Penalized Regression and Lasso Credibility
Penalized regression is a powerful tool to enhance traditional GLM modeling. This session will explore the nature of this enhancement both in theory and in practice. We will briefly review GLMs before discussing the benefits of penalized regression and its deep connections to credibility. Then, we will take this one step further and describe the application of penalized regression directly as a credibility procedure: lasso credibility. By following the evolution of modeling from GLMs to lasso credibility, attendees will be able to identify practical applications of these techniques in their own work.
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Learn more about the speakers
Tom Holmes is Akur8’s Chief Actuary for the US region and is a co-author of the upcoming publication Penalized Regression and Lasso Credibility. He has experience modeling personal and commercial insurance, and volunteers with the CAS on predictive modeling topics. He is a frequent presenter at CAS events and Akur8 webinars, and performs industry outreach to share actuarial modeling methodologies and best practices. Tom is a Fellow of the CAS and holds music degrees from the University of Michigan and Ohio University.
