IDEAS home Printed from https://ideas.repec.org/a/cup/astinb/v54y2024i2p263-279_3.html
   My bibliography  Save this article

Integration of traditional and telematics data for efficient insurance claims prediction

Author

Listed:
  • Peiris, Hashan
  • Jeong, Himchan
  • Kim, Jae-Kwang
  • Lee, Hangsuck

Abstract

While driver telematics has gained attention for risk classification in auto insurance, scarcity of observations with telematics features has been problematic, which could be owing to either privacy concerns or favorable selection compared to the data points with traditional features. To handle this issue, we apply a data integration technique based on calibration weights for usage-based insurance with multiple sources of data. It is shown that the proposed framework can efficiently integrate traditional data and telematics data and can also deal with possible favorable selection issues related to telematics data availability. Our findings are supported by a simulation study and empirical analysis in a synthetic telematics dataset.

Suggested Citation

  • Peiris, Hashan & Jeong, Himchan & Kim, Jae-Kwang & Lee, Hangsuck, 2024. "Integration of traditional and telematics data for efficient insurance claims prediction," ASTIN Bulletin, Cambridge University Press, vol. 54(2), pages 263-279, May.
    • Handle: RePEc:cup:astinb:v:54:y:2024:i:2:p:263-279_3
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0515036124000060/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:astinb:v:54:y:2024:i:2:p:263-279_3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/asb .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.