Decision tree applied in classifying the occurrence of cyber claims in banking sector companies

Detalhes bibliográficos
Autor(a) principal: Azevedo, Alana Katielli Nogueira
Data de Publicação: 2023
Tipo de documento: Artigo
Idioma: eng
por
Título da fonte: Contextus (Fortaleza. Online)
Texto Completo: http://periodicos.ufc.br/contextus/article/view/83423
Resumo: The study aimed to predict cyber claims in companies in the banking sector using a decision tree. To this end, 683 cases of cyber losses were extracted from an operational risk database. The independent variables considered in the modeling were the region of domicile, the size of the company and, as main explanatory variable, revenue. The classification reached 89% of global hits. The modeling in question guarantees a good classification quality and better fit when compared to traditional GLM modeling. The results of this work are useful and can act in an innovative way as a tool to support the decision making of insurers, aiming at useful responses to the management of cyber risks.
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spelling Decision tree applied in classifying the occurrence of cyber claims in banking sector companiesÁrbol de decisión aplicado en la clasificación de la ocurrencia de siniestros cibernéticos en empresas del sector bancario Árvore de decisão aplicada na classificação de ocorrência de sinistro cibernético em empresas do setor bancário risk managementcyber riskdecision treeGLMbanking sectorgerenciamento de riscorisco cibernéticoárvore de decisãoGLMsetor bancáriogestión de riesgosciberriesgoárbol de decisionesGLMsector bancarioThe study aimed to predict cyber claims in companies in the banking sector using a decision tree. To this end, 683 cases of cyber losses were extracted from an operational risk database. The independent variables considered in the modeling were the region of domicile, the size of the company and, as main explanatory variable, revenue. The classification reached 89% of global hits. The modeling in question guarantees a good classification quality and better fit when compared to traditional GLM modeling. The results of this work are useful and can act in an innovative way as a tool to support the decision making of insurers, aiming at useful responses to the management of cyber risks.El estudio tuvo como objetivo predecir ciber siniestros en empresas del sector bancario utilizando un árbol de decisión. Para ello, se extrajeron de una base de datos de riesgo operacional 683 casos de ciberpérdidas. Las variables independientes consideradas en la modelación fueron la región de domicilio, el tamaño de la empresa y, como principal variable explicativa, los ingresos. La clasificación alcanzó 89% de los hits globales. El modelado en cuestión garantiza una buena calidad de clasificación y un mejor ajuste en comparación con el modelado GLM tradicional. Los resultados son útiles y pueden actuar de forma innovadora como una herramienta de apoyo a la toma de decisiones de las aseguradoras, buscando respuestas útiles a la gestión de los riesgos cibernéticos. O estudo teve como objetivo a previsão de sinistros cibernéticos em empresas do setor bancário através do uso de árvore de decisão. Para tanto, foram extraídos 683 casos de perdas cibernéticas de um banco de dados de risco operacional. As variáveis independentes consideradas na modelagem foram a região de domicílio, o porte da empresa e, como principal variável explicativa, o faturamento. A classificação apresentou 89% de acertos globais. A modelagem em questão garante uma boa qualidade de classificação e melhor ajuste quando comparada a modelagem tradicional GLM. Os resultados desse trabalho são úteis e podem atuar de forma inovadora como ferramenta de apoio à tomada de decisão das seguradoras, visando respostas úteis ao gerenciamento de riscos cibernéticos. FEAAC/UFC2023-10-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfapplication/ziphttp://periodicos.ufc.br/contextus/article/view/8342310.19094/contextus.2023.83423Contextus - Contemporary Journal of Economics and Management; Vol. 21 No. esp.1 (2023): Special Edition - Actuarial Sciences; e83423Contextus – Revista Contemporánea de Economía y Gestión; Vol. 21 Núm. esp.1 (2023): Edición Especial - Ciencias Actuariales; e83423Contextus – Revista Contemporânea de Economia e Gestão; v. 21 n. esp.1 (2023): Edição Especial - Ciências Atuariais; e834232178-92581678-2089reponame:Contextus (Fortaleza. Online)instname:Universidade Federal do Ceará (UFC)instacron:UFCengporhttp://periodicos.ufc.br/contextus/article/view/83423/249768http://periodicos.ufc.br/contextus/article/view/83423/249769http://periodicos.ufc.br/contextus/article/view/83423/250230http://periodicos.ufc.br/contextus/article/view/83423/250231http://periodicos.ufc.br/contextus/article/view/83423/250232http://periodicos.ufc.br/contextus/article/view/83423/250233Copyright (c) 2023 Revista: apenas para a 1a. publicaçãohttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccess Azevedo, Alana Katielli Nogueira2024-02-28T23:43:27Zoai:periodicos.ufc:article/83423Revistahttp://periodicos.ufc.br/contextusPUBhttp://periodicos.ufc.br/contextus/oairevistacontextus@ufc.br2178-92581678-2089opendoar:2024-02-28T23:43:27Contextus (Fortaleza. Online) - Universidade Federal do Ceará (UFC)false
