Supervised Machine Learning Techniques: An Overview with Applications to Banking

TL;DR

This paper reviews supervised machine learning methods like Random Forests, GBMs, and Neural Networks, focusing on banking applications such as credit risk modeling, and discusses their features, hyper-parameter tuning, and interpretability.

Contribution

It provides a comprehensive overview of supervised ML techniques applied to banking, including recent algorithms, optimization methods, and interpretability considerations.

Findings

Comparison of ML algorithms for credit risk modeling

Insights into hyper-parameter optimization techniques

Discussion on interpretability of ML models in banking

Abstract

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