Predictive modeling in long-term care insurance
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
<record>
<leader>00000cab a2200000 4500</leader>
<controlfield tag="001">MAP20160022155</controlfield>
<controlfield tag="003">MAP</controlfield>
<controlfield tag="005">20160817142057.0</controlfield>
<controlfield tag="008">160712e20160601usa|||p |0|||b|eng d</controlfield>
<datafield tag="040" ind1=" " ind2=" ">
<subfield code="a">MAP</subfield>
<subfield code="b">spa</subfield>
<subfield code="d">MAP</subfield>
</datafield>
<datafield tag="084" ind1=" " ind2=" ">
<subfield code="a">6</subfield>
</datafield>
<datafield tag="100" ind1=" " ind2=" ">
<subfield code="0">MAPA20160008869</subfield>
<subfield code="a">Lally, Nathan R.</subfield>
</datafield>
<datafield tag="245" ind1="1" ind2="0">
<subfield code="a">Predictive modeling in long-term care insurance</subfield>
<subfield code="c">Nathan R. Lally, Brian M. Hartman</subfield>
</datafield>
<datafield tag="520" ind1=" " ind2=" ">
<subfield code="a">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.</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080602437</subfield>
<subfield code="a">Matemática del seguro</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080579258</subfield>
<subfield code="a">Cálculo actuarial</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20100014189</subfield>
<subfield code="a">Long term care insurance</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080555306</subfield>
<subfield code="a">Mortalidad</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080592059</subfield>
<subfield code="a">Modelos predictivos</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20090041721</subfield>
<subfield code="a">Distribución Poisson-Beta</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080591182</subfield>
<subfield code="a">Gerencia de riesgos</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20080592578</subfield>
<subfield code="a">Política de precios</subfield>
</datafield>
<datafield tag="650" ind1=" " ind2="4">
<subfield code="0">MAPA20130012056</subfield>
<subfield code="a">Gastos médicos</subfield>
</datafield>
<datafield tag="700" ind1="1" ind2=" ">
<subfield code="0">MAPA20130016856</subfield>
<subfield code="a">Hartman, Brian M.</subfield>
</datafield>
<datafield tag="773" ind1="0" ind2=" ">
<subfield code="w">MAP20077000239</subfield>
<subfield code="t">North American actuarial journal</subfield>
<subfield code="d">Schaumburg : Society of Actuaries, 1997-</subfield>
<subfield code="x">1092-0277</subfield>
<subfield code="g">01/06/2016 Tomo 20 Número 2 - 2016 , p. 160-183</subfield>
</datafield>
</record>
</collection>