dc.title.none.fl_str_mv Decision tree applied in classifying the occurrence of cyber claims in banking sector companies
Árbol de decisión aplicado en la clasificación de la ocurrencia de siniestros cibernéticos en empresas del sector bancario
Árvore de decisão aplicada na classificação de ocorrência de sinistro cibernético em empresas do setor bancário
title Decision tree applied in classifying the occurrence of cyber claims in banking sector companies
spellingShingle Decision tree applied in classifying the occurrence of cyber claims in banking sector companies
Azevedo, Alana Katielli Nogueira
risk management
cyber risk
decision tree
GLM
banking sector
gerenciamento de risco
risco cibernético
árvore de decisão
GLM
setor bancário
gestión de riesgos
ciberriesgo
árbol de decisiones
GLM
sector bancario
title_short Decision tree applied in classifying the occurrence of cyber claims in banking sector companies
title_full Decision tree applied in classifying the occurrence of cyber claims in banking sector companies
title_fullStr Decision tree applied in classifying the occurrence of cyber claims in banking sector companies
title_full_unstemmed Decision tree applied in classifying the occurrence of cyber claims in banking sector companies
title_sort Decision tree applied in classifying the occurrence of cyber claims in banking sector companies
author Azevedo, Alana Katielli Nogueira
author_facet Azevedo, Alana Katielli Nogueira
author_role author
dc.contributor.author.fl_str_mv Azevedo, Alana Katielli Nogueira
dc.subject.por.fl_str_mv risk management
cyber risk
decision tree
GLM
banking sector
gerenciamento de risco
risco cibernético
árvore de decisão
GLM
setor bancário
gestión de riesgos
ciberriesgo
árbol de decisiones
GLM
sector bancario
topic risk management
cyber risk
decision tree
GLM
banking sector
gerenciamento de risco
risco cibernético
árvore de decisão
GLM
setor bancário
gestión de riesgos
ciberriesgo
árbol de decisiones
GLM
sector bancario
description The study aimed to predict cyber claims in companies in the banking sector using a decision tree. To this end, 683 cases of cyber losses were extracted from an operational risk database. The independent variables considered in the modeling were the region of domicile, the size of the company and, as main explanatory variable, revenue. The classification reached 89% of global hits. The modeling in question guarantees a good classification quality and better fit when compared to traditional GLM modeling. The results of this work are useful and can act in an innovative way as a tool to support the decision making of insurers, aiming at useful responses to the management of cyber risks.
publishDate 2023
dc.date.none.fl_str_mv 2023-10-17
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://periodicos.ufc.br/contextus/article/view/83423
10.19094/contextus.2023.83423
url http://periodicos.ufc.br/contextus/article/view/83423
identifier_str_mv 10.19094/contextus.2023.83423
dc.language.iso.fl_str_mv eng
por
language eng
por
dc.relation.none.fl_str_mv http://periodicos.ufc.br/contextus/article/view/83423/249768
http://periodicos.ufc.br/contextus/article/view/83423/249769
http://periodicos.ufc.br/contextus/article/view/83423/250230
http://periodicos.ufc.br/contextus/article/view/83423/250231
http://periodicos.ufc.br/contextus/article/view/83423/250232
http://periodicos.ufc.br/contextus/article/view/83423/250233
dc.rights.driver.fl_str_mv Copyright (c) 2023 Revista: apenas para a 1a. publicação
http://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2023 Revista: apenas para a 1a. publicação
http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
application/zip
dc.publisher.none.fl_str_mv FEAAC/UFC
publisher.none.fl_str_mv FEAAC/UFC
dc.source.none.fl_str_mv Contextus - Contemporary Journal of Economics and Management; Vol. 21 No. esp.1 (2023): Special Edition - Actuarial Sciences; e83423
Contextus – Revista Contemporánea de Economía y Gestión; Vol. 21 Núm. esp.1 (2023): Edición Especial - Ciencias Actuariales; e83423
Contextus – Revista Contemporânea de Economia e Gestão; v. 21 n. esp.1 (2023): Edição Especial - Ciências Atuariais; e83423
2178-9258
1678-2089
reponame:Contextus (Fortaleza. Online)
instname:Universidade Federal do Ceará (UFC)
instacron:UFC
instname_str Universidade Federal do Ceará (UFC)
instacron_str UFC
institution UFC
reponame_str Contextus (Fortaleza. Online)
collection Contextus (Fortaleza. Online)
repository.name.fl_str_mv Contextus (Fortaleza. Online) - Universidade Federal do Ceará (UFC)
repository.mail.fl_str_mv revistacontextus@ufc.br
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