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The study investigates the effectiveness of government policies to mitigate the impact of a pandemic. The study adopts the small open economy of Nigeria for the following reasons. First, Nigeria is the largest economy in SSA. Second, Nigeria was also significantly impacted by the COVID-19 pandemic.

The study employed the time-varying structural autoregressive (TVSVAR) model to control for the potential asymmetry in fiscal variables and to control for the shift in the structural shift, following a macroeconomic shock. As a form of robustness, the study also implements the time-varying Granger causality to formally assess the temporal instability of the variable of interest.

The results show that an oil price shock is an important source of macroeconomic instability in Nigeria. Importantly, the results indicate that the effects of fiscal policy are strongly time varying. Specifically, the results show that fiscal policy helps to stabilize the economy, (i.e. they help to reduce inflation and spur output growth) following macroeconomic shock. Further, the Granger test shows that fiscal policy helped to spur growth in Nigeria. The research and policy implications are discussed.

The study accounts for the time-varying effects of fiscal policy.

The study analyzed the moderating role of information and communication technology (ICT) in the financial inclusion–poverty nexus in Nigeria.

The study adopts a battery of econometric techniques such as the generalized method of moments and the fully modified OLS to control for heterogeneity and endogeneity issues in the poverty literature.

The results show that ICT (regardless of the measure of ICT adopted) moderates the impact of financial inclusion on poverty in Nigeria. Specifically, the result shows that ICT strengthens the effectiveness of financial inclusion to reduce poverty. In particular, the results show that in the presence of unanticipated macroeconomic shock, ICT can help to deepen financial inclusion, reduce the negative effects of an unanticipated shock and ameliorate poverty in Nigeria. That is, the vulnerability of the poor in Nigeria to unanticipated economic shocks can be reduced by expanding the use of ICT in the financial sector. The research and policy implications are discussed.

The study accounts for the impact of COVID-19.

The study examined the roles of fiscal and monetary policy in reducing poverty in sub-Saharan Africa (SSA), while accounting for macroeconomic disruptions. In particular, the study examined the complementarity of fiscal and monetary policy to mitigate shocks and reduce poverty in SSA.

The study adopts the fixed effect (within regression) model to account for country-specific characteristics, and a cross-sectional dependence – consistent model to control for the potential cross-sectional in panel data modelling. The study used the dummy variable approach to account for the macroeconomic shocks. The authors assigned 1 to the following years – 2008, 2014 and 2020; and 0 otherwise to take care of the global financial crisis, commodity terms of trade shocks and the COVID-19 pandemic respectively.

The study found that fiscal policy (particularly, government spending on health and education) has the greater capacity to reduce the level of poverty in SSA. The results also indicate that fiscal policy and monetary policy can work in tandem to reduce the negative effects of a pandemic. However, the study found an optimal threshold level of monetary policy beyond which monetary policy reduces the effectiveness of fiscal policy to reduce poverty in SSA. The research and policy implications are discussed.

The study, unlike previous studies, accounts for the impact of macroeconomic shocks in the monetary/fiscal policy and poverty literature.

This paper explores the possibilities for policy coordination in the Southern African Development Community (SADC) as well as real effective exchange rate (REER) stability as a prerequisite towards sensible monetary integration. The underlying hypothesis goes with the assertion that countries meeting optimum currency area conditions face more stable exchange rates.

The quantitative analysis encompasses 12 SADC member states over the period 1995-2012. Correlation matrixes, dynamic pooled mean group (PMG) and mean group (MG) estimators and real effective exchange rate (REER) and real exchange rate (RER) equilibrium and misalignment analysis are carried out to arrive at the conclusions.

The study finds that the structural variables used in the PMG model show that there are common fiscal and monetary policy variables that determine REER/RER in the region. However, the exchange rate equilibrium misalignment analysis reveals that SADC economies are characterised by persistent overvaluation at least in the short term. This calls for further sustained policy coordination in the region.

The findings in this paper have important policy implications for economic stability and for the attempt of policy coordination in SADC region for the proposed monetary integration to proceed.

This study is the first attempt that relates the exchange rate as a policy coordination instrument among SADC economies.

This study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots.

From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints.

The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives.

The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources.

The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.

The purpose of this study is to use a decision support model based on the Analytical Hierarchy Process (AHP) and Pareto analysis (PA) for ranking the impact of different kinds of cybercrime in organisations in the financial sector to support decisions on cybercrime mitigation.

From a structured questionnaire to the staff of 17 licensed banks in South Africa in charge of management, administration and operations, the perceived effect of cybercrime on the organisation’s goals, namely, organisation’s profitability, goodwill, customers’ satisfaction and risk management was derived. The pairwise comparison of the organisation’s goals and identified forms of cybercrime was done using the AHP.

The results obtained indicate that there was a consensus (100% of the answers) that the effect of cybercrime has negatively impacted the organisation’s objectives profitability and goodwill. Also, still 95.23% of the respondents agreed that the effect of cybercrime has negatively impacted the level of customers’ satisfaction, while only 7.15% saw an impact on the organisation’s risk management processes. Using these results in the AHP, analysis delivers a hierarchical order about the relevance of prevalent forms of cybercrime for the organisation´s cybercrime mitigation. The PA further shows the magnitude of the forms of cybercrime relative to each other.

Hence, this study provides a decision support framework for organisational management in the quest to explore the impact of cyber fraud. It can serve as a practical guided approach for the application of AHP analysis for the existing and emerging forms of cybercrime.

