Credibility and Penalized Regression

Disponible en anglais 🇬🇧

Ces dernières années, un certain nombre d'extensions des modèles linéaires généralisés (GLM) ont été développées pour remédier à certaines contraintes, telles que leur incapacité à intégrer des hypothèses de Crédibilité. Parmi ces adaptations, les techniques de régression pénalisée, qui combinent les GLM avec la Crédibilité, sont largement adoptées dans la communauté du Machine Learning mais ne sont pas très populaires dans le monde actuariel. Alors que les méthodes de crédibilité et les GLM font partie de la boîte à outils actuarielle standard de modélisation prédictive, la littérature actuarielle décrivant comment la régression pénalisée combine la crédibilité avec les GLM n'est pas aussi développée. L'objectif de cet article de recherche est de fournir aux praticiens les concepts clés et les intuitions qui démontrent comment la régression pénalisée combine les GLM avec des hypothèses de Crédibilité. A travers un exemple simple, nous explorerons comment la régression pénalisée (et Lasso en particulier) peut être interprétée du point de vue de la Crédibilité et des GLM. L'objectif est donc de familiariser les praticiens avec la régression pénalisée en tant qu'extension des techniques actuarielles établies, au lieu de la considérer comme une nouvelle technique de modélisation parmi d'autres issues de la littérature sur l'apprentissage automatique et la science des données.
Couverture du research paper d'Akur8 intitulé "Credibility and Penalized Regression
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Any judgement on the elements contained in the Paper should be made only after studying the document in its entirety. Any conclusions drawn from one or more sections taken in isolation may be incorrect.The Paper is intended to be used by persons with certain levels of expertise in the areas covered and for the purposes stated. Any reader of the Paper should have a certain level of expertise in the areas related to this analysis, in order to appreciate the significance of the assumptions made and the impact of those assumptions on the results obtained.We recommend that any reader be assisted in reviewing the Paper by a qualified professional to ensure that our findings and recommendations have been understood.The Paper has been commissioned by and prepared for the exclusive use of Akur8, in accordance with the terms of our engagement letter. We make no commitment to third parties and shall not be liable for any use by a third party of an opinion or more generally for the use by such third party of the results included in the Paper. Milliman does not intend to benefit or create any legal obligation to any third-party recipient of its work. 
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TERMS OF USE OF THE PAPER “ANALYSIS OF AKUR8 PRICING SOLUTION AND METHODOLOGY” (“Terms of Use”)

In the context of Akur8’s and Milliman’s strategic alliance to provide AI-driven pricing solutions to insurers, Akur8 has engaged Milliman to analyse Akur8’s pricing solution and methodology (“Akur8 Tool”). Milliman’s Paper “Analysis of Akur8 pricing solution and methodology” (the “Paper”) provides a general opinion on the relevance of the modelling choices made by the Akur8 Tool in the context of non-life insurance pricing.Milliman is hereafter referred to as “we” and the readers of the Paper as “you”.

RESTRICTIONS
Distribution
You are not allowed to distribute or disclose all or part of the Paper to any third party whatsoever, without Milliman’s prior written consent, which may be given at their sole discretion.No Use of Milliman nameYou agree not to use and/or refer to Milliman’s name and/or trademarks directly or indirectly in any communication with any third party without Milliman’s prior written consent, which consent shall be required for each use or disclosure and shall be granted only at Milliman’s sole discretion.

RESERVATION
Absence of Commitment
Any judgement on the elements contained in the Paper should be made only after studying the document in its entirety. Any conclusions drawn from one or more sections taken in isolation may be incorrect.The Paper is intended to be used by persons with certain levels of expertise in the areas covered and for the purposes stated. Any reader of the Paper should have a certain level of expertise in the areas related to this analysis, in order to appreciate the significance of the assumptions made and the impact of those assumptions on the results obtained.We recommend that any reader be assisted in reviewing the Paper by a qualified professional to ensure that our findings and recommendations have been understood.The Paper has been commissioned by and prepared for the exclusive use of Akur8, in accordance with the terms of our engagement letter. We make no commitment to third parties and shall not be liable for any use by a third party of an opinion or more generally for the use by such third party of the results included in the Paper. Milliman does not intend to benefit or create any legal obligation to any third-party recipient of its work. 
Intellectual Property
Any right not expressly granted to you in these Terms of Use remains the right of Milliman and Akur8. Using the Paper under the conditions set forth in the section “Restrictions” does not give you ownership of any intellectual property rights in the content you access (including any branding used in the Paper)
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