39051

María Jesús Segovia Vargas
María del Mar Camacho Miñano
David Pascual Ezama

Revista Brasileira de Gestão de Negócios

Objetivo – o objetivo deste trabalho é testar a validade do uso de níveis “bonus-malus” (BM) para classificar satisfatoriamente os segurados. Método – A fim de alcançar o objetivo proposto e mostrar a evidência empírica, um método de inteligência artificial, a teoria de Rough Set, foi aplicado. Resultados – A evidência empírica mostra que os fatores de risco comuns empregados pela companhia de seguros são boas variáveis explicativas para classificar políticas dos segurados. Além disso, a variável do nível de BM aumenta ligeiramente o poder explicativo dos fatores de risco a priori. Implicações práticas – Para aumentar a capacidade de previsão do nível de BM, questionários psicológicos poderiam ser usados para medir as características ocultas dos segurados. Contribuições – A principal contribuição é que a metodologia utilizada para realizar a pesquisa, teoria de Rough Set, não foi ainda aplicada a esse problema.

companhia de seguros automobilísticos, fatores de risco, inteligência artificial, sistema de “bonus- malus”, teoria de Rough Set

VARGAS, M. J. S.; MIÑANO, M. D. M. C.; EZAMA, D. P. Seleção dos fatores de risco nas políticas de seguro de automóveis: uma maneira de aprimorar os lucros das companhias de seguro . Revista Brasileira de Gestão de Negócios, v. 17, n. 57, p. 1228-1245, 2015.

Vargas, M. J. S., Miñano, M. D. M. C., & Ezama, D. P. (2015). Seleção dos fatores de risco nas políticas de seguro de automóveis: uma maneira de aprimorar os lucros das companhias de seguro . Revista Brasileira de Gestão de Negócios, 17(57), 1228-1245.

10.7819/rbgn.v17i57.1741

https://www.spell.org.br/documentos/ver/39051/selecao-dos-fatores-de-risco-nas--politicas-de-seguro-de-automoveis---uma-maneira-de-aprimorar-os-lucros-das-companhias-de-seguro-/i/pt-br

Artigo

Português

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