Supervised Machine Learning Techniques: An Overview with Applications to Banking

This article provides an overview of supervised machine learning (ML) with a focus on applications in banking. The supervised ML techniques covered include bagging (random forest), boosting (gradient boosting machine) and neural networks. We begin with an introduction to ML tasks and techniques. This is followed by a description of tree‐based ensemble algorithms, including bagging with random forest and boosting with gradient boosting machines, as well as feedforward neural networks. We then provide an extensive discussion of hyper‐parameter optimisation techniques. Interpretability of ML results is an important topic in banking and other regulated industries, and it is also covered in some depth. The paper concludes with a comparison of the features of different ML algorithms and a discussion of their use in practice. An application from credit risk modelling in banking is used throughout the paper to illustrate the techniques and interpret the results of the algorithms.