2000
@article{SWB2000,
vgclass = {refpap},
author = {K. A. Smith and R. J. Willis and M. Brooks},
title = {An analysis of customer retention and insurance claim
patterns using data mining: a case study},
journal = {Journal of the Operational Research Society},
volume = {51},
number = {5},
pages = {532--541},
month = {May},
year = {2000},
url = {http://www.palgrave-journals.com/cgi-taf/DynaPage.taf?file=/jors/journal/v51/n5/abs/2600941a.html},
abstract = {The insurance industry is concerned with many problems of
interest to the operational research community. This paper presents a
case study involving two such problems and solves them using a variety
of techniques within the methodology of data mining. The first of these
problems is the understanding of customer retention patterns by
classifying policy holders as likely to renew or terminate their
policies. The second is better understanding claim patterns, and
identifying types of policy holders who are more at risk. Each of these
problems impacts on the decisions relating to premium pricing, which
directly affects profitability. A data mining methodology is used which
views the knowledge discovery process within an holistic framework
utilising hypothesis testing, statistics, clustering, decision trees,
and neural networks at various stages. The impacts of the case study on
the insurance company are discussed.},
}