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

By Giorgio Alfredo Spedicato, Christophe Dutang, Leonardo Petrini

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Abstract

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.

Keywords: Pricing optimization, conversion, machine learning, customer behavior, boosted trees

Citation

Spedicato, Giorgio Alfredo, Christophe Dutang, and Leonardo Petrini, "Machine Learning Methods to Perform Pricing Optimization: A Comparison with Standard Generalized Linear Models," Variance 12:1, 2018, pp. 69-89.

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Mission Statement

Variance (ISSN 1940-6452) is a peer-reviewed journal published by the Casualty Actuarial Society to disseminate work of interest to casualty actuaries worldwide. The focus of Variance is original practical and theoretical research in casualty actuarial science. Significant survey or similar articles are also considered for publication. Membership in the Casualty Actuarial Society is not a prerequisite for submitting papers to the journal and submissions by non-CAS members is encouraged.