  Credit scoring models, which are among the most potent risk management tools
that banks and financial institutes rely on, have been a popular subject for
research in the past few decades. Accordingly, many approaches have been
developed to address the challenges in classifying loan applicants and improve
and facilitate decision-making. The imbalanced nature of credit scoring
datasets, as well as the heterogeneous nature of features in credit scoring
datasets, pose difficulties in developing and implementing effective credit
scoring models, targeting the generalization power of classification models on
unseen data. In this paper, we propose the Bagging Supervised Autoencoder
Classifier (BSAC) that mainly leverages the superior performance of the
Supervised Autoencoder, which learns low-dimensional embeddings of the input
data exclusively with regards to the ultimate classification task of credit
scoring, based on the principles of multi-task learning. BSAC also addresses
the data imbalance problem by employing a variant of the Bagging process based
on the undersampling of the majority class. The obtained results from our
experiments on the benchmark and real-life credit scoring datasets illustrate
the robustness and effectiveness of the Bagging Supervised Autoencoder
Classifier in the classification of loan applicants that can be regarded as a
positive development in credit scoring models.
