Risk factor selection in automobile insurance policies: a way to improve the bottom line of insurance companies
The objective of this paper is to test the validity of using 'bonus-malus' (BM) levels to classify policyholders satisfactorily. In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. The empirical...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/34188 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/34188 |
| Access Level: | acceso abierto |
| Palabra clave: | Automobile insurance company Risk factors Bonus malus system Rough set theory Artificial intelligence Inteligencia artificial (Informática) Seguros 1203.04 Inteligencia Artificial 5304.05 Seguros |
| Sumario: | The objective of this paper is to test the validity of using 'bonus-malus' (BM) levels to classify policyholders satisfactorily. In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders' policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors. |
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