Machine learning (ML) has revolutionised data analysis over the past decade. Like in-numerous 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 compre-hensive 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 neigh-bours (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.
- Machine learning
- Risk assessment