Credibility and Penalized Regression

In recent years a number of extensions to Generalized Linear Models (GLMs) have been developed to address some limitations, such as their inability to incorporate Credibility-like assumptions. Among these adaptations, Penalized regression techniques, which blend GLMs with Credibility, are widely adopted in the Machine Learning community but are not very popular within the actuarial world. While Credibility methods and GLMs are part of the standard actuarial toolkit of predictive modeling, the actuarial literature describing how Penalized regression blends Credibility with GLMs is not equally developed. The aim of this whitepaper is to provide practitioners with key concepts and intuitions that demonstrate how Penalized regression blends GLM with Credibility-like assumptions. By walking through a simple example, we will explore how Penalized regression (and Lasso in particular) can be interpreted from the perspective of both Credibility and GLM frameworks. The whitepaper objective is to familiarize practitioners with Penalized regression as an extension of established actuarial techniques, instead of considering it one among several new modeling techniques from the Machine Learning and Data Science literature.
Titelblatt der Forschungsarbeit von Akur8 "Credibility and Penalized Regression
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‍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
DistributionYou 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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