Tree Based Machine Learning for Insurance Pricing

The goal of this paper is to apply machine learning techniques to insurance pricing, thereby leaving the actuarial comfort zone of generalized linear models (GLMs) and generalized additive models (GAMs). We focus on developing full tariff plans, built from both the frequency and severity of claims. We adapt the cost functions and performance measures used in the algorithms such that the specific characteristics of insurance data are carefully incorporated: highly unbalanced count data with excess zeros on t