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

arXiv: https://arxiv.org/abs/1904.10890 · HTML: https://mlsanity.com/paper/1904.10890/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 17 of 17)

- [Machine Learning with High-Cardinality Categorical Features in Actuarial Applications](https://mlsanity.com/paper/2301.12710/) (2023)
- [Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff](https://mlsanity.com/paper/2310.12671/) (2023)
- [Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBM](https://mlsanity.com/paper/2307.07771/) (2023)
- [Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots](https://mlsanity.com/paper/2404.18702/) (2024)
- [Customer Price Sensitivities in Competitive Automobile Insurance Markets](https://mlsanity.com/paper/2101.08551/) (2021)
- [Bayesian CART models for insurance claims frequency](https://mlsanity.com/paper/2303.01923/) (2023)
- [Conformal prediction for frequency-severity modeling](https://mlsanity.com/paper/2307.13124/) (2023)
- [Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning](https://mlsanity.com/paper/2111.07508/) (2021)
- [Bayesian CART models for aggregate claim modeling](https://mlsanity.com/paper/2409.01908/) (2024)
- [From Point to probabilistic gradient boosting for claim frequency and severity prediction](https://mlsanity.com/paper/2412.14916/) (2024)
- [Frequency-Severity Experience Rating based on Latent Markovian Risk Profiles](https://mlsanity.com/paper/2109.01413/) (2021)
- [Classification problem in liability insurance using machine learning models: a comparative study](https://mlsanity.com/paper/2411.00354/) (2024)
- [Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance](https://mlsanity.com/paper/2503.21321/) (2025)
- [A multi-view contrastive learning framework for spatial embeddings in risk modelling](https://mlsanity.com/paper/2511.17954/) (2025)
- [Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach](https://mlsanity.com/paper/2512.24747/) (2025)
- [Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors](https://mlsanity.com/paper/2604.21893/) (2026)
- [Is TabPFN the Silver Bullet for Insurance Pricing?](https://mlsanity.com/paper/2605.22892/) (2026)

## Abstract (arXiv)

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.