This study sought to investigate the impact of AI on digital financial inclusion. Digital financial inclusion is becoming central in the debate on how to ensure that people who are at the lower levels of the pyramid become financially active. Fintech companies are using AI and its various applications to ensure that the goal of digital financial inclusion is realized that is to ensure that low-income earners, the poor, women, youths, small businesses participate in the mainstream financial market. This study used conceptual and documentary analysis of peer-reviewed journals, reports and other authoritative documents on AI and digital financial inclusion to assess the impact of AI on digital financial inclusion. The present study discovered that AI has a strong influence on digital financial inclusion in areas related to risk detection, measurement and management, addressing the problem of information asymmetry, availing customer support and helpdesk through chatbots and fraud detection and cybersecurity. Therefore, it is recommended that financial institutions and non-financial institutions and governments across the world adopt and scale up the use of AI tools and applications as they present benefits in the quest to ensure that the vulnerable groups of people who are not financially active do participate in the formal financial market with minimum challenges and maximum benefits.

Artificial intelligence in the fourth industrial revolution is beginning to live up to its promises of delivering real value necessitated by the availability of relevant data, computational ability, and algorithms. Therefore, this study sought to investigate the influence of artificial intelligence on the attainment of Sustainable Development Goals with a direct focus on poverty reduction, goal one, industry, innovation, and infrastructure development goal 9, in emerging economies. Using content analysis, the result pointed to the fact that artificial intelligence has a strong influence on the attainment of Sustainable Development Goals particularly on poverty reduction, improvement of the certainty and reliability of infrastructure like transport making economic growth and development possible in emerging economies. The results revealed that Artificial intelligence is making poverty reduction possible through improving the collection of poverty-related data through poverty maps, revolutionizing agriculture education and the finance sector through financial inclusion. The study also discovered that AI is also assisting a lot in education, and the financial sector allowing the previously excluded individuals to be able to participate in the mainstream economy. Therefore, it is important that governments in emerging economies need to invest more in the use of AI and increase the research related to it so that the Sustainable Development Goals (SDGs) related to innovation, infrastructure development, poverty reduction are attained.

The study sought to assess the influence of the COVID-19 pandemic in motivating digital transformation in the education sector in South Africa. The study was premised on the fact that learning in South Africa and the rest of the world came to a standstill due to the lockdown necessitated by COVID-19. To assess the impact, the study tracked the rate at which the Fourth Industrial Revolution (4IR) tools were used by various institutions during the COVID-19 lockdown. Data were obtained from secondary sources. The findings are that, in South Africa, during the lockdown, a variety of 4IR tools were unleashed from primary education to higher and tertiary education where educational activities switched to remote (online) learning. These observations reflect that South Africa generally has some pockets of excellence to drive the education sector into the 4IR, which has the potential to increase access. Access to education, particularly at a higher education level, has always been a challenge due to a limited number of spaces available. Much as this pandemic has brought with it massive human suffering across the globe, it has presented an opportunity to assess successes and failures of deployed technologies, costs associated with them, and scaling these technologies to improve access.

In banking and finance, credit risk is among the important topics because the process of issuing a loan requires a lot of attention to assessing the possibilities of getting the loaned money back. At the same time in emerging markets, the underbanked individuals cannot access traditional forms of collateral or identification that is required by financial institutions for them to be granted loans. Using the literature review approach through documentary and conceptual analysis to investigate the impact of machine learning and artificial intelligence in credit risk assessment, this study discovered that artificial intelligence and machine learning have a strong impact on credit risk assessments using alternative data sources such as public data to deal with the problems of information asymmetry, adverse selection, and moral hazard. This allows lenders to do serious credit risk analysis, to assess the behaviour of the customer, and subsequently to verify the ability of the clients to repay the loans, permitting less privileged people to access credit. Therefore, this study recommends that financial institutions such as banks and credit lending institutions invest more in artificial intelligence and machine learning to ensure that financially excluded households can obtain credit.

An increase in consumption and inefficiency, fluctuating trends in demand and supply, and a lack of critical analytics for successful management are just some of the problems that the energy business throughout the world is currently facing. This study set out to assess the potential contributions that AI and ML technologies could make to the expansion of energy production in developing countries, where these issues are more pronounced because of the prevalence of numerous unauthorized connections to the electricity grid, where a large amount of energy is not being measured or paid for. This study primarily aims to address issues that arise due to frequent power outages and widespread lack of access to energy in a wide range of developing countries. Findings suggest that AI and ML have the potential to make major contributions to the fields of predictive turbine maintenance, energy consumption optimization, grid management, energy price prediction, and residential building energy demand and efficiency assessment. A discussion of what has to be done so that developing nations may reap the benefits of artificial intelligence and machine learning in the energy sector concluded the paper.