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

Research output: Contribution to conferencePaper, not in proceedingpeer-review

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.
Original languageSwedish
Publication statusPublished - 2017 Oct 4
EventSPAS2017 International Conference on Stochastic Processes and Algebraic Structures – From Theory Towards Applications - Västerås and Stockholm, Sweden
Duration: 2017 Oct 42017 Oct 6
https://spas2017blog.wordpress.com/

Conference

ConferenceSPAS2017 International Conference on Stochastic Processes and Algebraic Structures – From Theory Towards Applications
Abbreviated titleSPAS 2017
Country/TerritorySweden
Period2017/10/042017/10/06
Internet address

Subject classification (UKÄ)

  • Probability Theory and Statistics

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