Classification of climate-related insurance claims using gradient boosting

George Tzougas; Viet Dang; Asif John; Stathis Kroustalis; Debashish Dey; Konstantin Kutzkov

doi:10.26360/2022_6

Autores/as

George Tzougas Heriot-Watt University, Edinburgh (United Kingdom) https://orcid.org/0000-0003-4072-1454
Viet Dang Ar Genesis (United Kingdom)
Asif John Ar Genesis (United Kingdom)
Stathis Kroustalis University of Economics and Business, Athens (Greece)
Debashish Dey WTW (United Kingdom)
Konstantin Kutzkov Teva Pharmaceuticals (United Kingdom)

DOI:

https://doi.org/10.26360/2022_6

Palabras clave:

siniestros de seguros relacionados con las tormentas, aprendizaje conjunto, árboles de decisión, boosting

Resumen

El objetivo de este trabajo es aplicar uno de los enfoques de aprendizaje supervisado más representativos, el método de conjunto basado en árboles de decisión denominado gradient boosting, para clasificar el número de siniestros causados por tormentas en Grecia utilizando datos de una importante compañía de seguros que opera en este país. Por último, se utiliza un algoritmo de aprendizaje automático para clasificar el número de siniestros que se han producido como consecuencia de un "evento de tormenta" en 3 categorías: "ningún siniestro", "1 siniestro", "2 o más siniestros".

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Citas

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