Estimating loss reserves using hierarchical Bayesian Gaussian process regression with input warping

Nathan Lally and Brian Hartman

Insurance: Mathematics and Economics, 2018, vol. 82, issue C, 124-140

Abstract: In this paper, we visualize the loss reserve runoff triangle as a spatially-organized data set. We apply Gaussian Process (GP) regression with input warping and several covariance functions to estimate future claims. We then compare our results over a range of product lines, including workers’ comp, medical malpractice, and personal auto. Even though the claims development of the lines are very different, the GP method is very flexible and can be applied to each without much customization. We find that our model generally outperforms the classical chain ladder model as well as the recently proposed hierarchical growth curve models of Guszcza (2008) in terms of point-wise predictive accuracy and produces dramatically better estimates of outstanding claims liabilities.

Keywords: Loss reserves; Claims triangles; Input warping; Gaussian process regression; Spatial statistics (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:82:y:2018:i:c:p:124-140

DOI: 10.1016/j.insmatheco.2018.06.008

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