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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/125144
Ver los metadatos del registro completo
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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 |
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eng |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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