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Prediction of individual automobile RBNS claim reserves in the context of Solvency II

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Author Info

  • Mercedes Ayuso

    ()
    (Faculty of Economics, University of Barcelona)

  • Miguel Santolino

    ()
    (Faculty of Economics, University of Barcelona)

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    Abstract

    Automobile bodily injury (BI) claims remain unsettled for a long time after the accident. The estimation of an accurate reserve for Reported But Not Settled (RBNS) claims is therefore vital for insurers. In accordance with the recommendation included in the Solvency II project (CEIOPS, 2007) a statistical model is here implemented for RBNS reserve estimation. Lognormality on empirical compensation cost data is observed for different levels of BI severity. The individual claim provision is estimated by allocating the expected mean compensation for the predicted severity of the victim’s injury, for which the upper bound is also computed. The BI severity is predicted by means of a heteroscedastic multiple choice model, because empirical evidence has found that the variability in the latent severity of injured individuals travelling by car is not constant. It is shown that this methodology can improve the accuracy of RBNS reserve estimation at all stages, as compared to the subjective assessment that has traditionally been made by practitioners.

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    File URL: http://www.ub.edu/irea/working_papers/2008/200806.pdf
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    Bibliographic Info

    Paper provided by University of Barcelona, Research Institute of Applied Economics in its series IREA Working Papers with number 200806.

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    Length: 23 pages
    Date of creation: May 2008
    Date of revision: May 2008
    Handle: RePEc:ira:wpaper:200806

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    Postal: Tinent Coronel Valenzuela, Num 1-11 08034 Barcelona
    Web page: http://www.ub.edu/irea/
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    Related research

    Keywords: Automobile accident; Solvency II; bodily injury claims; individual RBNS reserve..;

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    1. Ayuso, Mercedes & Santolino, Miguel, 2007. "Predicting automobile claims bodily injury severity with sequential ordered logit models," Insurance: Mathematics and Economics, Elsevier, vol. 41(1), pages 71-83, July.
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