Show simple item record

dc.contributor.advisorGalpin, Ixent
dc.contributor.advisorMade available in DSpace on 2020-09-25T21:52:14Z (GMT). No. of bitstreams: 1 Trabajo_Grado.zip: 1546897 bytes, checksum: 4c28f242dbcbf2812965479feec42a9c (MD5)
dc.coverage.spatialColombiaspa
dc.creatorRico Poveda, Carlos Alvaro
dc.date.accessioned2020-09-28T14:18:57Z
dc.date.available2020-09-28T14:18:57Z
dc.date.created2020
dc.identifier.urihttp://hdl.handle.net/20.500.12010/13883
dc.description.abstractEste trabajo tiene como objetivo el estudio, aplicación e implementación de modelos Machine Learning para identificar qué clientes desean cancelar alguna de sus tarjetas de crédito. La industria bancaria utiliza esta tecnología con el fin de obtener predicciones más fiables a la hora de identificar oportunidades de compra, inversión o fraude. Estos modelos se pueden adaptar de forma independiente, por medio del reconocimiento de patrones y algoritmos basados en cálculos matemáticos. Para desarrollar la investigación se implementaron y evaluaron cuatro modelos (LightGBM, XGBoost, Random Forest y Logistic Regression) con el fin de predecir a través de los datos del cliente y sus productos la posibilidad de que cancele sus tarjetas de crédito. Mediante una análisis de la curvas ROC usando las métricas AUC, se llegó a la conclusión que de los modelos seleccionados, el modelo elegido para realizar la predicción fue LightGBM, ya que fue el que tuvo mejor desempeño en los experimentos realizados. De igual forma, se encontró que la variable Score Acierta, una calificación del cliente proveída por la central de riesgos, es la que más discrimina en los modelos predicción.spa
dc.format.extent15 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherUniversidad de Bogotá Jorge Tadeo Lozanospa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.subjectAprendizaje Supervisadospa
dc.subjectRegresión Logísticaspa
dc.subjectAprendizajespa
dc.titleForecasting credit card attrition using machine learning modelsspa
dc.type.localTrabajo de grado de maestríaspa
dc.subject.lembAprendizajespa
dc.subject.lembLogística empresarialspa
dc.subject.lembProcesamiento de datosspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.subject.keywordMachine Learningspa
dc.subject.keywordAttritionspa
dc.identifier.repourlhttp://expeditio.utadeo.edu.cospa
dc.creator.degreeMagíster en Ingeniería y Analítica de Datosspa
dc.publisher.programMaestría en Ingeniería y Analítica de Datosspa
dc.relation.referencesBreiman, L.: Random forests. Machine learning 45(1), 5{32 (2001)spa
dc.relation.referencesCasta~no, H.F., Ram rez, F.O.P.: El modelo log stico: una herramienta estad stica para evaluar el riesgo de cr edito. Revista Ingenier as Universidad de Medell n 4(6), 55{75 (2005)spa
dc.relation.referencesChen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y.: Xgboost: extreme gradient boosting. R package version 0.4-2 pp. 1{4 (2015)spa
dc.relation.referencesChitra, K., Subashini, B.: Customer retention in banking sector using predictive data mining technique. In: ICIT 2011 The 5th International Conference on Information Technology (2011)spa
dc.relation.referencesDfuf, I.A.: An alisis de Sensibilidad Mediante Random Forest. Ph.D. thesis, Universidad Polit ecnica de Madrid (2018)spa
dc.relation.referencesHe, B., Shi, Y., Wan, Q., Zhao, X.: Prediction of customer attrition of commercial banks based on svm model. Procedia Computer Science 31, 423{430 (2014)spa
dc.relation.referencesJes us, E.Z.J., Gerencia, C.: Aplicaci on de algoritmos random forest y xgboost en una base de solicitudes de tarjetas de cr edito application of random forest and xgboost algorithms based on a credit card applications database. IngenIer a InvestIgacI on y tecnolog a 21(3), 1{16 (2020)spa
dc.relation.referencesKe, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y.: Lightgbm: A highly e cient gradient boosting decision tree. In: Advances in neural information processing systems. pp. 3146{3154 (2017)spa
dc.relation.referencesKrishnan, S.: Weight of evidence and information value using python (2018), https://medium.com/@sundarstyles89/ weight-of-evidence-and-information-value-using-python-6f05072e83ebspa
dc.relation.referencesLaMorte, W.W.: Cox proportional hazards regression analysis (2016), https://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Survival/BS704_ Survival6.htmlspa
dc.relation.referencesManrai, L.A., Manrai, A.K.: A eld study of customers' switching behavior for bank services. Journal of retailing and consumer services 14(3), 208{215 (2007)spa
dc.relation.referencesMittal, V., Kamakura, W.A.: Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating e ect of customer characteristics. Journal of marketing research 38(1), 131{142 (2001)spa
dc.relation.referencesVan den Poel, D., Lariviere, B.: Customer attrition analysis for nancial services using proportional hazard models. European journal of operational research 157(1), 196{217 (2004)spa
dc.relation.referencesRez a c, M., Rez a c, F.: How to measure the quality of credit scoring models. Finance a uv er: Czech Journal of Economics and Finance 61(5), 486{507 (2011)spa
dc.relation.referencesTang, L., Thomas, L., Fletcher, M., Pan, J., Marshall, A.: Assessing the impact of derived behavior information on customer attrition in the nancial service industry. European Journal of Operational Research 236(2), 624{633 (2014)spa
dc.relation.referencesVerbeke, W., Martens, D., Mues, C., Baesens, B.: Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert systems with applications 38(3), 2354{2364 (2011)spa
dc.relation.referencesWirth, R., Hipp, J.: Crisp-dm: Towards a standard process model for data mining. In: Proc. of the 4th international conference on the practical applications of knowledge discovery and data mining. pp. 29{39. Springer-Verlag London, UK (2000)spa
dc.relation.referencesXie, Y., Li, X., Ngai, E., Ying, W.: Customer churn prediction using improved balanced random forests. Expert Systems with Applications 36(3), 5445{5449 (2009)spa
dc.relation.referencesZhao, X., Shi, Y., Lee, J., Kim, H.K., Lee, H.: Customer churn prediction based on feature clustering and nonparallel support vector machine. International Journal of Information Technology & Decision Making 13(05), 1013{1027 (2014)spa
dc.description.abstractenglishThe objective of this work is the implementation and evaluation of Machine Learning models to identify which customers want to cancel their credit cards. The banking industry uses this technology to obtain more reliable predictions when identifying opportunities for purchase, investment, or fraud. These models can be adapted independently, by recognizing patterns and algorithms based on mathematical calculations. Four models (LightGBM, XGBoost, Random Forest and Logistic Regression) were implemented and evaluated to predict, using data about customers and products held pertaining to a bank in Colombia, the likelihood of customers cancelling their credit cards. By analysing the ROC curves using the AUC metric, it is concluded that, of the selected models, the model chosen for deployment would be LightGBM, since it was the one that performed best in the experiments conducted. Furthermore, the ``Score Acierta'' variable, a customer rating provided by the Colombian credit rating agency, was found to be the most discriminating in prediction models.spa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record