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Predictive modeling in long-term care insurance

Recurso electrónico / Electronic resource
Registro MARC
Tag12Valor
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001  MAP20160022155
003  MAP
005  20160817142057.0
008  160712e20160601usa|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20160008869‎$a‎Lally, Nathan R.
24510‎$a‎Predictive modeling in long-term care insurance‎$c‎Nathan R. Lally, Brian M. Hartman
520  ‎$a‎The 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.
650 4‎$0‎MAPA20080602437‎$a‎Matemática del seguro
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20100014189‎$a‎Long term care insurance
650 4‎$0‎MAPA20080555306‎$a‎Mortalidad
650 4‎$0‎MAPA20080592059‎$a‎Modelos predictivos
650 4‎$0‎MAPA20090041721‎$a‎Distribución Poisson-Beta
650 4‎$0‎MAPA20080591182‎$a‎Gerencia de riesgos
650 4‎$0‎MAPA20080592578‎$a‎Política de precios
650 4‎$0‎MAPA20130012056‎$a‎Gastos médicos
7001 ‎$0‎MAPA20130016856‎$a‎Hartman, Brian M.
7730 ‎$w‎MAP20077000239‎$t‎North American actuarial journal‎$d‎Schaumburg : Society of Actuaries, 1997-‎$x‎1092-0277‎$g‎01/06/2016 Tomo 20 Número 2 - 2016 , p. 160-183