Titulo:
Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means
.
Sumario:
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.
Guardado en:
1794-1113
2346-2140
2023-07-04
7
37
Diego Barragán Garnica - 2023
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
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Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means Costumer behavior with credit cards with deterioration of the risk rating using 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 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. Barragán Garnica, Diego K-means; machine learning; credit cards; credit score modelo K-means; machine learning; tarjetas de crédito; calificación de riesgo de crédito 22 Núm. 22 , Año 2022 : Enero-Junio Artículo de revista Journal article 2023-07-04T13:39:59Z 2023-07-04T13:39:59Z 2023-07-04 application/pdf text/html Universidad Externado de Colombia ODEON 1794-1113 2346-2140 https://revistas.uexternado.edu.co/index.php/odeon/article/view/8873 10.18601/17941113.n22.02 https://doi.org/10.18601/17941113.n22.02 spa http://creativecommons.org/licenses/by-nc-sa/4.0 Diego Barragán Garnica - 2023 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. 7 37 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. https://revistas.uexternado.edu.co/index.php/odeon/article/download/8873/14884 https://revistas.uexternado.edu.co/index.php/odeon/article/download/8873/14885 info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 http://purl.org/redcol/resource_type/ARTREF info:eu-repo/semantics/publishedVersion http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text Publication |
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UNIVERSIDAD EXTERNADO DE COLOMBIA |
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Colombia |
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Revista ODEON |
| 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 Barragán Garnica, Diego 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 |
| title_eng |
Costumer behavior with credit cards with deterioration of the risk rating using K-means |
| 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.
|
| description_eng |
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.
|
| author |
Barragán Garnica, Diego |
| author_facet |
Barragán Garnica, Diego |
| topic |
K-means; machine learning; credit cards; credit score modelo K-means; machine learning; tarjetas de crédito; calificación de riesgo de crédito |
| topic_facet |
K-means; machine learning; credit cards; credit score modelo K-means; machine learning; tarjetas de crédito; calificación de riesgo de crédito |
| topicspa_str_mv |
modelo K-means; machine learning; tarjetas de crédito; calificación de riesgo de crédito |
| citationissue |
22 |
| citationedition |
Núm. 22 , Año 2022 : Enero-Junio |
| publisher |
Universidad Externado de Colombia |
| ispartofjournal |
ODEON |
| source |
https://revistas.uexternado.edu.co/index.php/odeon/article/view/8873 |
| language |
spa |
| format |
Article |
| rights |
http://creativecommons.org/licenses/by-nc-sa/4.0 Diego Barragán Garnica - 2023 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| references |
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. |
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