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Gaussian process models in actuarial science

Michael Ludkovski

Annals of Actuarial Science2026https://doi.org/10.1017/s1748499526100244article
AJG 1ABDC A
Weight
0.50

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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https://doi.org/https://doi.org/10.1017/s1748499526100244

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@article{michael2026,
  title        = {{Gaussian process models in actuarial science}},
  author       = {Michael Ludkovski},
  journal      = {Annals of Actuarial Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/s1748499526100244},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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