Spatial statistical modelling of insurance risk : a spatial epidemiological approach to car insurance
Tufvesson, Oskar ; Lindström, Johan LU
and Lindström, Erik
LU
(2019)
In Scandinavian Actuarial Journal
2019(6).
p.508-522
- Abstract
Spatial models, such as the Besag, York and Mollie (BYM) model, have long been used in epidemiology and disease mapping. A common research question in these subjects is modelling the number of disease events per region; here the BYM models provides a holistic framework for both covariates and dependencies between regions. We use these tools to assess the relative insurance risk associated with the policyholders geographical location. A Bayesian modelling approach is presented and an elastic net is used to reduce the large number of possible geographic covariates. The final inference is performed using Integrated Nested Laplace Approximation. The model is applied to car insurance data from If P&C Insurance together with spatially... (More)
Spatial models, such as the Besag, York and Mollie (BYM) model, have long been used in epidemiology and disease mapping. A common research question in these subjects is modelling the number of disease events per region; here the BYM models provides a holistic framework for both covariates and dependencies between regions. We use these tools to assess the relative insurance risk associated with the policyholders geographical location. A Bayesian modelling approach is presented and an elastic net is used to reduce the large number of possible geographic covariates. The final inference is performed using Integrated Nested Laplace Approximation. The model is applied to car insurance data from If P&C Insurance together with spatially referenced covariate data of high resolution, provided by Insightone. The entire analysis is performed using freely available R-packages. Including spatial dependence when modelling the number of claims significantly improves on the result obtained using ordinary generalised linear models. However, the support for adding a spatial component to the model for claims cost is weaker.
(Less)
- author
- Tufvesson, Oskar
; Lindström, Johan
LU
and Lindström, Erik
LU
- organization
- publishing date
- 2019-02-14
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bayesian hierarchical modelling, Besag–York–Mollie model, geographical pricing, integrated nested laplace approximation, spatial modelling
- in
- Scandinavian Actuarial Journal
- volume
- 2019
- issue
- 6
- pages
- 508 - 522
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85061910917
- ISSN
- 0346-1238
- DOI
- 10.1080/03461238.2019.1576146
- language
- English
- LU publication?
- yes
- id
- 9f58d080-39ee-4ec5-8053-0c7842916ab7
- date added to LUP
- 2019-03-04 10:50:24
- date last changed
- 2022-04-25 21:31:49
@article{9f58d080-39ee-4ec5-8053-0c7842916ab7,
abstract = {{<p>Spatial models, such as the Besag, York and Mollie (BYM) model, have long been used in epidemiology and disease mapping. A common research question in these subjects is modelling the number of disease events per region; here the BYM models provides a holistic framework for both covariates and dependencies between regions. We use these tools to assess the relative insurance risk associated with the policyholders geographical location. A Bayesian modelling approach is presented and an elastic net is used to reduce the large number of possible geographic covariates. The final inference is performed using Integrated Nested Laplace Approximation. The model is applied to car insurance data from If P&C Insurance together with spatially referenced covariate data of high resolution, provided by Insightone. The entire analysis is performed using freely available R-packages. Including spatial dependence when modelling the number of claims significantly improves on the result obtained using ordinary generalised linear models. However, the support for adding a spatial component to the model for claims cost is weaker.</p>}},
author = {{Tufvesson, Oskar and Lindström, Johan and Lindström, Erik}},
issn = {{0346-1238}},
keywords = {{Bayesian hierarchical modelling; Besag–York–Mollie model; geographical pricing; integrated nested laplace approximation; spatial modelling}},
language = {{eng}},
month = {{02}},
number = {{6}},
pages = {{508--522}},
publisher = {{Taylor & Francis}},
series = {{Scandinavian Actuarial Journal}},
title = {{Spatial statistical modelling of insurance risk : a spatial epidemiological approach to car insurance}},
url = {{http://dx.doi.org/10.1080/03461238.2019.1576146}},
doi = {{10.1080/03461238.2019.1576146}},
volume = {{2019}},
year = {{2019}},
}