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A survey of personalized treatment models for pricing strategies in insurance

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  • Guelman, Leo
  • Guillén, Montserrat
  • Pérez-Marín, Ana M.

Abstract

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.

Suggested Citation

  • Guelman, Leo & Guillén, Montserrat & Pérez-Marín, Ana M., 2014. "A survey of personalized treatment models for pricing strategies in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 68-76.
    • Handle: RePEc:eee:insuma:v:58:y:2014:i:c:p:68-76
    DOI: 10.1016/j.insmatheco.2014.06.009
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    References listed on IDEAS

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    Cited by:

    1. Bolancé, Catalina & Vernic, Raluca, 2019. "Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 89-103.
    2. Catalina Bolancé & Raluca Vernic, 2017. "“Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution”," IREA Working Papers 201718, University of Barcelona, Research Institute of Applied Economics, revised Oct 2017.
    3. Himchan Jeong & Guojun Gan & Emiliano A. Valdez, 2018. "Association Rules for Understanding Policyholder Lapses," Risks, MDPI, vol. 6(3), pages 1-18, July.
    4. Wenhui Zhang & Yongmin Su & Ruimin Ke & Xinqiang Chen, 2018. "Evaluating the influential priority of the factors on insurance loss of public transit," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-11, January.
    5. Chen, Kun & Huang, Rui & Chan, Ngai Hang & Yau, Chun Yip, 2019. "Subgroup analysis of zero-inflated Poisson regression model with applications to insurance data," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 8-18.

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