Abstract

Machine learning (ML) has revolutionised data analysis over the past decade. Like innumerous other industries heavily reliant on accurate information, banking supervision stands to benefit greatly from this technological advance. The objective of this review is to provide a comprehensive walk-through of how the most common ML techniques have been applied to risk assessment in banking, focusing on a supervisory perspective. We searched Google Scholar, Springer Link, and ScienceDirect databases for articles including the search terms “machine learning” and (“bank” or “banking” or “supervision”). No language, date, or Journal filter was applied. Papers were then screened and selected according to their relevance. The final article base consisted of 41 papers and 2 book chapters, 53% of which were published in the top quartile journals in their field. Results are presented in a timeline according to the publication date and categorised by time slots. Credit risk assessment and stress testing are highlighted topics as well as other risk perspectives, with some references to ML application surveys. The most relevant ML techniques encompass k-nearest neighbours (KNN), support vector machines (SVM), tree-based models, ensembles, boosting techniques, and artificial neural networks (ANN). Recent trends include developing early warning systems (EWS) for bankruptcy and refining stress testing. One limitation of this study is the paucity of contributions using supervisory data, which justifies the need for additional investigation in this field. However, there is increasing evidence that ML techniques can enhance data analysis and decision making in the banking industry.

Highlights

  • Decision support systems had their genesis in the 1960s (Burstein et al 2008)

  • The most relevant machine learning (ML) techniques encompass k-nearest neighbours (KNN), support vector machines (SVM), tree-based models, ensembles, boosting techniques, and artificial neural networks (ANN)

  • Based on the reviewed works from the previous section, the following paragraphs describe how machine learning techniques have been used in the banking sector

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Summary

Introduction

Decision support systems had their genesis in the 1960s (Burstein et al 2008). Perhaps because of the exposure risk and magnitude of revenues generated, the financial sector has been a avid driver for developing these technologies.Predicting how financial institutions will perform and whether they will create value is key for every contender in this field—financial institutions, central banks, consultancy companies, and academia. Decision support systems had their genesis in the 1960s (Burstein et al 2008). Perhaps because of the exposure risk and magnitude of revenues generated, the financial sector has been a avid driver for developing these technologies. Predicting how financial institutions will perform and whether they will create value is key for every contender in this field—financial institutions, central banks, consultancy companies, and academia. The use of new technology and methods to support risk assessment tasks (fin-tech) is a rising trend in this sector (Milian et al 2019). Machine learning (ML) methods and, to some extent, deep learning (DL), have been used for the assessment of credit risk, and more broadly, predicting bank failures. Traditional statistical methods are still commonly used for this purpose

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