  Predictive power and generalizability of models depend on the quality of
features selected in the model. Machine learning (ML) models in banks consider
a large number of features which are often correlated or dependent.
Incorporation of these features may hinder model stability and prior feature
screening can improve long term performance of the models. A Markov boundary
(MB) of features is the minimum set of features that guarantee that other
potential predictors do not affect the target given the boundary while ensuring
maximal predictive accuracy. Identifying the Markov boundary is straightforward
under assumptions of Gaussianity on the features and linear relationships
between them. This paper outlines common problems associated with identifying
the Markov boundary in structured data when relationships are non-linear, and
predictors are of mixed data type. We have proposed a multi-group
forward-backward selection strategy that not only handles the continuous
features but addresses some of the issues with MB identification in a mixed
data setup and demonstrated its capabilities on simulated and real datasets.
