A machine learning approach for banks classification and forecast

n this research, a classification model is developed for the banking sector using the machine earning technique GLMNET. In the first place, a clustering process was developed, where 3 clearly differentiated groups were found. Subsequently, a Fuzzy analysis was performed finding the probabilities of...

Full description

Autores:
Fontalvo Herrera, Tomas
De La Hoz Dominguez, Enrique
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/12096
Acceso en línea:
https://hdl.handle.net/20.500.12585/12096
Palabra clave:
Customer Churn;
Sales;
Customer Relationship Management
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv A machine learning approach for banks classification and forecast
title A machine learning approach for banks classification and forecast
spellingShingle A machine learning approach for banks classification and forecast
Customer Churn;
Sales;
Customer Relationship Management
LEMB
title_short A machine learning approach for banks classification and forecast
title_full A machine learning approach for banks classification and forecast
title_fullStr A machine learning approach for banks classification and forecast
title_full_unstemmed A machine learning approach for banks classification and forecast
title_sort A machine learning approach for banks classification and forecast
dc.creator.fl_str_mv Fontalvo Herrera, Tomas
De La Hoz Dominguez, Enrique
dc.contributor.author.none.fl_str_mv Fontalvo Herrera, Tomas
De La Hoz Dominguez, Enrique
dc.subject.keywords.spa.fl_str_mv Customer Churn;
Sales;
Customer Relationship Management
topic Customer Churn;
Sales;
Customer Relationship Management
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description n this research, a classification model is developed for the banking sector using the machine earning technique GLMNET. In the first place, a clustering process was developed, where 3 clearly differentiated groups were found. Subsequently, a Fuzzy analysis was performed finding the probabilities of transition of the banks to each group found, finally, the GLMNET algorithm was implemented, the automatic classification of the banks according to their financial items, obtaining a result of 95% accuracy. © 2019 International Business Information Management Association (IBIMA).
publishDate 2019
dc.date.issued.none.fl_str_mv 2019-04
dc.date.accessioned.none.fl_str_mv 2023-07-14T13:47:54Z
dc.date.available.none.fl_str_mv 2023-07-14T13:47:54Z
dc.date.submitted.none.fl_str_mv 2023-07
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status_str draft
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/12096
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/12096
identifier_str_mv Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.format.extent.none.fl_str_mv 10 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv Proceedings of the 33rd International Business Information Management Association Conference, IBIMA 2019: Education Excellence and Innovation Management through Vision 2020
institution Universidad Tecnológica de Bolívar
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spelling Fontalvo Herrera, Tomaseb7fcb9d-ad94-4c1c-a9c5-d8d3fc30bee2De La Hoz Dominguez, Enriquead200026-a42f-466a-ac7a-31f67a4303c32023-07-14T13:47:54Z2023-07-14T13:47:54Z2019-042023-07https://hdl.handle.net/20.500.12585/12096Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de Bolívarn this research, a classification model is developed for the banking sector using the machine earning technique GLMNET. In the first place, a clustering process was developed, where 3 clearly differentiated groups were found. Subsequently, a Fuzzy analysis was performed finding the probabilities of transition of the banks to each group found, finally, the GLMNET algorithm was implemented, the automatic classification of the banks according to their financial items, obtaining a result of 95% accuracy. © 2019 International Business Information Management Association (IBIMA).10 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Proceedings of the 33rd International Business Information Management Association Conference, IBIMA 2019: Education Excellence and Innovation Management through Vision 2020A machine learning approach for banks classification and forecastinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Customer Churn;Sales;Customer Relationship ManagementLEMBCartagena de IndiasAcuna, E., Rodriguez, C. 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Cited 10640 times. doi: 10.1111/j.1467-9868.2005.00503.xhttp://purl.org/coar/resource_type/c_6501ORIGINALA machine learning approach for banks classification and forecast.pdfA machine learning approach for banks classification and forecast.pdfapplication/pdf149545https://repositorio.utb.edu.co/bitstream/20.500.12585/12096/1/A%20machine%20learning%20approach%20for%20banks%20classification%20and%20forecast.pdf5a651ec0964c104a5d3b93668f3a9523MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/12096/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12096/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXTA machine learning approach for banks classification and forecast.pdf.txtA machine learning approach for banks classification and forecast.pdf.txtExtracted 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