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Machine learning, identified as one of the technologies with important implications for risk management, can enable the building of more accurate risk models by identifying complex, nonlinear patterns within large datasets.The predictive power of these models can grow with every bit of information added, thus enhancing predictive power over time.The research evaluated the risk areas where machine learning has been implemented in the risk types and the specific risk methodology they addressed.
It is expected that machine learning will be applied across multiple areas within a bank’s risk organisation.
Machine learning has also been recommended as an initiative that could help in the transformation of the risk management function at banks.
Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected, measured, reported and managed.
Considerable research in academia and industry has focused on the developments in banking and risk management and the current and emerging challenges.
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.However, there are other sources of credit risk both on and off the balance sheet.Off-balance sheet items include letters of credit unfunded loan commitments, and lines of credit.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.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.is the essential professional and research journal for all those involved in the management of risk at retail and investment banks, investment managers, broker-dealers, hedge funds, exchanges, central banks, financial regulators and depositories, as well as service providers, advisers, researchers and academics.is listed in Cabells' Directories of Publishing Opportunities.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.