The 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.