Estudo da sinistralidade no mercado securitário de veículos: uma abordagem multivariada

Detalhes bibliográficos
Ano de defesa: 2014
Autor(a) principal: Costa, Priscila Amorim da [UNESP]
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Estadual Paulista (Unesp)
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/11449/122190
Resumo: The insurer that has an effective control of the risks involved by the insureds can avoid issues such as bankruptcy and loss of profitability. The purpose of the research was to identify the varieties that define the risk assciated with future buyers, enabling classify them into one two groups, the claimed and the unclaimed, based in probabilities defined by a multivariate model. Therefore, were questions considered (variables) existing on the risk evaluation of the insurers questionnarie and others nominated by the expert brokers of the insurers. To achieve the objective, was used in the analysis, the multivariate statistical technique, known as Discriminant Analylsis, in order to segregate the individual into one of two groups. A discriminant function was constructed from the independent variables, associated to the risks, and the dependent variable, which covers the two groups. Other results as estimation of the classification rule, evaluation of the quality of the discrimination rule settings, estimation of the overall probability of correct answers and tests related to the assumptions of discrimination analysis were presented. The studied sample was consisted of 2,000 insured served by the broker, divided into two groups: the first composed of individuals without claims; and the second with those who has one or more claims. The model enabled to classify individuals by the two groups, wherein in the development sample and test the classification represented 69% accuracy. The separation was carried out by variables with higher importance degrees. Were elected: car power, time of insurance, bonus, parking lot use, and professional activities defined by the insured occupation. Such variables can be considered hihgly discriminating, based on the contribution coefficient for the discrimination. This work has presented results that contradict the common sense in the insurance market, in which technicians say that sex and age determine...