Machine Learning Methods to Perform Pricing Optimization: A Comparison with Standard Generalized Linear Models

As the level of competition increases, pricing optimization is gaining a central role in most mature insurance markets, forcing insurers to optimize their rating and consider customer behavior; the modeling scene for the latter is one currently dominated by frameworks based on generalized linear models (GLMs). In this paper, we explore the applicability of novel machine learning techniques, such as tree-boosted models, to optimize the proposed premium on prospective policyholders. Given their predictive gain over GLMs, we carefully analyze both the advantages and disadvantages induced by their use.