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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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An incremental loss ratio method using prior information on calendar year effects.

Ulrich Riegel1

  • 1Munich Reinsurance Company, Königinstrasse 107, 80802 Munich, Germany.

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|May 25, 2022
PubMed
Summary

External factors like the Covid-19 pandemic can distort insurance loss triangles, impacting reserving methods. This study extends the additive claims reserving model to account for calendar year effects, improving accuracy for insurance loss reserving.

Keywords:
Additive claims reserving modelCalendar year effects modelingEM algorithmLoss development

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Area of Science:

  • Actuarial Science
  • Risk Management
  • Insurance Mathematics

Background:

  • External factors can significantly influence insurance loss development triangles, distorting traditional reserving methods such as the chain ladder and loss ratio methods.
  • The Covid-19 pandemic serves as a recent, impactful example of an external factor affecting insurance premiums and losses across multiple countries.
  • The insurance industry is actively gathering market knowledge on the pandemic's specific impacts on claims and pricing.

Purpose of the Study:

  • To extend the additive claims reserving model to incorporate calendar year effects.
  • To develop a modified incremental loss ratio method capable of utilizing market knowledge about external factors.
  • To provide a robust framework for insurance loss reserving in the presence of significant calendar year influences.

Main Methods:

  • Extension of the additive claims reserving model to explicitly account for calendar year effects.
  • Development of a variant of the incremental loss ratio method (additive method) incorporating external market knowledge.
  • Derivation of formulas for the mean squared error of prediction to quantify model uncertainty.

Main Results:

  • The proposed model extension effectively incorporates calendar year effects into insurance loss reserving.
  • The modified incremental loss ratio method demonstrates improved performance when external factors are present.
  • Formulas for mean squared error of prediction are derived, enabling assessment of prediction accuracy.

Conclusions:

  • The developed methods provide a more accurate approach to insurance loss reserving when external factors cause significant calendar year effects.
  • Incorporating market knowledge about events like the Covid-19 pandemic enhances the reliability of reserving estimates.
  • The study offers practical tools for actuaries to navigate complex reserving challenges posed by external shocks.