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The paper examines the possible existence of systematic performance differences between Broad based Black Economic Empowerment (B-BBEE) affiliated and non-B-BBEE affiliated mutual fund firms in South Africa and see whether the indigenisation laws affect firm performance directly through their effects on firm behaviour.

The authors’ baseline regression is a model features that varies between the observed groups in Fama-MacBeth regressions. To address the issue of how B-BBEE laws affect mutual funds' performance, the study follows Golec (1988, p. 77) in calculating mutual fund returns and follows Carhart's (1997) four-factor regression model.

The paper's results also cannot confirm with statistical significance the expectation motivated by theory that B-BBEE laws influence firm performance negatively, thus, predicting a block for foreign investment. The authors’ much longer sample period (from 2004 to 2016) does not lead to significant other results than a prior study published only shortly after the B-BBEE laws coming into force. However, this study’s results could not confirm that these laws have effects on firm performance.

The authors chose all the 3,320 B-BBEE-affiliated mutual fund firms and 3,329 non-B-BBEE-affiliated ones in the Morningstar database that had complete data for the period 2004– 2016.

The study's results cannot confirm with statistical significance the expectation motivated by theory that B-BBEE laws influence firm performance negatively, thus, predicting a block for foreign investment.

B-BBEE laws have been topical in the South African mutual fund industry. The unit trust industry in South Africa started with the establishment of the Sage Fund in 1965 in order to cater for the normal investors' needs for an easy product that starts with low investment amounts, but offers professional assets management and wide risk diversification across an extensive shares portfolio, that can be liquidated at short notice.

The purpose of this study is to examine the level of effectiveness of the anti-fraud technologies employed by the South African banking industry for cyberfraud mitigation.

This research employed a qualitative research design involving a purposive sampling method. Primary data was collected from the key organisational staff across the 17 licensed commercial banks in South Africa via the use of structured questionnaires. In particular, these were experts involved in combating fraud and taking managerial decisions regarding the use of anti-fraud technologies for cyberfraud mitigation. Non-parametric statistical analyses were carried out from the responses obtained.

The results obtained indicated that the combination of internal and external anti-fraud technologies such as filtering software, firewalls, encryption, continuous auditing, discovery sampling, virus protection, financial ratios, digital analysis and data mining may have a positive effect on cyberfraud mitigation. These technologies are employed mostly to ensure effective internal control systems capable of minimising cyberfraud. In addition, the anti-fraud technologies employed in the South African banking industry may also be effective in the mitigation of cyberfraud, although significant cases of cyberattacks were reported by the respondents.

The study recommends investment in more digital and emerging technologies and the development of human capacities to effectively deploy them in the combat against cybercrime.

The novelty of this study lies in the identification of the type of anti-fraud technologies/software employed by the South African banking industry and their level of effectiveness or success rate.

The purpose of this paper is to review the effect of cybercrime in the banking sector.

This study uses a survey of literature and the balanced scorecard (BSC) to analyse the effect of cybercrime on the banking sector.

The literature reviewed confirms an increasing wave of cybercrime that has impacted negatively on the good will and economic growth of financial institutions, indirectly through loss of trust in the digital infrastructure or directly through fraud and extortion in both developing and developed countries.

This study is limited to the application of BSC to analyse the effect of cybercrime in the banking sector only.

To avert on going massive losses owing to cybercrime, the authors quest for development of an alert system that can create the awareness of both the banks and the customers by effectively implementing and integrating big data technology into their system to mitigate the negative impacts of cybercrime.

The novelty of this study lies in the fact that this study uses the BSC for the analysis of cybercrime in the banking sector, a problem that has not been sufficiently highlighted in the existing literature.

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 purpose of this study is to assess the impact of cyberfraud in the South African banks with the aim to provide recommendations to effectively mitigate it.

The study uses a qualitative approach involving the use of structured questionnaires. The questionnaires were made available to the staff of 17 licensed banks in South Africa who deal with management, operation, administration and banking services. Two hypotheses were formulated and non-parametric statistical analyses involving the use of Chi-square test, Fischer’s Exact test and Spearman’s correlation were carried out. The two hypotheses formulated were tested to draw a conclusion.

The results obtained indicate that the impact of cyberfraud in the South African banking industry is highly significant and has affected the reputation of some of the banks. This calls for the need to review the diverse ways of curbing cyberfraud to lessen their impact and that of associated fraud risks on the banking operation.

This study provides an analysis on the relationship cyberfraud occurrences and the reputation of South African banks. The implementation of the recommendations may reinforce the existing security measures in the fight against cyberfraud.

The novelty of this study lies in the fact that the assessment of the impact of cyberfraud on the banking industry in South Africa has not been sufficiently highlighted by the existing literature.

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 develop an innovative approach of combating economic crime using the forensic accounting techniques.

The approach considered the identification of the effective forensic accounting techniques from the available literature and also explored the anti-economic crime policy, capable of assisting in the combating of economic crime. This brought about the development of two conceptual models, which incorporate all the requirements for the implementation of forensic accounting and the integration of forensic accounting technique into the organizational control system for effective fraud mitigation.

The analysis of the literature review indicated that one of the drawbacks, which has continue to mitigate the implementation of forensic accounting as a tool for combating fraud is lack of a suitable framework. This was the major focal point of this work, which produced two simplified conceptual models suitable for effective fraud mitigation.

This study is limited to the development of conceptual models for fraud mitigation only.

The simplified model can easily be adopted into the structure of an organization to provide a sustainable solution to mitigate fraud occurrences.

The novelty of this study lies in the development of two simplified conceptual models. The first model addressed the incorporation of forensic accounting into the organization structure while the second captured the detailed investigation and comprehensive data analysis processes of uncovering fraud. The development of conceptual models with all these peculiarities for fraud mitigation has not been widely reported by the existing literature.