Tree-Based Machine Learning for Insurance Pricing

At the Conference for useRs of R 2018, the team at the University of Leuven in Belgium presented on 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 on 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 the frequency side and scarce, but potentially heavy-tailed and right-censored data on the severity side. One of the key requirements is the need for transparent, interpretable pricing models which are easily explainable to all stakeholders. We, therefore, shy away from black box models such as neural networks and rather focus on tree-based machine learning models. Starting from single recursive trees we work towards more advanced ensembles such as bagged trees, random forests, and boosted trees. We also present visualization tools to obtain insights from the models by assessing the importance of the different risk factors and their impact on the price of an insurance contract.

Main Sections

0:00 Introduction
3:20 MTPL data
4:27 Modeling approach
7:37 R Universe
10:17 Some results
14:38 Conclusion
15:42 Thank you

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