The novelty of this study lies in the fact that the combination of the AHP and PA to support solving a multi-criteria decision problem relating to the prevalence of cybercrime has not been sufficiently highlighted by the existing literature.

The study examined the effect of fiscal policy on poverty in sub-Saharan Africa (SSA) while accounting asymmetric (captured by economic downturns) and spillover effects.

The study used a fixed effect (within regression) IV model to account for country-specific characteristics. The study also adopts a cross-sectional and spatial dependence-consistent model to account for the potential cross-sectional and temporal dependence in panel data modeling.

The study discovered that the effect of fiscal policy on poverty is dependent on the state of the economy. Specifically, we find that fiscal policy helps to reduce the level of poverty during an economic downturn, more than at any other time. More specifically, the findings indicate that the fiscal policy lowers the rate of poverty in SSA, following macroeconomic shocks (captured by the COVID-19 epidemic, the Global Financial Crisis, and the commodity terms of trade shocks). Our findings suggest that fiscal policy is an important policy tool to mitigate the effects of macroeconomic shocks in SSA. Further, the findings also demonstrate that there is a spillover effect of poverty in the region. This implies coordinated, constructive actions by the regional governments can help to lessen the detrimental effects of extreme poverty.

The study examined the effectiveness of fiscal policy to reduce poverty in the event of an economic downturn.

The study investigates the link between structural transformation and sustainable development in sub-Saharan Africa.

The study adopts the traditional ordinary least square method and the Driscoll and Kraay covariance matrix estimator to address every form of cross-sectional and temporal dependence in panel data.

The study finds the structural transformation of the SSA economy will engender sustainable development. Specifically, the study finds that knowledge exerts a positive and statistically significant impact on sustainable development in SSA. Similarly, we found that technology (mobile cellular subscription and fixed telephone line subscription) promotes sustainable development. The results also show that all the economic transformation promotes sustainable development in SSA. Further, we also found that economic development and physical capital are important drivers of sustainable development in SSA. However, trade openness does not contribute to sustainable development in SSA. This might be because the combined scale effect in trade outweighs the combined technology and composition effects in SSA. This suggests the technology component in total trade activities in SSA does not promote sustainable development. The study recommends that governments across SSA should invest more in ICT and mobile cellular infrastructure or create an enabling environment that encourages digitization and the development of financial technology in the manufacturing, mining, construction, agriculture and services sectors to enhance green and quality growth for sustainable development in SSA.

The study uncovers the role of structural transformation in promoting sustainable development in SSA.

A functional financial sector is a major driver of economic development. The purpose of this paper is to provide a comprehensive understanding of existing research findings, gaps in knowledge and emerging trends in the field of banking and finance.

By conducting a systematic literature review, a total of 98 peer-reviewed articles whose focus and relevance match with the subject matter were reviewed and synthesised to answer the research questions. Multiple regression was also carried to investigate the relationship amongst the identified probable factors affecting financial inclusions.

The outcome of this study highlighted some factors mitigating the growth of the banking sector in the Sub-Saharan Africa (SSA). These include excessive or stringent regulations, market segmentation, high interest rates, information asymmetry, low credit status and uneven distribution of credit amongst others.

Some of the policy recommendations that could aid the development of the banking sector in SSA include: development and deepening of interbank markets, financial inclusion, improvement of overall market efficiency through redistribution of liquidity within the banking system, improvement of price and encouragement of competition. This study recommends financial inclusion by formulating policies that balances the capital adequacy requirements with the risk of insolvency to ensure credit flows and promotes financial stability via effective operations financial institutions.

This study contributes valuable insights to the understanding of banking and financial regulations in SSA, informing both academic research and policy development in the region.

The purpose of this study is to analyse cyberfraud in the South African banking industry using a multiple regression approach and develop a predictive model for the estimation and prediction of financial losses due to cyberfraud.

To mitigate the occurrence of cyberfraud, this study uses the multiple regression approach to correlate the relationship between financial loss and cyberfraud activities. The cyberfraud activities in South Africa are classified into three, namely, digital banking application, online and mobile banking fraud. Secondary data that captures the rate of cyberfraud occurrences within these three major categories with their resulting financial losses were used for the multiple regression analysis that was carried out in the Statistical Package for Social Science (SPSS, 2022 environment).

The results obtained indicate that the South African financial institutions still incur significant financial losses due to cyberfraud perpetration. The two main independent variables used to estimate the magnitude of financial loss in the South Africa’s banking industry are online (internet) banking fraud (X2) and mobile banking fraud (X3). Furthermore, a multiple regression model equation was developed for the prediction of financial loss as a function of the two independent variables (X2 and X3).

This study adds to the literature on cyberfraud mitigation. The findings may promote the combat against cyberfraud in the South Africa’s financial institutions. It may also assist South Africa’s financial institutions to predict the financial loss that financial institutions can incur over time. It is recommended that South Africa’s financial institutions pay attention to these two key variables and mitigate any associated risks as they are crucial in determining their profitability.

Existing literature indicated significant financial losses to cyberfraud perpetration without establishing any relationship between the magnitude of losses incurred and the prevalent forms of cyberfraud. Thus, the novelty of this study lies in the analysis of cyberfraud in the South African banking industry using a multiple regression approach to link financial losses to the perpetration of the prevalent forms of cyberfraud. It also develops a predictive model for the estimation and projection of financial losses.