Gaussian process models in actuarial science

Abstract Gaussian Process (GP) modeling is a probabilistic, non-parametric framework for describing spatio-temporal dependence that is well-suited for fitting risk-related surfaces. I summarize the main emerging actuarial use cases of GPs, including their applications in longevity modeling, insurance contract valuation, and loss development. The editorial also discusses further contexts with potential for GP-based approaches.

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Publication Details

Journal
Annals of Actuarial Science
Published
2026-02-26
DOI
https://doi.org/10.1017/s1748499526100244
Primary Topic
Soil Geostatistics and Mapping
Type
article
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article

Gaussian process models in actuarial science

Michael Ludkovski
Annals of Actuarial Science
Soil Geostatistics and Mapping
article

Gaussian process models in actuarial science

Michael Ludkovski
article en

Abstract

Abstract Gaussian Process (GP) modeling is a probabilistic, non-parametric framework for describing spatio-temporal dependence that is well-suited for fitting risk-related surfaces. I summarize the main emerging actuarial use cases of GPs, including their applications in longevity modeling, insurance contract valuation, and loss development. The editorial also discusses further contexts with potential for GP-based approaches.

Annals of Actuarial ScienceVol. 20(1)
University of California, Santa Barbara (US)
Decent work and economic growth
Openalex Percentile: Top 50%
Soil Geostatistics and Mapping
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Gaussian process models in actuarial science — Michael Ludkovski · Annals of Actuarial Science (2026) | TGRS Research Map | TGRS