Machine Learning Methods to Perform Pricing Optimization. A Comparison with Standard GLMs

Giorgio Alfredo Spedicato , Christophe Dutang (1) , Leonardo Petrini

1 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision

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

Mots clés: Pricing Optimization; Conversion; Machine Learning; Customer Behaviour; Boosted Trees

Domaines: Statistiques [math.ST]; Applications [stat.AP]

Giorgio Alfredo Spedicato, Christophe Dutang, Leonardo Petrini. Machine Learning Methods to Perform Pricing Optimization. A Comparison with Standard GLMs. Variance, 2018, 12 (1), pp.69-89. ⟨hal-01942038v2⟩