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GEOGRAPHIC RATEMAKING WITH SPATIAL EMBEDDINGS

Published online by Cambridge University Press:  04 October 2021

Christopher Blier-Wong Open the ORCID record for Christopher Blier-Wong [Opens in a new window] ,
Hélène Cossette ,
Luc Lamontagne  and
Etienne Marceau
Christopher Blier-Wong
Affiliation:
École d’actuariat, Université Laval, Quebec, Canada Centre de recherche en données massives, Université Laval, Quebec, Canada E-mail: christopher.blier-wong.1@ulaval.ca
Hélène Cossette
Affiliation:
École d’actuariat, Université Laval, Quebec, Canada Centre de recherche en données massives, Université Laval, Quebec, Canada Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, Canada E-mail: helene.cossette@act.ulaval.ca
Luc Lamontagne
Affiliation:
Département d’informatique et de génie logiciel, Université Laval, Quebec, Canada Centre de recherche en données massives, Université Laval, Quebec, Canada E-mail: luc.lamontagne@ift.ulaval.ca
Etienne Marceau*
Affiliation:
École d’actuariat, Université Laval, Quebec, Canada Centre de recherche en données massives, Université Laval, Quebec, Canada Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, Canada E-mail: etienne.marceau@act.ulaval.ca
*
E-mail: etienne.marceau@act.ulaval.ca
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Abstract

Spatial 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.

Keywords

Embeddingsterritorial pricingrepresentation learningneural networksnonlife insuranceG22C44C45C51C13
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 52 , Issue 1 , January 2022 , pp. 1 - 31
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The International Actuarial Association

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