Neural Networks for the Joint Development of Individual Payments and Claim Incurred

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ID: 116701
2020
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Abstract
The goal of this paper is to develop regression models and postulate distributions which can be used in practice to describe the joint development process of individual claim payments and claim incurred. We apply neural networks to estimate our regression models. As regressors we use the whole claim history of incremental payments and claim incurred, as well as any relevant feature information which is available to describe individual claims and their development characteristics. Our models are calibrated and tested on a real data set, and the results are benchmarked with the Chain-Ladder method. Our analysis focuses on the development of the so-called Reported But Not Settled (RBNS) claims. We show benefits of using deep neural network and the whole claim history in our prediction problem.
Reference Key
delong2020risksneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Łukasz Delong;Mario V. Wüthrich;Delong, Łukasz;Wüthrich, Mario V.;
Journal risks
Year 2020
DOI
10.3390/risks8020033
URL
https://www.mdpi.com/2227-9091/8/2/33
Keywords
neural networks individual claims reported but not settled claims claims simulations

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