Dataset Information

Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks.

ABSTRACT: Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new approach based on a micro-level model for reporting delays involving neural networks is proposed. It is shown by extensive simulation experiments and an application to a large-scale real data set involving motor legal insurance claims that the new approach provides more accurate predictions in case of non-homogeneous portfolios.

Supplementary information

The online version contains supplementary material available at 10.1007/s13385-022-00314-4.

SUBMITTER: Bucher A

PROVIDER: S-EPMC9098157 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks.

Bücher Axel A Rosenstock Alexander A

European actuarial journal 20220512 1

Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new approach based on a micro-level model for reporting delays involving neural networks is proposed. It is shown by extensive simulation experiments and an application to a large-scale real data set involving motor ...[more]

PMID: 35582301

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Dataset Information

Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks.

Supplementary information

Publications

Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks.

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