# Frequency-Severity Experience Rating based on Latent Markovian Risk Profiles

arXiv: https://arxiv.org/abs/2109.01413 · HTML: https://mlsanity.com/paper/2109.01413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 87 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Boosting insights in insurance tariff plans with tree-based machine learning methods](https://mlsanity.com/paper/1904.10890/) (2019)
- [Regression for Copula-linked Compound Distributions with Applications in Modeling Aggregate Insurance Claims](https://mlsanity.com/paper/1910.05676/) (2019)
- [A copula transformation in multivariate mixed discrete-continuous models](https://mlsanity.com/paper/2008.12411/) (2020)

## Abstract (arXiv)

Bonus-Malus Systems traditionally consider a customer's number of claims irrespective of their sizes, even though these components are dependent in practice. We propose a novel joint experience rating approach based on latent Markovian risk profiles to allow for a positive or negative individual frequency-severity dependence. The latent profiles evolve over time in a Hidden Markov Model to capture updates in a customer's claims experience, making claim counts and sizes conditionally independent. We show that the resulting risk premia lead to a dynamic, claims experience-weighted mixture of standard credibility premia. The proposed approach is applied to a Dutch automobile insurance portfolio and identifies customer risk profiles with distinctive claiming behavior. These profiles, in turn, enable us to better distinguish between customer risks.