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

Łukasz Delong and Mario V. Wüthrich
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Mario V. Wüthrich: Department of Mathematics, ETH Zurich, RiskLab, Rämistrasse 101, 8092 Zurich, Switzerland

Risks, 2020, vol. 8, issue 2, 1-34

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.

Keywords: neural networks; individual claims; reported but not settled claims; claims simulations (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:8:y:2020:i:2:p:33-:d:342279

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