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.

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In the ‘industry 4.0’ era, the phenomenon of digitalization of smart cities has gained increasing prominence, as it has the potential to address the problem of resource optimization and the pressure of demand of the growing urban population. In the past, smart city initiatives may not have created desired impacts as these initiatives were limited in scope, focusing on physical digital integration of the underlying systems of cities and of their citizens. This article examines the success factors that affect the digitalization of smart cities based on secondary research. To achieve smart cities’ core objectives of increasing quality of living, providing efficient and optimal services, thereby making the functioning of the city smart through digitalization, it is essential to look at these critical success factors, namely, sustainability, ecosystems and digital citizen. The article points out important elements such as lack of governance of sustainability, methods and processes to enhance participation of digital citizens and inadequate knowledge about structures and value creation through ecosystems that would need to be addressed while digitalizing smart cities. Given the required policy attention and focus, these factors would be expected to make smart cities sustainable, improve the quality of life of citizens and create new economic opportunities, while digitalizing them.

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