From GLM to GBM: The Future of AI in Lending and Insurance

Presented by

Patrick Hall, Advisory Consultant at and Michael Proksch, Senior Director, Customer Data Science at

About this talk

Insurance and credit lending are highly regulated industries that have relied heavily on mathematical modeling for decades. In order to provide explainable results for their models, data scientists and statisticians in both industries relied heavily on generalized linear models (GLMs). However, new machine learning algorithms like GBMs are not only more sophisticated estimators of risk, but due to a Nobel-laureate breakthrough known as Shapley values, they are now seemingly just as interpretable as traditional GLMs. More nuanced risk estimation means less payouts and write-offs for policy and credit issuers, but it also means a broader group of customers can participate in mainstream insurance and credit markets. In this webinar, you will learn about: - The Advantages of Machine Learning vs. Linear Models - Why you should think about the AI shift in perspective - How to move to new ML Methods (e.g. GBM) Presenters: Patrick Hall, Advisory Consultant at Michael Proksch, Senior Director, Customer Data Science at

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