The current role of machine learning and explainability in actuarial science

Autores
Lozano, Catalina; Romero, Francisco P.; Serrano-Guerrero, Jesus; Olivas, Jose A.
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Actuarial science seeks to evaluate, predict and manage the impact of future events. Nowadays, the actuary faces the challenge of predicting and managing risks efficiently, with a universe of information growing exponentially in real-time and with a business dynamic that demands constant competitiveness and innovation. The techniques associated with data engineering and data science open a window of tools that seek, through technology, to improve the processes of product design, pricing, reserves and establishment of market niches practically and realistically, considering the pros and cons that brings the availability and constant updating of information, as well as the computational times that this implies. Therefore, this article aims to review the application of Explainable Machine Learning techniques as an alternative to the development of more efficient and practical actuarial models.
Facultad de Informática
Materia
Ciencias Informáticas
Machine learning
Actuarial Models
Explainability
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/125144

id SEDICI_21c999f2fdc20acb1df85b37589146dd
oai_identifier_str oai:sedici.unlp.edu.ar:10915/125144
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling The current role of machine learning and explainability in actuarial scienceLozano, CatalinaRomero, Francisco P.Serrano-Guerrero, JesusOlivas, Jose A.Ciencias InformáticasMachine learningActuarial ModelsExplainabilityActuarial science seeks to evaluate, predict and manage the impact of future events. Nowadays, the actuary faces the challenge of predicting and managing risks efficiently, with a universe of information growing exponentially in real-time and with a business dynamic that demands constant competitiveness and innovation. The techniques associated with data engineering and data science open a window of tools that seek, through technology, to improve the processes of product design, pricing, reserves and establishment of market niches practically and realistically, considering the pros and cons that brings the availability and constant updating of information, as well as the computational times that this implies. Therefore, this article aims to review the application of Explainable Machine Learning techniques as an alternative to the development of more efficient and practical actuarial models.Facultad de Informática2021info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf29-32http://sedici.unlp.edu.ar/handle/10915/125144enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2016-4info:eu-repo/semantics/reference/hdl/10915/121564info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:02:13Zoai:sedici.unlp.edu.ar:10915/125144Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:02:13.898SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv The current role of machine learning and explainability in actuarial science
title The current role of machine learning and explainability in actuarial science
spellingShingle The current role of machine learning and explainability in actuarial science
Lozano, Catalina
Ciencias Informáticas
Machine learning
Actuarial Models
Explainability
title_short The current role of machine learning and explainability in actuarial science
title_full The current role of machine learning and explainability in actuarial science
title_fullStr The current role of machine learning and explainability in actuarial science
title_full_unstemmed The current role of machine learning and explainability in actuarial science
title_sort The current role of machine learning and explainability in actuarial science
dc.creator.none.fl_str_mv Lozano, Catalina
Romero, Francisco P.
Serrano-Guerrero, Jesus
Olivas, Jose A.
author Lozano, Catalina
author_facet Lozano, Catalina
Romero, Francisco P.
Serrano-Guerrero, Jesus
Olivas, Jose A.
author_role author
author2 Romero, Francisco P.
Serrano-Guerrero, Jesus
Olivas, Jose A.
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Machine learning
Actuarial Models
Explainability
topic Ciencias Informáticas
Machine learning
Actuarial Models
Explainability
dc.description.none.fl_txt_mv Actuarial science seeks to evaluate, predict and manage the impact of future events. Nowadays, the actuary faces the challenge of predicting and managing risks efficiently, with a universe of information growing exponentially in real-time and with a business dynamic that demands constant competitiveness and innovation. The techniques associated with data engineering and data science open a window of tools that seek, through technology, to improve the processes of product design, pricing, reserves and establishment of market niches practically and realistically, considering the pros and cons that brings the availability and constant updating of information, as well as the computational times that this implies. Therefore, this article aims to review the application of Explainable Machine Learning techniques as an alternative to the development of more efficient and practical actuarial models.
Facultad de Informática
description Actuarial science seeks to evaluate, predict and manage the impact of future events. Nowadays, the actuary faces the challenge of predicting and managing risks efficiently, with a universe of information growing exponentially in real-time and with a business dynamic that demands constant competitiveness and innovation. The techniques associated with data engineering and data science open a window of tools that seek, through technology, to improve the processes of product design, pricing, reserves and establishment of market niches practically and realistically, considering the pros and cons that brings the availability and constant updating of information, as well as the computational times that this implies. Therefore, this article aims to review the application of Explainable Machine Learning techniques as an alternative to the development of more efficient and practical actuarial models.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/125144
url http://sedici.unlp.edu.ar/handle/10915/125144
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-2016-4
info:eu-repo/semantics/reference/hdl/10915/121564
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
29-32
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1842260520244084736
score 13.13397