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 objective of this paper is to develop a methodology to calculate expected credit loss (ECL) using a transparent-modularised approach utilising three components: probability of default (PD), loss given default (LGD) and exposure at default (EAD). The proposed methodology is described by first providing a methodology to calculate the marginal PD, then the methodology for calculating the marginal recovery rates and resulting LGD, and lastly a methodology to calculate the EAD. These three components are combined to calculate the ECL in an empirical style. In markets where sophisticated IFRS9 models are developed, our proposed methodology can be used as in two settings: either as a benchmark to compare newly developed IFRS9 models, or, in markets where limited resources or technological sophistication exists, our methodology can be used to calculate ECL for IFRS9 purposes. This paper includes two such comparative studies to illustrate how our proposed methodology can be used as a benchmark for a newly developed IFRS9 model based on an emerging country’s unsecured and secured retail banking portfolio. This paper is, in essence, a step-by-step implementation guide of the proposed IFRS 9 methodology to calculate ECL as well as the use of such a model as a benchmark.

The International Financial Reporting Standard (IFRS) 9 relates to the recognition of an entity’s financial asset/liability in its financial statement, and includes an expected credit loss (ECL) framework for recognising impairment. The quantification of ECL is often broken down into its three components, namely, the probability of default (PD), loss given default (LGD), and exposure at default (EAD). The IFRS 9 standard requires that the ECL model accommodates the influence of the current and the forecasted macroeconomic conditions on credit loss. This enables a determination of forward-looking estimates on impairments. This paper proposes a methodology based on principal component regression (PCR) to adjust IFRS 9 PD term structures for macroeconomic forecasts. We propose that a credit risk index (CRI) is derived from historic defaults to approximate the default behaviour of the portfolio. PCR is used to model the CRI with the macroeconomic variables as the set of explanatory variables. A novice all-subset variable selection is proposed, incorporating business decisions. We demonstrate the method’s advantages on a real-world banking data set, and compare it to several other techniques. The proposed methodology is on portfolio-level with the recommendation to derive a macroeconomic scalar for each different risk segment of the portfolio. The proposed scalar is intended to adjust loan-level PDs for forward-looking information.

A direct or indirect modelling methodology can be used to predict Loss Given Default (LGD). When using the indirect LGD methodology, two components exist, namely, the loss severity component and the probability component. Commonly used models to predict the loss severity and the probability component are the haircut- and the logistic regression models, respectively. In this article, survival analysis was proposed as an improvement to the more traditional logistic regression method. The mean squared error, bias and variance for the two methodologies were compared and it was shown that the use of survival analysis enhanced the model's predictive power. The proposed LGD methodology (using survival analysis) was applied on two simulated datasets and two retail bank datasets, and according to the results obtained it outperformed the logistic regression LGD methodology. Additional benefits included that the new methodology could allow for censoring as well as predicting probabilities over varying outcome periods.

Background: With the increasing use of complex quantitative models in applications throughout the financial world, model risk has become a major concern. The credit crisis of 2008–2009 provoked added concern about the use of models in finance. Measuring and managing model risk has subsequently come under scrutiny from regulators, supervisors, banks and other financial institutions. Regulatory guidance indicates that meticulous monitoring of all phases of model development and implementation is required to mitigate this risk. Considerable resources must be mobilised for this purpose. The exercise must embrace model development, assembly, implementation, validation and effective governance.Setting: Model validation practices are generally patchy, disparate and sometimes contradictory, and although the Basel Accord and some regulatory authorities have attempted to establish guiding principles, no definite set of global standards exists.Aim: Assessing the available literature for the best validation practices.Methods: This comprehensive literature study provided a background to the complexities of effective model management and focussed on model validation as a component of model risk management.Results: We propose a coherent ‘best practice’ framework for model validation. Scorecard tools are also presented to evaluate if the proposed best practice model validation framework has been adequately assembled and implemented.Conclusion: The proposed best practice model validation framework is designed to assist firms in the construction of an effective, robust and fully compliant model validation programme and comprises three principal elements: model validation governance, policy and process.

Survival analysis is one of the techniques that could be used to predict loss given default (LGD) for regulatory capital (Basel) purposes. When using survival analysis to model LGD, a proposed methodology is the default weighted survival analysis (DWSA) method. This paper is aimed at adapting the DWSA method (used to model Basel LGD) to estimate the LGD for International Financial Reporting Standard (IFRS) 9 impairment requirements. The DWSA methodology allows for over recoveries, default weighting and negative cashflows. For IFRS 9, this methodology should be adapted, as the estimated LGD is a function of in the expected credit losses (ECL). Our proposed IFRS 9 LGD methodology makes use of survival analysis to estimate the LGD. The Cox proportional hazards model allows for a baseline survival curve to be adjusted to produce survival curves for different segments of the portfolio. The forward-looking LGD values are adjusted for different macro-economic scenarios and the ECL is calculated for each scenario. These ECL values are probability weighted to produce a final ECL estimate. We illustrate our proposed IFRS 9 LGD methodology and ECL estimation on a dataset from a retail portfolio of a South African bank.