IDEAS home Printed from https://ideas.repec.org/a/taf/uaajxx/v20y2016i2p114-132.html
   My bibliography  Save this article

Credibility in Loss Reserving

Author

Listed:
  • Peng Shi
  • Brian M. Hartman

Abstract

This article proposes using credibility theory in the context of stochastic claims reserving. We consider the situation where an insurer has access to the claims experience of its peer competitors and has the potential to improve prediction of outstanding liabilities by incorporating information from other insurers. Based on the framework of Bayesian linear models, we show that the development factor in the classical chain-ladder setting has a credibility expression: a weighted average of the prior mean and the best estimate from the data. In the empirical analysis, we examine loss triangles for the line of commercial auto insurance from a portfolio of insurers in the United States. We employ hierarchical model for the specification of prior and show that prediction could be improved through borrowing strength among insurers based on a hold-out sample validation.

Suggested Citation

  • Peng Shi & Brian M. Hartman, 2016. "Credibility in Loss Reserving," North American Actuarial Journal, Taylor & Francis Journals, vol. 20(2), pages 114-132, April.
    • Handle: RePEc:taf:uaajxx:v:20:y:2016:i:2:p:114-132
    DOI: 10.1080/10920277.2015.1109456
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10920277.2015.1109456
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10920277.2015.1109456?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. László Martinek, 2019. "Analysis of Stochastic Reserving Models By Means of NAIC Claims Data," Risks, MDPI, vol. 7(2), pages 1-27, June.
    2. Karthik Sriram & Peng Shi, 2021. "Stochastic loss reserving: A new perspective from a Dirichlet model," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 195-230, March.
    3. Lally, Nathan & Hartman, Brian, 2018. "Estimating loss reserves using hierarchical Bayesian Gaussian process regression with input warping," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 124-140.

    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:taf:uaajxx:v:20:y:2016:i:2:p:114-132. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uaaj .

    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.