Max Martinelli, Actuarial Data Scientist at Akur8, had the pleasure to host a webinar about a new research paper on "Derivative Lasso Credibility-based signal fitting for GLMs", co-written by Mattia Casotto, Thomas Holmes and Guillaume Beraud-Sudreau.

Abstract: Building transparent and accurate models for insurance risks is challenging due to the complex correlations and non linearities of the modeled effects. Generalized Linear Models (GLMs) are an essential tool to handle correlations and build transparent models. However they require a lot of iterative work to incorporate nonlinearities and develop robust and credible models.

This white paper suggests that these drawbacks can be effectively solved within the Penalized Regression framework, in a manner that does not change GLM’s input hypotheses’ and table based output. Furthermore, the approach is sound as it relates to intuitive statistical assumptions that integrate credibility and nonlinear effects in the modeling. We will also provide the insurance practitioner with guidance on how to build and analyze these models with examples from publicly available data.

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