Stochastic Claims Reserving Methods with State Space Representations: A Review

Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle claims reserving appropriately. Although...

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Autores principales:	Nataliya Chukhrova, Arne Johannssen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	adaptive learning dependence modeling evolutionary models insurance Kalman filter machine learning Insurance HG8011-9999
Acceso en línea:	https://doaj.org/article/3fa5277fc74a4edf966911cef0de49a5
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id	oai:doaj.org-article:3fa5277fc74a4edf966911cef0de49a5
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spelling	oai:doaj.org-article:3fa5277fc74a4edf966911cef0de49a52021-11-25T18:56:09ZStochastic Claims Reserving Methods with State Space Representations: A Review10.3390/risks91101982227-9091https://doaj.org/article/3fa5277fc74a4edf966911cef0de49a52021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9091/9/11/198https://doaj.org/toc/2227-9091Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle claims reserving appropriately. Although claims data are time series data, the majority of the proposed (stochastic) claims reserving methods is not based on time series models. Among the time series models, state space models combined with Kalman filter learning algorithms have proven to be very advantageous as they provide high flexibility in modeling and an accurate detection of the temporal dynamics of a system. Against this backdrop, this paper aims to provide a comprehensive review of stochastic claims reserving methods that have been developed and analyzed in the context of state space representations. For this purpose, relevant articles are collected and categorized, and the contents are explained in detail and subjected to a conceptual comparison.Nataliya ChukhrovaArne JohannssenMDPI AGarticleadaptive learningdependence modelingevolutionary modelsinsuranceKalman filtermachine learningInsuranceHG8011-9999ENRisks, Vol 9, Iss 198, p 198 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	adaptive learning dependence modeling evolutionary models insurance Kalman filter machine learning Insurance HG8011-9999
spellingShingle	adaptive learning dependence modeling evolutionary models insurance Kalman filter machine learning Insurance HG8011-9999 Nataliya Chukhrova Arne Johannssen Stochastic Claims Reserving Methods with State Space Representations: A Review
description	Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle claims reserving appropriately. Although claims data are time series data, the majority of the proposed (stochastic) claims reserving methods is not based on time series models. Among the time series models, state space models combined with Kalman filter learning algorithms have proven to be very advantageous as they provide high flexibility in modeling and an accurate detection of the temporal dynamics of a system. Against this backdrop, this paper aims to provide a comprehensive review of stochastic claims reserving methods that have been developed and analyzed in the context of state space representations. For this purpose, relevant articles are collected and categorized, and the contents are explained in detail and subjected to a conceptual comparison.
format	article
author	Nataliya Chukhrova Arne Johannssen
author_facet	Nataliya Chukhrova Arne Johannssen
author_sort	Nataliya Chukhrova
title	Stochastic Claims Reserving Methods with State Space Representations: A Review
title_short	Stochastic Claims Reserving Methods with State Space Representations: A Review
title_full	Stochastic Claims Reserving Methods with State Space Representations: A Review
title_fullStr	Stochastic Claims Reserving Methods with State Space Representations: A Review
title_full_unstemmed	Stochastic Claims Reserving Methods with State Space Representations: A Review
title_sort	stochastic claims reserving methods with state space representations: a review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3fa5277fc74a4edf966911cef0de49a5
work_keys_str_mv	AT nataliyachukhrova stochasticclaimsreservingmethodswithstatespacerepresentationsareview AT arnejohannssen stochasticclaimsreservingmethodswithstatespacerepresentationsareview
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Stochastic Claims Reserving Methods with State Space Representations: A Review

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