Other products, activities, and services that expose a bank to credit risk are credit derivatives, foreign exchange, and cash management services.
There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored.
Credit risk arises from the potential that a borrower or counterparty will fail to perform on an obligation.
For most banks, loans are the largest and most obvious source of credit risk.
Considerable research (Van Liebergen 2017; Deloitte University Press 2017; Helbekkmo et al.
2013; Metric Stream 2018; Oliver Wyman 2017), both in academia and industry, has focused on the developments in banking and risk management and the current and emerging challenges.The paper seeks to study the extent to which machine learning, which has been highlighted as an emergent business enabler, has been researched in the context of risk management within the banking industry and, subsequently, to identify potential areas for further research.The aim of this review paper is to assess, analyse and evaluate machine-learning techniques that have been applied to banking risk management, and to identify areas or problems in risk management that have been inadequately explored and make suggestions for further research.Section 5 summarises the general findings from the study.The paper concludes by listing additional areas or problems in banking risk management where the application of machine learning can be further researched.Section 2.1 provides an overview of risk management at banks, the key risk types and risk management tools and methodologies.Section 2.2 gives a quick introduction to machine learning and its use.In tandem, there has been a growing influence of machine learning in business applications, with many solutions already implemented and many more being explored.Mc Kinsey & Co highlighted that risk functions in banks, by 2025, would need to be fundamentally different from what they are today.To determine the risks specific to banks, as an alternate to leveraging on existing literature, this paper provides a taxonomy of risks that is developed based on a review of bank annual reports.An analysis of the available literature was carried out to evaluate the areas of banking risk management where machine-learning techniques have been researched.