 |Volume: 4 |Issue: 1 |Pages: 27
ISSN: 1947-8569|EISSN: 1947-8577|EISBN13: 9781466631342|DOI: 10.4018/jsds.2013010104
Cite Article Cite Article

MLA

Hanafizadeh, Payam, and Neda Rastkhiz Paydar. "A Data Mining Model for Risk Assessment and Customer Segmentation in the Insurance Industry." IJSDS vol.4, no.1 2013: pp.52-78. http://doi.org/10.4018/jsds.2013010104

APA

Hanafizadeh, P. & Paydar, N. R. (2013). A Data Mining Model for Risk Assessment and Customer Segmentation in the Insurance Industry. International Journal of Strategic Decision Sciences (IJSDS), 4(1), 52-78. http://doi.org/10.4018/jsds.2013010104

Chicago

Hanafizadeh, Payam, and Neda Rastkhiz Paydar. "A Data Mining Model for Risk Assessment and Customer Segmentation in the Insurance Industry," International Journal of Strategic Decision Sciences (IJSDS) 4, no.1: 52-78. http://doi.org/10.4018/jsds.2013010104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Customer segmentation on the basis of predictable risks can help insurance firms maximize their earnings and minimize their losses. Car insurance is one of the most lucrative and profitable branches in the insurance industry. Utilizing the concept of self-organizing map, the authors propose a two-phase model called ‘Auto Insurance Customers Segmentation Intelligent Tool’ to segment customers in insurance companies on basis of risk. In the first phase, the authors extract 18 risk factors in four categories consisting of demographic specifications, auto specifications, policy specifications, and the driver’s record extracted from the literature review. In the second phase, they finalize the selection process by drawing on expert opinion polls. The authors utilize self-organizing maps since they are able to display the output in the form of illustrative and comprehensible graphical maps capable of representing linear and non-linear relationships among variables, insensitive to the learning input, and slightly sensitive to the noise in the learning input. Finally, K-means are employed to compare the results with those obtained through self-organizing maps.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.