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.
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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.
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.
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.
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.