Special Issue "Insurance: Spatial and Network Data"

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

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editor

Prof. Dr. Arthur Charpentier
Website
Guest Editor
Département de mathématiques, Université du Québec à Montréal, Montreal, QC H2X 3Y7, Canada
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (2 papers)

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Research

Open AccessArticle
Quantile Regression with Telematics Information to Assess the Risk of Driving above the Posted Speed Limit
Risks 2019, 7(3), 80; https://doi.org/10.3390/risks7030080 - 15 Jul 2019
Cited by 1
Abstract
We analyzed real telematics information for a sample of drivers with usage-based insurance policies. We examined the statistical distribution of distance driven above the posted speed limit—which presents a strong positive asymmetry—using quantile regression models. We found that, at different percentile levels, the [...] Read more.
We analyzed real telematics information for a sample of drivers with usage-based insurance policies. We examined the statistical distribution of distance driven above the posted speed limit—which presents a strong positive asymmetry—using quantile regression models. We found that, at different percentile levels, the distance driven at speeds above the posted limit depends on total distance driven and, more generally, on factors such as the percentage of urban and nighttime driving and on the driver’s gender. However, the impact of these covariates differs according to the percentile level. We stress the importance of understanding telematics information, which should not be limited to simply characterizing average drivers, but can be useful for signaling dangerous driving by predicting quantiles associated with specific driver characteristics. We conclude that the risk of driving for long distances above the speed limit is heterogeneous and, moreover, we show that prevention campaigns should target primarily male non-urban drivers, especially if they present a high percentage of nighttime driving. Full article
(This article belongs to the Special Issue Insurance: Spatial and Network Data)
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Open AccessArticle
Convolutional Neural Network Classification of Telematics Car Driving Data
Risks 2019, 7(1), 6; https://doi.org/10.3390/risks7010006 - 10 Jan 2019
Cited by 5
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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