We reflect on the good, the bad and the ugly of the fatal accidents occurring on South Africa’s roads. The cost of human lives indisputably equates to ‘the ugly’ and the economic cost of accidents associates with ‘the bad’. ‘The good’ relates to the reduction of both these costs that may result from the entrance of self-driving cars into the South African market as well as awareness campaigns like the Arrive Alive National Road Safety Strategy. The general contribution of this paper is to raise awareness of the effects of accidents, more specifically fatal accidents. Current trends in terms of human factors as well as road and environmental factors involved in the fatal accidents on South African roads are summarised. This paper also serves as a preliminary investigation into possible factors influencing these accidents, which ought to be of interest to a very broad readership, more specifically those focusing on risk analysis, and certainly is of interest to any citizen of South Africa.Significance: Awareness is raised of the effects of fatal accidents on South African roads. Current trends in terms of human factors as well as road and environmental factors on road accidents are reflected upon. The futuristic effect of self-driving cars is explored.

The erratic nature of electricity supply in South Africa combines with other factors continuous to affect household electricity demand, leading to increasing reliance on other fuels. This dependence is characterised by the use of traditional fuels by mostly low-income households, contributing significantly to environmentally harmful emissions. This study assessed the primary determinants of energy fuel choice in selected South African households, to alert policymakers to important energy consumption behavioural tendencies that can inform policies and that can assist sustainable energy growth and reduction of biomass use in households. The investigation was primarily based on energy consumption models and used a quantitative research design. Gauteng and North West were considered for data collection. Households, in general, tended to practice energy stacking. The results suggest policy measures that could promote sustainable energy use by households.

Forensic social work in South Africa is challenging, increasing the likelihood of secondary traumatic stress among its practitioners. Proactive coping strategies are necessary to reduce the impact of secondary traumatic stress on forensic social workers. The aim of this study was to describe the association between the frequency of different coping strategies and the frequency of secondary traumatic stress symptoms in forensic social workers. The study applied a quantitative approach with a cross-sectional descriptive design. An all-inclusive willing participation sampling method was used, focusing on all qualified forensic social workers who graduated from a South African university. The study found that avoidant and emotion-focused coping strategies were linked to an increase in secondary traumatic stress symptoms. Employers and therapists can support forensic social workers by offering techniques that encourage the use of problem-focused coping strategies, aiming to alleviate symptoms of perceived secondary traumatic stress.

The key criteria for making business decisions is profit, so when making credit limit-setting strategy decisions, profitability will be the most important driver. The profitability of a credit limit-setting strategy is dependent on the customer’s utilisation of the limits set by the strategy. This points towards a need to determine the extent to which a limit can be increased before a customer’s utilisation will decline beyond the point of being profitable. This paper sets out to define a Customer Comfort Limit Utilisation (CCLU) measure that can be used to gauge a customer’s level of comfort (in terms of limit utilisation) with a newly proposed limit. The CCLU is defined as a function of the customer’s limit utilisation and credit limit; in simpler terms, it can be viewed as a function of balance growth vs limit growth. The neural network model that predicted utilisation first and then the CCLU based on the resulting utilisation values was selected as the final model. Once the final model was determined, it was then verified whether profitability could be improved by using a CCLU measure as a management tool when making limit-setting strategy decisions. It was found that strategies involving CCLU values could lead to increased profitability since CCLU values near 100 (i.e. the customer is 100% comfortable with the new limit and will utilise to the same extent as the previous one) are associated with higher key performance metrics levels.

Background: This article considers whether South African banks should utilise the credit ratings provided by US-based credit rating agencies when assessing the creditworthiness of corporate borrowers.Aim: A review is conducted of the relevant literature and specifically the methodologies used by the credit rating agencies for ranking corporates in emerging markets.Setting: The three largest international credit rating agencies are Fitch Ratings, Moody’s Investor Services, and Standard and Poor’s. These agencies’ credit ratings cover the global spectrum of corporate, sovereign, financial and other public entities and the securities and obligations they issue. The analytical frameworks used to produce these ratings are referred to as credit rating methodologies.Method: A review of Moody’s ratings for South African corporate entities was undertaken to examine claims of a sovereign ceiling influencing the external ratings obtained by these institutions in emerging markets.Results: Only 14 of the 200 global South African ratings pierced the sovereign ceiling.Conclusion: The study concludes that the use of unmodified external ratings by banks to assess a corporate borrower should be discouraged. High-level suggestions are provided on how the methodologies and data used by the external agencies may rather be used to arrive at more suitable internal ratings.

The landscape of financial credit risk models is changing rapidly. This study takes a brief look into the future of predictive modelling by considering some factors that influence financial credit risk modelling. The first factor is machine learning. As machine learning expands, it becomes necessary to understand how these techniques work and how they can be applied. The second factor is financial crises. Where predictive models view the future as a reflection of the past, financial crises can violate this assumption. This creates a new field of research on how to adjust predictive models to incorporate forward-looking conditions, which include future expected financial crises. The third factor considers the impact of financial technology (Fintech) on the future of predictive modelling. Fintech creates new applications for predictive modelling and therefore broadens the possibilities in the financial predictive modelling field. This changing landscape causes some challenges but also creates a wealth of opportunities. One way of exploiting these opportunities and managing the associated risks is via industry collaboration. Academics should join hands with industry to create industry-focused training and industry-focused research. In summary, this study made three novel contributions to the field of financial credit risk models. Firstly, it conducts an investigation and provides a comprehensive discussion on three factors that contribute to rapid changes in the credit risk predictive models’ landscape. Secondly, it presents a unique discussion of the challenges and opportunities arising from these factors. Lastly, it proposes an innovative solution, specifically collaboration between academic and industry partners, to effectively manage the challenges and take advantage of the opportunities for mutual benefits.