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Bootstrap aggregation, known as bagging, is one of the most popular ensemble methods used in machine learning (ML). An ensemble method is a ML method that combines multiple hypotheses to form a single hypothesis used for prediction. A bagging algorithm combines multiple classifiers modeled on different sub-samples of the same data set to build one large classifier. Banks, and their retail banking activities, are nowadays using the power of ML algorithms, including decision trees and random forests, to optimize their processes. However, banks have to comply with regulators and governance and, hence, delivering effective ML solutions is a challenging task. It starts with the bank's validation and governance department, followed by the deployment of the solution in a production environment up to the external validation of the national financial regulator. Each proposed ML model has to be validated and clear rules for every algorithm-based decision must be justified. In this context, we propose XtracTree, an algorithm capable of efficiently converting an ML bagging classifier, such as a random forest, into simple "if-then" rules satisfying the requirements of model validation. We use a public loan data set from Kaggle to illustrate the usefulness of our approach. Our experiments demonstrate that using XtracTree, one can convert an ML model into a rule-based algorithm, leading to easier model validation by national financial regulators and the bank's validation department. The proposed approach allowed our banking institution to reduce up to 50% the time of delivery of our AI solutions to the end-user.