An individual claims reserving model for reported claims
type: Item
instance of: scholarly article
title: An individual claims reserving model for reported claims
author: Andrea Gabrielli
published in: European Actuarial Journal
publication date: 2022-01-14T00:00:00Z
review text: The article presents a claims reserving technique that uses claim-specific feature and past payment information in order to estimate claims reserves for individual reported claims. The author focuses on modeling RBNS (reported but not settled) reserves, which consist of estimated expected future payments for claims that have already been reported. A single neural network in designed, which allows to estimate expected future cash flows for every individual reported claim. A consistent way of using dropout layers in introduced in order to fit the neural network to the incomplete time series of past individual claims payment. The author mainly uses synthetic insurance data, for the sake of reproducibility. This analysis is complemented by an application on real Swiss accident insurance data. The synthetic data is generated from the individual claims history simulation machine. For every individual reported claim, there is a claim-specific feature information such as age or labor sector of the insured. After a claim gets reported, it can trigger multiple (yearly) payments. The time lags (on a yearly scale) between payments and reporting date are called payment delays. At the end of the last considered accident year, for every reported claim we have claim specific feature information and past payment information. The main goal is to use this information in order to estimate expected future payments for every individual reported claim. This corresponds to estimating RBNS reserves.
Mathematics Subject Classification ID: 91G05
Mathematics Subject Classification ID: 68T07
zbMATH DE Number: 7457623
zbMATH Keywords: claims reserving
zbMATH Keywords: individual claims
zbMATH Keywords: RBNS reserves
zbMATH Keywords: neural networks
zbMATH Keywords: multi-task learning
zbMATH Keywords: dropout
zbMATH Keywords: time series
zbMATH Keywords: micro reserving
reviewed by: G. M. Shevchenko
describes a project that uses: R
describes a project that uses: DCL
MaRDI profile type: Publication
full work available at URL: s13385-021-00271-4
OpenAlex ID: W3152912788
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DOI: 10.1007/S13385-021-00271-4
description: scientific article; zbMATH DE number 7457623