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Spatial Statistical Modeling of Insurance Risk An epidemiologist approach to car insurance

Tufvesson, Oscar; Lindström, Johan LU and Lindström, Erik LU (2017) SPAS2017 International Conference on Stochastic Processes and Algebraic Structures – From Theory Towards Applications
Abstract (Swedish)
Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the... (More)
Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the modelling of number ofclaims signicantly improves on the result obtained using ordinary generalisedlinear models. However, the support for adding a spatial componentto the model for claims cost is weaker. (Less)
Abstract
Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the... (More)
Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the modelling of number ofclaims signicantly improves on the result obtained using ordinary generalisedlinear models. However, the support for adding a spatial componentto the model for claims cost is weaker. (Less)
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SPAS2017 International Conference on Stochastic Processes and Algebraic Structures – From Theory Towards Applications
language
Swedish
LU publication?
yes
id
ce52eaf5-d3c8-4639-bd5d-9d138a0fe796
date added to LUP
2017-10-26 16:16:35
date last changed
2018-07-08 13:41:05
@misc{ce52eaf5-d3c8-4639-bd5d-9d138a0fe796,
  abstract     = {Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the modelling of number ofclaims signicantly improves on the result obtained using ordinary generalisedlinear models. However, the support for adding a spatial componentto the model for claims cost is weaker.},
  author       = {Tufvesson, Oscar and Lindström, Johan and Lindström, Erik},
  language     = {swe},
  month        = {10},
  title        = {Spatial Statistical Modeling of Insurance Risk An epidemiologist approach to car insurance},
  year         = {2017},
}