Collective Reserving using Individual Claims Data

Scandinavian Actuarial Journal 2021

Posted: 19 May 2020 Last revised: 11 May 2021

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


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). Scandinavian Actuarial Journal 2021, Available at SSRN: or

Lukasz Delong

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

Niepodleglosci 164
Warsaw, 02-554

Mathias Lindholm

Stockholm University ( email )

Universitetsvägen 10
Stockholm, Stockholm SE-106 91

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics