Como usar bases de datos público-privadas en la gestión de riesgos aseguradores: geografía, clima y personas en el seguro de automóviles
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https://doi.org/10.26360/2024_08Palabras clave:
análisis de datos, modelos relacionales, sostenibilidadResumen
Este trabajo se centra en la utilización de fuentes de información pública en la aplicación de modelos relacionales en las entidades aseguradoras, para el mejor conocimiento de las características de los riesgos y asistir a la toma de decisiones en nuevos entornos de sostenibilidad. Primero, proponemos utilizar la metodología de grado de urbanización de Eurostat para agrupar siniestros o pólizas de automóviles en categorías potencialmente más homogéneas en el sector asegurador (urbano / suburbano / rural) para su segmentación y análisis. Segundo, analizamos como las compañías aseguradoras pueden utilizar información climatológica local conjuntamente con el grado de urbanización para modelizar el número de siniestros de automóviles en una zona geográfica específica. Finalmente, aplicamos modelos relacionales a bases de datos con información anonimizada de pasajeros en accidentes de tráfico proporcionadas por la Dirección General de Tráfico de España con el objetivo de definir mejor las características de los siniestros en función del perfil de las personas que se encuentran dentro del vehículo. Se trata de conocer, por ejemplo, el perfil de los pasajeros de vehículos conducidos por personas mayores, también en relación con el sexo y la zona geográfica. Las compañías aseguradoras conocen la enorme potencialidad del análisis de datos y deben apostar por la búsqueda de relaciones usando información que puede estar dispersa en múltiples bases de datos, incluyendo aquella que es de uso público y que puede facilitar la homogeneización y comparación de resultados, junto al diseño de políticas preventivas y de gestión de riesgos. Incluimos los códigos en R poniéndolos a disposición del sector asegurador y de la academia para su uso.
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Derechos de autor 2024 Luis Enrique Cespedes Coimbra, Mercedes Ayuso Gutierrez, Miguel Ángel Santolino Prieto
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
