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.

Mergers and acquisitions are an important strategic option for a corporation to drive higher growth and profitability. Despite significant investments being made in mergers and acquisitions, past experiences indicate that many mergers and acquisitions fail. The failure is often attributed to employees’ resistance to change. Factors, such as organisation change, culture, leadership, trust, and communication, among others, lead to employees’ resistance to change. This issue has a significant impact on service organisations since human capital is their key source of competitive advantage. The purpose of this study is to determine factors influencing employees’ resistance to change during the post-merger integration. This paper synthesises findings from a systemised literature review of ‘peer-reviewed’ research papers, published from November 2020 to November 2023, in ProQuest and EBSCOhost databases with keywords ‘employees’ resistance to change’ and ‘post-merger integration’. The findings establish strategies managers could adopt such as creating a continuous learning environment, recognition of the existence of subcultures within a culture, greater involvement of middle managers in the integration phase, devising an appropriate reward plan for early adopters, and focusing on the pace of changes. The study outcomes will help organisations in designing effective post-merger integration programmes leading to efficient use of capital.

Organization culture, internal resources and tools, industry, eco–techno environment and socio-political environment factors are combined to build a model to evaluate their impact on the effectiveness of strategic investment decisions. Empirical data collected from decisions makers in the Indian power sector was used for the study, and it was observed that all latent factors have a positive effect on the effectiveness of investment decisions at 90% confidence level. Though the internal resources and tools and socio-political environment do not have a positive impact on the effectiveness of investment decisions at 95% confidence level based on interaction effects, it may be argued that the socio-political environment strengthens a positive relationship between internal resources and tools; and the effectiveness of strategic investment decisions. There is no significant difference except for the effect of socio-political environment factors and internal resources and tools.