  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.
