# Boosting insights in insurance tariff plans with tree-based machine learning methods

## TL;DR

This paper explores the use of decision tree-based machine learning methods, including boosted trees, to develop transparent and effective insurance tariff plans that outperform traditional GLMs in predictive accuracy and risk management.

## Contribution

It introduces adapted loss functions for insurance data and demonstrates how to tune, visualize, and evaluate tree-based models for insurance pricing, enhancing interpretability and profitability.

## Key findings

- Boosted trees outperform classical GLMs in predictive accuracy.
- Tree-based models provide better risk segmentation and portfolio profitability.
- Visualization tools help interpret complex machine learning models.

## Abstract

Pricing actuaries typically operate within the framework of generalized linear models (GLMs). With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of claims. We adapt the loss functions used in the algorithms such that the specific characteristics of insurance data are carefully incorporated: highly unbalanced count data with excess zeros and varying exposure on the frequency side combined with scarce, but potentially long-tailed data on the severity side. A key requirement is the need for transparent and interpretable pricing models which are easily explainable to all stakeholders. We therefore focus on machine learning with decision trees: starting from simple regression trees, we work towards more advanced ensembles such as random forests and boosted trees. We show how to choose the optimal tuning parameters for these models in an elaborate cross-validation scheme, we present visualization tools to obtain insights from the resulting models and the economic value of these new modeling approaches is evaluated. Boosted trees outperform the classical GLMs, allowing the insurer to form profitable portfolios and to guard against potential adverse risk selection.