Vehicle claims in the south of Minas Gerais: an approach using classification models
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/43330 |
Resumo: | With the changes in the patterns of risk, new insurance products are available on the market. Consequently, pricing models are restructured to manage levels of risk and create premiums that maintain the well-being of insurers. This work analyzed the Logistics and Random forests models in the classification of total loss events in the south of Minas Gerais using original and artificial samples, built by the ROSE resampling method, which is a procedure for constructing artificial samples in a smoothing bootstrap. A total loss of a vehicle is considered when the repair costs for the same event exceed a percentage established by contract. As a result, it was obtained that the models with artificial data improved the balanced accuracy rate on unbalanced data. |
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Vehicle claims in the south of Minas Gerais: an approach using classification modelsOcorrência de sinistros em veículos no sul de Minas Gerais: uma abordagem via modelos de classificaçãoRandom forestRandom over sampling examplesLogistic regressionFloresta aleatóriaAmostra aleatóriaRegressão logísticaWith the changes in the patterns of risk, new insurance products are available on the market. Consequently, pricing models are restructured to manage levels of risk and create premiums that maintain the well-being of insurers. This work analyzed the Logistics and Random forests models in the classification of total loss events in the south of Minas Gerais using original and artificial samples, built by the ROSE resampling method, which is a procedure for constructing artificial samples in a smoothing bootstrap. A total loss of a vehicle is considered when the repair costs for the same event exceed a percentage established by contract. As a result, it was obtained that the models with artificial data improved the balanced accuracy rate on unbalanced data.Com as mudanças nos padrões de risco, novos produtos de seguros são disponibilizados no mercado, atendendo as demandas do consumidor. Consequentemente, os modelos de precificação são reestruturados de modo a gerenciar os níveis de risco e estabelecer prêmios que mantenham o bem estar atuarial, alocando apólices em carteiras através de modelos de classificação e clusterização. Este trabalho analisou o desempenho dos modelos Logísticos e Random forests na classificação de ocorrências de sinistros do tipo colisão por perda total no sul de Minas Gerais utilizando amostras de treino originais e artificiais via método de reamostragem ROSE, que é um procedimento de construção de amostras artificiais em uma suavização bootstrap. Considerase a perda total de um veículo quando os custos de reparos do sinistro de um mesmo evento superarem um percentual estabelecido contratualmente. Como resultado, obteve-se que os modelos com amostra artificial apresentaram resultados de acurácia balanceada superiores aos demais, indicando a melhoria através de métodos de reamostragem durante o treino.Universidade Estadual de Londrina2020-10-06T20:59:33Z2020-10-06T20:59:33Z2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfPALA, L. O. de O. et al. Vehicle claims in the south of Minas Gerais: an approach using classification models. Semina: Ciências Exatas e Tecnológicas, Londrina, v. 41, n. 1, p. 79-86, Jan./June 2020. DOI: http://dx.doi.org/10.5433/1679-0375.2020v41n1p79.http://repositorio.ufla.br/jspui/handle/1/43330Semina: Ciências Exatas e Tecnológicasreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessPala, Luiz Otávio de OliveiraCarvalho, Marcela de MarillacGuimarães, Paulo Henrique SalesSáfadi, Thelmaeng2023-05-26T19:36:02Zoai:localhost:1/43330Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-26T19:36:02Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Vehicle claims in the south of Minas Gerais: an approach using classification models Ocorrência de sinistros em veículos no sul de Minas Gerais: uma abordagem via modelos de classificação |
title |
Vehicle claims in the south of Minas Gerais: an approach using classification models |
spellingShingle |
Vehicle claims in the south of Minas Gerais: an approach using classification models Pala, Luiz Otávio de Oliveira Random forest Random over sampling examples Logistic regression Floresta aleatória Amostra aleatória Regressão logística |
title_short |
Vehicle claims in the south of Minas Gerais: an approach using classification models |
title_full |
Vehicle claims in the south of Minas Gerais: an approach using classification models |
title_fullStr |
Vehicle claims in the south of Minas Gerais: an approach using classification models |
title_full_unstemmed |
Vehicle claims in the south of Minas Gerais: an approach using classification models |
title_sort |
Vehicle claims in the south of Minas Gerais: an approach using classification models |
author |
Pala, Luiz Otávio de Oliveira |
author_facet |
Pala, Luiz Otávio de Oliveira Carvalho, Marcela de Marillac Guimarães, Paulo Henrique Sales Sáfadi, Thelma |
author_role |
author |
author2 |
Carvalho, Marcela de Marillac Guimarães, Paulo Henrique Sales Sáfadi, Thelma |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Pala, Luiz Otávio de Oliveira Carvalho, Marcela de Marillac Guimarães, Paulo Henrique Sales Sáfadi, Thelma |
dc.subject.por.fl_str_mv |
Random forest Random over sampling examples Logistic regression Floresta aleatória Amostra aleatória Regressão logística |
topic |
Random forest Random over sampling examples Logistic regression Floresta aleatória Amostra aleatória Regressão logística |
description |
With the changes in the patterns of risk, new insurance products are available on the market. Consequently, pricing models are restructured to manage levels of risk and create premiums that maintain the well-being of insurers. This work analyzed the Logistics and Random forests models in the classification of total loss events in the south of Minas Gerais using original and artificial samples, built by the ROSE resampling method, which is a procedure for constructing artificial samples in a smoothing bootstrap. A total loss of a vehicle is considered when the repair costs for the same event exceed a percentage established by contract. As a result, it was obtained that the models with artificial data improved the balanced accuracy rate on unbalanced data. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-10-06T20:59:33Z 2020-10-06T20:59:33Z 2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
PALA, L. O. de O. et al. Vehicle claims in the south of Minas Gerais: an approach using classification models. Semina: Ciências Exatas e Tecnológicas, Londrina, v. 41, n. 1, p. 79-86, Jan./June 2020. DOI: http://dx.doi.org/10.5433/1679-0375.2020v41n1p79. http://repositorio.ufla.br/jspui/handle/1/43330 |
identifier_str_mv |
PALA, L. O. de O. et al. Vehicle claims in the south of Minas Gerais: an approach using classification models. Semina: Ciências Exatas e Tecnológicas, Londrina, v. 41, n. 1, p. 79-86, Jan./June 2020. DOI: http://dx.doi.org/10.5433/1679-0375.2020v41n1p79. |
url |
http://repositorio.ufla.br/jspui/handle/1/43330 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual de Londrina |
publisher.none.fl_str_mv |
Universidade Estadual de Londrina |
dc.source.none.fl_str_mv |
Semina: Ciências Exatas e Tecnológicas reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1815439270326829056 |