Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means

En este documento se presenta el análisis del comportamiento de los clientes con tarjetas de crédito de una institución financiera colombiana con base en su calificación de riesgo de crédito, a través de la aplicación del modelo de ma­chine learning no supervisado denominado K-means. Se obtienen clú...

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Autores:
Barragán Garnica, Diego
Tipo de recurso:
Article of journal
Fecha de publicación:
2023
Institución:
Universidad Externado de Colombia
Repositorio:
Biblioteca Digital Universidad Externado de Colombia
Idioma:
spa
OAI Identifier:
oai:bdigital.uexternado.edu.co:001/15348
Acceso en línea:
https://bdigital.uexternado.edu.co/handle/001/15348
https://doi.org/10.18601/17941113.n22.02
Palabra clave:
K-means;
machine learning;
credit cards;
credit score
modelo K-means;
machine learning;
tarjetas de crédito;
calificación de riesgo de crédito
Rights
openAccess
License
Diego Barragán Garnica - 2023
id uexternad2_a48621c17be117e4442d366c263a0cdf
oai_identifier_str oai:bdigital.uexternado.edu.co:001/15348
network_acronym_str uexternad2
network_name_str Biblioteca Digital Universidad Externado de Colombia
repository_id_str
dc.title.spa.fl_str_mv Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means
dc.title.translated.eng.fl_str_mv Costumer behavior with credit cards with deterioration of the risk rating using K-means
title Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means
spellingShingle Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means
K-means;
machine learning;
credit cards;
credit score
modelo K-means;
machine learning;
tarjetas de crédito;
calificación de riesgo de crédito
title_short Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means
title_full Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means
title_fullStr Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means
title_full_unstemmed Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means
title_sort Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means
dc.creator.fl_str_mv Barragán Garnica, Diego
dc.contributor.author.spa.fl_str_mv Barragán Garnica, Diego
dc.subject.eng.fl_str_mv K-means;
machine learning;
credit cards;
credit score
topic K-means;
machine learning;
credit cards;
credit score
modelo K-means;
machine learning;
tarjetas de crédito;
calificación de riesgo de crédito
dc.subject.spa.fl_str_mv modelo K-means;
machine learning;
tarjetas de crédito;
calificación de riesgo de crédito
description En este documento se presenta el análisis del comportamiento de los clientes con tarjetas de crédito de una institución financiera colombiana con base en su calificación de riesgo de crédito, a través de la aplicación del modelo de ma­chine learning no supervisado denominado K-means. Se obtienen clústeres de clientes que permiten identificar sus patrones de comportamiento.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-07-04T13:39:59Z
2024-06-07T07:31:02Z
dc.date.available.none.fl_str_mv 2023-07-04T13:39:59Z
2024-06-07T07:31:02Z
dc.date.issued.none.fl_str_mv 2023-07-04
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
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dc.identifier.issn.none.fl_str_mv 1794-1113
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dc.identifier.url.none.fl_str_mv https://doi.org/10.18601/17941113.n22.02
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url https://bdigital.uexternado.edu.co/handle/001/15348
https://doi.org/10.18601/17941113.n22.02
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https://revistas.uexternado.edu.co/index.php/odeon/article/download/8873/14885
dc.relation.citationedition.spa.fl_str_mv Núm. 22 , Año 2022 : Enero-Junio
dc.relation.citationendpage.none.fl_str_mv 37
dc.relation.citationissue.spa.fl_str_mv 22
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dc.relation.ispartofjournal.spa.fl_str_mv ODEON
dc.relation.references.spa.fl_str_mv Accenture (2019). 2019 Global Financial Services Consumer Study. Accenture.
Adams, N. M., Hand, D. J. y Till, R. J. (2001). Mining for classes and patterns in be-havioural data. Journal of the Operational Research Society, 52 (9), 1017-1024.
Banco de la República (2020). Informe especial riesgo de mercado. Banrep.
Bakoben, M., Bellotti, T. y Adams, N. (2019). Identification of credit risk based on cluster analysis of account behaviours. Journal of the Operational Research Society, 0(0), 1-9. https://doi.org/10.1080/01605682.2019.1582586
Edelman, D. B. (1992). An application of cluster analysis in credit control. IMA Journal of Management Mathematics, 4(1), 81-87. https://doi.org/10.1093/imaman/4.1.81
Eduardo, C., López, B., Alfredo, J., García, J., Antonio, J. y López, V. (2019). Banca¬ria por métodos estadísticos y redes neuronales artificiales usando r. Resumen, 40(132), 43-63.
Fisher, L. y Ness, J. W. V. (1971). Admissible clustering procedures. Biometrika, 58 (1), 91-104.
Ge, Z., Member, S., Song, Z. y Ding, S. X. (2017). Data Mining and Analytics in the Process Industry: The Role of Machine Learning, IEEE access, 5.
