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Geographic ratemaking with spatial embeddings

Recurso electrónico / Electronic resource
Registro MARC
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24500‎$a‎Geographic ratemaking with spatial embeddings‎$c‎Christopher Blier-Wong...[et.al]
520  ‎$a‎Spatial data are a rich source of information for actuarial applications: knowledge of a risk's location could improve an insurance company's ratemaking, reserving or risk management processes. Relying on historical geolocated loss data is problematic for areas where it is limited or unavailable. In this paper, we construct spatial embeddings within a complex convolutional neural network representation model using external census data and use them as inputs to a simple predictive model. Compared to spatial interpolation models, our approach leads to smaller predictive bias and reduced variance in most situations. This method also enables us to generate rates in territories with no historical experience.
650 4‎$0‎MAPA20080591182‎$a‎Gerencia de riesgos
650 4‎$0‎MAPA20080573935‎$a‎Seguros no vida
7730 ‎$w‎MAP20077000420‎$g‎03/01/2022 Volumen 52 Número 1 - enero 2022 , p. 1-31‎$x‎0515-0361‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association