Bonus-Malus Systems in Vehicle Insurance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F15%3A00082157" target="_blank" >RIV/00216224:14560/15:00082157 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/S2212-5671(15)00354-8" target="_blank" >http://dx.doi.org/10.1016/S2212-5671(15)00354-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/S2212-5671(15)00354-8" target="_blank" >10.1016/S2212-5671(15)00354-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bonus-Malus Systems in Vehicle Insurance

Popis výsledku v původním jazyce

Actuaries in insurance companies try to design a tariff structure that will fairly distribute the burden of claims among policyholders. Therefore they try to find the best model for an estimation of the insurance premium. The paper deals with an estimateof a priori annual claim frequency and application of bonus-malus system in the vehicle insurance. In this paper, analysis of the portfolio of vehicle insurance data using generalized linear model (GLM) is performed. Based on large real-world sample ofdata from 67 857 vehicles, the present study proposes a classification analysis approach addressing the selection of predictor variables. The models with different predictor variables are compared by the analysis of deviance. Based on this comparison, the model for the best estimate of annual claim frequency is chosen. Then the bonus-malus (BM) system is used for each class of drivers and Bayesian relative premium is calculated. Finally a fairer premium for different groups of drivers is

Název v anglickém jazyce

Bonus-Malus Systems in Vehicle Insurance

Popis výsledku anglicky

Actuaries in insurance companies try to design a tariff structure that will fairly distribute the burden of claims among policyholders. Therefore they try to find the best model for an estimation of the insurance premium. The paper deals with an estimateof a priori annual claim frequency and application of bonus-malus system in the vehicle insurance. In this paper, analysis of the portfolio of vehicle insurance data using generalized linear model (GLM) is performed. Based on large real-world sample ofdata from 67 857 vehicles, the present study proposes a classification analysis approach addressing the selection of predictor variables. The models with different predictor variables are compared by the analysis of deviance. Based on this comparison, the model for the best estimate of annual claim frequency is chosen. Then the bonus-malus (BM) system is used for each class of drivers and Bayesian relative premium is calculated. Finally a fairer premium for different groups of drivers is

Druh

D - Stať ve sborníku

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Procedia Finance and Economics

ISBN

—

ISSN

2212-5671

e-ISSN

—

Počet stran výsledku

7

Strana od-do

216-222

Název nakladatele

Elsevier B.V.

Místo vydání

Amsterdam

Místo konání akce

Prague - Czech Republic

Datum konání akce

1. 1. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000360103600032