Grosan, C. (2011). Evolution of Modern Computational. En Intelligent Systems, Springer. 1-11.
Hsieh, N. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27(4), 623-633. https://doi.org/10.1016/j.eswa.2004.06.007
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., y Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means q. Pattern Recognition Letters, 31(8), 651-666. https://doi.org/10.1016/j.patrec.2009.09.011
Kleinberg, J. (2002). An impossibility theorem for clustering. Advances in neural information processing systems, 15.
Li, W., Wu, X., Sun, Y. y Zhang, Q. (2010). Credit card customer segmentation and target marketing based on data mining. Proceedings–2010 International Confe¬rence on Computational Intelligence and Security, cis 2010, 73-76. https://doi.org/10.1109/CIS.2010.23
Buchanan, B. G. (2005). A (Very) Brief History of Artificial Intelligence. AAAI Publi¬cations, 53-60.
Martins, M. C. y Cardoso, M. (2012). Cross-validation of segments of credit card holders. Journal of Retailing and Consumer Services, 19(6), 629-636. https://doi.org/10.1016/j.jretconser.2012.08.004
Minskyt, M. (s. d.). Steps Toward Artificial Intelligence. Proceedings of the IRE.
Morissette, L. y Chartier, S. (2013). The k-means clustering technique: General consi-derations and implementation in Mathematica. Tutorials in Quantitative Methods for Psychology, 9(1), 15-24. https://doi.org/10.20982/tqmp.09.1.p015
Paõ, U., Paõ, U., Vasco, Â., Modron, J. I., Paõ, U. y Paõ, U. (1998). Clients’ characte¬ristics and marketing of products: Some evidence from a financial institution. https://doi.org/10.1108/02652320310488420
Pelleg, D. y Moore, A. W. (2000). X-means: Extending k-means with efficient estimation of the number of clusters. Icml, 1, 727-734.
Shefrin, H. y Nicols, C. M. (2014). Credit card behavior, financial styles, and heuris¬tics. Journal of Business Research, 67(8), 1679-1687. https://doi.org/10.1016/j.jbusres.2014.02.014
Scholkopf, B., Smola, A. y Muller, K. (1998). Non-linear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299-1319. https://doi.org/10.1162/089976698300017467
Soeini, R. A. y Rodpysh, K. V. (2012). Applying Data Mining to Insurance Customer Churn Management, 30, 82-92.
Soukal, I. y Hedvicaková, M. (2011). Procedia computer retail core banking services e-banking client cluster identification. Procedia Computer Science, 3, 1205-1210. https://doi.org/10.1016/j.procs.2010.12.195
Timón, C. E. (2017). Análisis predictivo: técnicas y modelos utilizados y aplicaciones del mismo–Herramientas Open Source que permiten su uso (trabajo de de grado).
Wallace, C. S. y Boulton, D. M. (1968). An information measure for classification. The Computer Journal, 11 (2), 185-194.
Wallace, C. S. y Freeman, P. R. (1987). Estimation and inference by compact coding. Journal of the Royal Statistical Society: Series B (Methodological), 49 (3), 240- 252.
dc.rights.spa.fl_str_mv Diego Barragán Garnica - 2023
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spelling Barragán Garnica, Diego2023-07-04T13:39:59Z2024-06-07T07:31:02Z2023-07-04T13:39:59Z2024-06-07T07:31:02Z2023-07-04En este documento se presenta el análisis del comportamiento de los clientes con tarjetas de crédito de una institución financiera colombiana con base en su calificación de riesgo de crédito, a través de la aplicación del modelo de ma­chine learning no supervisado denominado K-means. Se obtienen clústeres de clientes que permiten identificar sus patrones de comportamiento.This document presents the analysis of the behavior of cardholders of a Colombian financial institution based on their credit risk rating through the application of the unsupervised machine learning model called K-means. Clusters of clients are obtained that allow identifying their behavior.application/pdftext/html10.18601/17941113.n22.022346-21401794-1113https://bdigital.uexternado.edu.co/handle/001/15348https://doi.org/10.18601/17941113.n22.02spaUniversidad Externado de Colombiahttps://revistas.uexternado.edu.co/index.php/odeon/article/download/8873/14884https://revistas.uexternado.edu.co/index.php/odeon/article/download/8873/14885Núm. 22 , Año 2022 : Enero-Junio37227ODEONAccenture (2019). 