This paper surveys the extant literature on machine learning, artificial intelligence, and deep learning mechanisms within the financial sphere using bibliometric methods. We considered the conceptual and social structure of publications in ML, AI, and DL in finance to better understand the research’s status, development, and growth. The study finds an upsurge in publication trends within this research arena, with a bit of concentration around the financial domain. The institutional contributions from USA and China constitute much of the literature on applying ML and AI in finance. Our analysis identifies emerging research themes, with the most futuristic being ESG scoring using ML and AI. However, we find there is a lack of empirical academic research with a critical appraisal of these algorithmic-based advanced automated financial technologies. There are severe pitfalls in the prediction process using ML and AI due to algorithmic biases, mostly in the areas of insurance, credit scoring and mortgages. Thus, this study indicates the next evolution of ML and DL archetypes in the economic sphere and the need for a strategic turnaround in academics regarding these forces of disruption and innovation that are shaping the future of finance.

The coronavirus (COVID-19) outbreak has acutely affected trade, investment, growth and employment around the world and has instigated a global economic slowdown. Estimates of labor income losses (before taking into account income support measures) suggest a global decline of 10.7 per cent during the first three quarters of 2020 compared with the corresponding period in 2019, which amounts to US$3.5 trillion or 5.5 per cent of global gross domestic product (GDP) for the first three quarters of 2019. Likewise, as with any crisis, the condition has become even worse in developing countries, where the stability or growth for start-ups and MSME’s has been significantly endangered. Every business or enterprises that drove to closure leads to multiple stories of unemployment, economic and social dislocation as well staggering uncertainty. It remains paramount that governments, in partnership with other public and private institutions, associations and bodies and various other stakeholders bestow their support to drive competitiveness of micro, small and medium enterprises (MSME’s) with a particular focus on those small businesses in developing countries. In many countries, this interference has led to immediate and innovative approaches and models and also helped deploy resources and put up a timely and comprehensive response to the novel coronavirus disease. This study resorts to various MSME’s in developing countries confronting and combating the difficult time caused by COVID-19 pandemic which can help monitor the evolving business climate to design workable and enforceable policy support for MSME development.

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