Modelling the occurrence and reporting dynamics of weather-related home insurance claims

Abstract

We study the occurrence and reporting dynamics of weather-related claims in private home insurance, combining detailed portfolio and claim information with publicly available high-resolution weather data. Existing approaches typically start from an identified severe weather event, such as a hailstorm, and model the dynamics of the claims caused by that event. In practice, however, claims arise under moderate as well as severe conditions, and the claims handling process runs continuously. An insurer therefore requires insight into the occurrence and reporting of claims on every calendar day. Modelling all calendar days jointly poses two challenges: recently occurred claims are only partially reported, and days with severe weather are rare while accounting for a large share of the claims. We propose a multi-regime EM-XGBoost framework that addresses both: an expectation-maximisation setup accounts for the incurred-but-not-reported (IBNR) claims, while a data-driven regime structure lets the occurrence and reporting dynamics differ across weather conditions. In a case study on a Dutch home insurance portfolio, we compare single- and two-regime specifications for two spatial resolutions: the country as a whole, and its individual regions. We find that the regional two-regime model outperforms the regional single-regime model as well as both national models, indicating that the benefit of a multi-regime setting depends on a sufficiently granular spatial resolution. We also show that daily covariates constructed from hourly weather observations, capturing the duration and the intra-day timing of severe weather, improve both the occurrence and the reporting model.

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BibTeX RIS

BibTeX

@misc{wilsens2026modelling,
  title = {{Modelling the occurrence and reporting dynamics of weather-related home insurance claims}},
  author = {Paul Wilsens and Katrien Antonio and Gerda Claeskens and Koos Gubbels},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.14542},
  eprint = {2611.14542},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.14542},
  note = {CC BY-SA 4.0},
}

RIS

TY  - UNPB
TI  - Modelling the occurrence and reporting dynamics of weather-related home insurance claims
AU  - Paul Wilsens
AU  - Katrien Antonio
AU  - Gerda Claeskens
AU  - Koos Gubbels
PY  - 2026
DA  - 2026/11/01
PB  - arXiv
DO  - 10.48550/arxiv.2611.14542
UR  - https://arxiv.org/abs/2611.14542
AB  - We study the occurrence and reporting dynamics of weather-related claims in private home insurance, combining detailed portfolio and claim information with publicly available high-resolution weather data. Existing approaches typically start from an identified severe weather event, such as a hailstorm, and model the dynamics of the claims caused by that event. In practice, however, claims arise under moderate as well as severe conditions, and the claims handling process runs continuously. An insurer therefore requires insight into the occurrence and reporting of claims on every calendar day. Modelling all calendar days jointly poses two challenges: recently occurred claims are only partially reported, and days with severe weather are rare while accounting for a large share of the claims. We propose a multi-regime EM-XGBoost framework that addresses both: an expectation-maximisation setup accounts for the incurred-but-not-reported (IBNR) claims, while a data-driven regime structure lets the occurrence and reporting dynamics differ across weather conditions. In a case study on a Dutch home insurance portfolio, we compare single- and two-regime specifications for two spatial resolutions: the country as a whole, and its individual regions. We find that the regional two-regime model outperforms the regional single-regime model as well as both national models, indicating that the benefit of a multi-regime setting depends on a sufficiently granular spatial resolution. We also show that daily covariates constructed from hourly weather observations, capturing the duration and the intra-day timing of severe weather, improve both the occurrence and the reporting model.
ER  -

Abstract

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