2019 Global Financial Services Consumer Study. Accenture.Adams, N. M., Hand, D. J. y Till, R. J. (2001). Mining for classes and patterns in be-havioural data. Journal of the Operational Research Society, 52 (9), 1017-1024.Banco de la República (2020). Informe especial riesgo de mercado. Banrep.Bakoben, M., Bellotti, T. y Adams, N. (2019). Identification of credit risk based on cluster analysis of account behaviours. Journal of the Operational Research Society, 0(0), 1-9. https://doi.org/10.1080/01605682.2019.1582586Edelman, D. B. (1992). An application of cluster analysis in credit control. IMA Journal of Management Mathematics, 4(1), 81-87. https://doi.org/10.1093/imaman/4.1.81Eduardo, C., López, B., Alfredo, J., García, J., Antonio, J. y López, V. (2019). Banca¬ria por métodos estadísticos y redes neuronales artificiales usando r. Resumen, 40(132), 43-63.Fisher, L. y Ness, J. W. V. (1971). Admissible clustering procedures. Biometrika, 58 (1), 91-104.Ge, Z., Member, S., Song, Z. y Ding, S. X. (2017). Data Mining and Analytics in the Process Industry: The Role of Machine Learning, IEEE access, 5.Grosan, C. (2011). Evolution of Modern Computational. En Intelligent Systems, Springer. 1-11.Hsieh, N. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27(4), 623-633. https://doi.org/10.1016/j.eswa.2004.06.007Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., y Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.Jain, A. K. (2010). Data clustering: 50 years beyond K-means q. Pattern Recognition Letters, 31(8), 651-666. https://doi.org/10.1016/j.patrec.2009.09.011Kleinberg, J. (2002). An impossibility theorem for clustering. Advances in neural information processing systems, 15.Li, W., Wu, X., Sun, Y. y Zhang, Q. (2010). Credit card customer segmentation and target marketing based on data mining. Proceedings–2010 International Confe¬rence on Computational Intelligence and Security, cis 2010, 73-76. https://doi.org/10.1109/CIS.2010.23Buchanan, B. G. (2005). A (Very) Brief History of Artificial Intelligence. AAAI Publi¬cations, 53-60.Martins, M. C. y Cardoso, M. (2012). Cross-validation of segments of credit card holders. Journal of Retailing and Consumer Services, 19(6), 629-636. https://doi.org/10.1016/j.jretconser.2012.08.004Minskyt, M. (s. d.). Steps Toward Artificial Intelligence. Proceedings of the IRE.Morissette, L. y Chartier, S. (2013). The k-means clustering technique: General consi-derations and implementation in Mathematica. Tutorials in Quantitative Methods for Psychology, 9(1), 15-24. https://doi.org/10.20982/tqmp.09.1.p015Paõ, U., Paõ, U., Vasco, Â., Modron, J. I., Paõ, U. y Paõ, U. (1998). Clients’ characte¬ristics and marketing of products: Some evidence from a financial institution. https://doi.org/10.1108/02652320310488420Pelleg, D. y Moore, A. W. (2000). X-means: Extending k-means with efficient estimation of the number of clusters. Icml, 1, 727-734.Shefrin, H. y Nicols, C. M. (2014). Credit card behavior, financial styles, and heuris¬tics. Journal of Business Research, 67(8), 1679-1687. https://doi.org/10.1016/j.jbusres.2014.02.014Scholkopf, B., Smola, A. y Muller, K. (1998). Non-linear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299-1319. https://doi.org/10.1162/089976698300017467Soeini, R. A. y Rodpysh, K. V. (2012). Applying Data Mining to Insurance Customer Churn Management, 30, 82-92.Soukal, I. y Hedvicaková, M. (2011). Procedia computer retail core banking services e-banking client cluster identification. Procedia Computer Science, 3, 1205-1210. https://doi.org/10.1016/j.procs.2010.12.195Timón, C. E. (2017). Análisis predictivo: técnicas y modelos utilizados y aplicaciones del mismo–Herramientas Open Source que permiten su uso (trabajo de de grado).Wallace, C. S. y Boulton, D. M. (1968). An information measure for classification. The Computer Journal, 11 (2), 185-194.Wallace, C. S. y Freeman, P. R. (1987). Estimation and inference by compact coding. Journal of the Royal Statistical Society: Series B (Methodological), 49 (3), 240- 252.Diego Barragán Garnica - 2023info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.http://creativecommons.org/licenses/by-nc-sa/4.0https://revistas.uexternado.edu.co/index.php/odeon/article/view/8873K-means;machine learning;credit cards;credit scoremodelo K-means;machine learning;tarjetas de crédito;calificación de riesgo de créditoPatrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-meansCostumer behavior with credit cards with deterioration of the risk rating using K-meansArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml2642https://bdigital.uexternado.edu.co/bitstreams/2cef4011-8a2c-47bd-b52a-fcdb44178215/download62eb04f75f644f333da8d330024cccdfMD51001/15348oai:bdigital.uexternado.edu.co:001/153482024-06-07 02:31:03.034http://creativecommons.org/licenses/by-nc-sa/4.0Diego Barragán Garnica - 2023https://bdigital.uexternado.edu.coUniversidad Externado de Colombiametabiblioteca@metabiblioteca.org

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