A Survey of personalized treatment models for pricing strategies in insurance

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      <subfield code="a">A Survey of personalized treatment models for pricing strategies in insurance</subfield>
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      <subfield code="a">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.</subfield>
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      <subfield code="g">01/09/2014 Volumen 58 Número 1 - septiembre 2014 , p. 68-76</subfield>
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