Supervised Machine Learning Techniques: An Overview with Applications to Banking

This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Boosting (Gradient GBM) and Neural Networks (NNs). We begin introduction ML tasks techniques. is followed by description of: i) tree-based ensemble algorithms including RF GBMs, ii) Feedforward NNs, iii) discussion hyper-parameter optimization techniques, iv) machine learning interpretability. paper concludes comparison the features different algorithms. Examples taken from credit risk modeling in banking are used throughout illustrate interpret results