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
Barra lateral del artículo
PDF
FLIP
HTML
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Contenido principal del artículo
Resumen
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.
Palabras clave:
Descargas
Detalles del artículo
Referencias (VER)
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.
Citado por
