Collective Reserving using Individual Claims Data

35 Pages Posted: 19 May 2020

See all articles by Lukasz Delong

Lukasz Delong

Warsaw School of Economics (SGH) - Institute of Econometrics

Mathias Lindholm

Stockholm University

Mario V. Wuthrich

RiskLab, ETH Zurich

Date Written: April 22, 2020

Abstract

The aim of this paper is to operationalize claims reserving based on general insurance 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.

Keywords: claims reserving, general insurance, individual claims data, micro-level reserving, neural networks, IBNR claims, RBNS claims, chain-ladder method, over-dispersed Poisson model

JEL Classification: G22, C22, C25, C45, C51, C53

Suggested Citation

Delong, Lukasz and Lindholm, Mathias and Wuthrich, Mario V., Collective Reserving using Individual Claims Data (April 22, 2020). Available at SSRN: https://ssrn.com/abstract=3582398 or http://dx.doi.org/10.2139/ssrn.3582398

Lukasz Delong

Warsaw School of Economics (SGH) - Institute of Econometrics ( email )

Niepodleglosci 164
Warsaw, 02-554
Poland

Mathias Lindholm

Stockholm University ( email )

Universitetsvägen 10
Stockholm, Stockholm SE-106 91
Sweden

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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