Predictive modeling in long-term care insurance

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<title>Predictive modeling in long-term care insurance</title>
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<namePart>Hartman, Brian M.</namePart>
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<abstract displayLabel="Summary">The accurate prediction of long-term care insurance (LTCI) mortality, lapse, and claim rates is essential when making informed pricing and risk management decisions. Unfortunately, academic literature on the subject is sparse and industry practice is limited by software and time constraints. In this article, it is reviewed current LTCI industry modeling methodology, which is typically Poisson regression with covariate banding/modification and stepwise variable selection. It is tested the claim that covariate banding improves predictive accuracy, examine the potential downfalls of stepwise selection, and contend that the assumptions required for Poisson regression are not appropriate for LTCI data.</abstract>
<note type="statement of responsibility">Nathan R. Lally, Brian M. Hartman</note>
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<topic>Matemática del seguro</topic>
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<topic>Cálculo actuarial</topic>
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<topic>Long term care insurance</topic>
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<topic>Mortalidad</topic>
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<topic>Modelos predictivos</topic>
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<topic>Distribución Poisson-Beta</topic>
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<topic>Gerencia de riesgos</topic>
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<topic>Política de precios</topic>
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<topic>Gastos médicos</topic>
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<title>North American actuarial journal</title>
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<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
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<text>01/06/2016 Tomo 20 Número 2 - 2016 , p. 160-183</text>
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