期刊
INSURANCE MATHEMATICS & ECONOMICS
卷 58, 期 -, 页码 68-76出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.insmatheco.2014.06.009
关键词
Rate making; Cross-selling in insurance; Predictive models; Causal inference; Nonlife insurance
类别
资金
- Spanish Ministry of Science/FEDER [ECO2010-21787-C03-01]
We consider a model for price calculations based on three components: a fair premium; price loadings reflecting general expenses and solvency requirements; and profit. The first two components are typically evaluated on a yearly basis, while the third is viewed from a longer perspective. When considering the value of customers over a period of several years, and examining policy renewals and cross-selling in relation to price adjustments, many insurers may prefer to reduce their short-term benefits so as to focus on their most profitable customers and the long-term value. We show how models of personalized treatment learning can be used to select the policy holders that should be targeted in a company's marketing strategies. An empirical application of the causal conditional inference tree method illustrates how best to implement a personalized cross-sell marketing campaign in this framework. (C) 2014 Elsevier B.V. All rights reserved.
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