Collective reserving using individual claims data
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Author / Producer
Date
2022
Publication Type
Journal Article
ETH Bibliography
yes
Citations
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Data
Abstract
The aim of this paper is to operationalize claims reserving based on individual claims data. We design a modeling architecture that is based on six different neural networks. Each network is a separate module that serves a certain modeling purpose. We apply our architecture to individual claims data and predict their settlement processes on a monthly time grid. A proof of concept is provided by benchmarking the resulting claims reserves with the ones received from the classical chain-ladder method which uses much coarser (aggregated) data.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
2022 (1)
Pages / Article No.
1 - 28
Publisher
Taylor & Francis
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Claims reserving; Individual claims data; micro-level reserving; neural networks; IBNR claims; RBNS claims; chain-ladder method; over-dispersed Poisson model
Organisational unit
08813 - Wüthrich, Mario Valentin (Tit.-Prof.)
02204 - RiskLab / RiskLab