Mack-net model: Blending Mack's model with Recurrent Neural Networks

In general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions. Since the Financial Crisis of 2007?2008 and the strengthening of regulation, the focus is not only on the total reserve but also on its variability, which is...

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Autores: Ramos Pérez, Eduardo, Alonso González, Pablo Jesús|||0000-0002-4999-0151, Núñez Velázquez, José Javier|||0000-0002-7084-5629
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/59298
Acceso en línea:http://hdl.handle.net/10017/59298
https://dx.doi.org/10.1016/j.eswa.2022.117146
Access Level:acceso abierto
Palabra clave:Deep Learning
Mack's model
Recurrent Neural Networks
Reserving Risk
Stochastic Reserving
Economía
Economics
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spelling Mack-net model: Blending Mack's model with Recurrent Neural NetworksRamos Pérez, EduardoAlonso González, Pablo Jesús|||0000-0002-4999-0151Núñez Velázquez, José Javier|||0000-0002-7084-5629Deep LearningMack's modelRecurrent Neural NetworksReserving RiskStochastic ReservingEconomíaEconomicsIn general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions. Since the Financial Crisis of 2007?2008 and the strengthening of regulation, the focus is not only on the total reserve but also on its variability, which is an indicator of the risk assumed by the company. Thus, measures that relate profitability with risk are crucial in order to understand the financial position of insurance firms. Taking advantage of the increasing computational power, this paper introduces a stochastic reserving model whose aim is to improve the performance of the traditional Mack?s reserving model by applying an ensemble of Recurrent Neural Networks. The results demonstrate that blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general insurance liabilities.20222022-04-06journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/59298https://dx.doi.org/10.1016/j.eswa.2022.117146reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/592982026-06-18T11:13:07Z
dc.title.none.fl_str_mv Mack-net model: Blending Mack's model with Recurrent Neural Networks
title Mack-net model: Blending Mack's model with Recurrent Neural Networks
spellingShingle Mack-net model: Blending Mack's model with Recurrent Neural Networks
Ramos Pérez, Eduardo
Deep Learning
Mack's model
Recurrent Neural Networks
Reserving Risk
Stochastic Reserving
Economía
Economics
title_short Mack-net model: Blending Mack's model with Recurrent Neural Networks
title_full Mack-net model: Blending Mack's model with Recurrent Neural Networks
title_fullStr Mack-net model: Blending Mack's model with Recurrent Neural Networks
title_full_unstemmed Mack-net model: Blending Mack's model with Recurrent Neural Networks
title_sort Mack-net model: Blending Mack's model with Recurrent Neural Networks
dc.creator.none.fl_str_mv Ramos Pérez, Eduardo
Alonso González, Pablo Jesús|||0000-0002-4999-0151
Núñez Velázquez, José Javier|||0000-0002-7084-5629
author Ramos Pérez, Eduardo
author_facet Ramos Pérez, Eduardo
Alonso González, Pablo Jesús|||0000-0002-4999-0151
Núñez Velázquez, José Javier|||0000-0002-7084-5629
author_role author
author2 Alonso González, Pablo Jesús|||0000-0002-4999-0151
Núñez Velázquez, José Javier|||0000-0002-7084-5629
author2_role author
author
dc.subject.none.fl_str_mv Deep Learning
Mack's model
Recurrent Neural Networks
Reserving Risk
Stochastic Reserving
Economía
Economics
topic Deep Learning
Mack's model
Recurrent Neural Networks
Reserving Risk
Stochastic Reserving
Economía
Economics
description In general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions. Since the Financial Crisis of 2007?2008 and the strengthening of regulation, the focus is not only on the total reserve but also on its variability, which is an indicator of the risk assumed by the company. Thus, measures that relate profitability with risk are crucial in order to understand the financial position of insurance firms. Taking advantage of the increasing computational power, this paper introduces a stochastic reserving model whose aim is to improve the performance of the traditional Mack?s reserving model by applying an ensemble of Recurrent Neural Networks. The results demonstrate that blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general insurance liabilities.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-04-06
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/59298
https://dx.doi.org/10.1016/j.eswa.2022.117146
url http://hdl.handle.net/10017/59298
https://dx.doi.org/10.1016/j.eswa.2022.117146
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
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