Special Issue "Insurance: Spatial and Network Data"

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 31 May 2019

Special Issue Editor

Guest Editor
Prof. Dr. Arthur Charpentier

UQAM, Universite du Quebec a Montreal
Website | E-Mail
Interests: actuarial science; predictive modeling; computational science; statistics and econometrics; risks; visualization; data science

Special Issue Information

Dear Colleagues,

The insurance industry is overwhelmed by data, even more so than in the past. With telematics, and more generally connected objects, insurers now have more information about the spatial components of risks. In motor insurance, how can we use spatial information to more fairly price insurance products, either based on locations (where the drive lives and where (s)he works) or on length of trajectories. Should those products still be on a yearly basis, or should they be based on the distance driven? In household insurance, how can we incorporate old information (about flood) or additional information (about burglaries in the neighborhood)?

In some cases, insurers also have information about connections (a more general word for “friends”) about some insured. Such information can be used to create peer-to-peer insurance products, based on natural homophilia ("birds of a feather flock together"—individuals associate and bond with similar others) of friends’ networks, which can be seen as another way of creating risks categories (classically based on shared covariates). Peer effects can also be important in prevention for instance. Another popular kind of networks are family trees. Does having information of relatives (ancestors, cousins, etc.) affect predictive probabilities, in heath or like insurance? Networks can also be used on a more macro level, to assess solvency of insurance companies, based on the small number of reinsurance companies.

Moving from these considerations, this Special Issue aims to compile high quality papers that offer a discussion of the state-of-the-art, or introduce new theoretical or practical developments in this field. We welcome papers related, but not limited to, the following topics:

  • Use of telematic data in motor insurance
  • Family history for life insurance
  • Peer to peer insurance
  • Peer effects and risk prevention
  • Insurance with friends and fraud issues

Prof. Dr. Arthur Charpentier
Guest Editor

Manuscript Submission Information

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Keywords

  • spatial risk factors
  • spatial heterogeneity
  • spatial smoothing
  • telematic data
  • peer effects
  • networks and contagion
  • pooling risks on networks
  • sampling on networks
  • covariates and homophily

Published Papers (1 paper)

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Research

Open AccessArticle Convolutional Neural Network Classification of Telematics Car Driving Data
Received: 29 October 2018 / Revised: 21 December 2018 / Accepted: 9 January 2019 / Published: 10 January 2019
PDF Full-text (1670 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this [...] Read more.
The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks. Full article
(This article belongs to the Special Issue Insurance: Spatial and Network Data)
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