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Carry out a systematic literature review on the application of machine learning in Banking risk management building on the work of Leo et al., 2019 (attached). · 12-13 pages (excluding title page and reference pages) · References must not be older than 2 years. · APA 7.0 Carry out a systematic literature review on the application of machine learning in Banking risk management building on the work of Leo et al., 2019 (attached). · 1 2 - 1 3 pages (excluding title page and reference pages) · References must not be older t han 2 years. · APA 7.0 Carry out a systematic literature review on the application of machine learning in Banking risk management building on the work of Leo et al., 2019 (attached). 12-13 pages (excluding title page and reference pages) References must not be older than 2 years. APA 7.0 risks Article Machine Learning in Banking Risk Management: A Literature Review Martin Leo * , Suneel Sharma and K. Maddulety SP Jain School of Global Management, Sydney 2127, Australia; [email protected] (S.S.); [email protected] (K.M.) * Correspondence: [email protected]; Tel.: +65-9028-9209 Received: 25 January 2019; Accepted: 27 February 2019; Published: 5 March 2019 ���������� ������� Abstract: There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. 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. This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas or problems in risk management that have been inadequately explored and are potential areas for further research. The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn’t appear commensurate with the current industry level of focus on both risk management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems. Keywords: risk management; bank; machine learning; credit scoring; fraud 1. Introduction Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus on how risks are being detected, measured, reported and managed. Considerable research (Van Liebergen 2017; Deloitte University Press 2017; Helbekkmo et al. 2013; MetricStream 2018; Oliver Wyman 2017), both in academia and indus ...
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Stress testing banks
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