Risk management is a critical function within the financial services industry and the methodologies for assessing and controlling can be complex. To find out more about the challenges of managing risk in African investment portfolios, TradeInvestAfrica spoke to Sunil Rajan, director of S&P Capital IQ EMEA. What are the challenges for risk professionals in African markets? The different maturity levels of the various credit markets in Africa do not lend themselves to a one-size-fits-all risk management strategy. Some are relatively developed - as in South Africa. But most can be considered emerging markets. Each presents unique challenges, but there are common characteristics. For instance, a credit risk professional managing South African holdings will have access to more comprehensive historic default data than one overseeing investments in less-developed sub-Saharan nations. So the challenges in the former, more developed market lie in recovery data (i.e. the losses and recoveries post-default) rather than in hard default data. Of course, there will be more available data than in the other African markets but there may still be insufficient data for risk managers of South African portfolios to develop statistical models. Yet risk managers of sub-Saharan African investment portfolios have more basic difficulties. These markets often lack historical data on default statistics as well as default recovery. Risk management here would focus on risk advisory and development support, with a view to enhancing default data and building more robust probability of default (PD) measures. So lack of data is a problem common to both regions. However, the challenges posed by limited recovery data can be overcome in the use of sophisticated and tailored recovery modelling. Explain the Loss Given Default (LGD) model and how it can benefit investors operating in Africa. Loss Given Default modelling is an effective and robust solution to cases where an insufficient amount of data makes it difficult to build a statistical model. Working in relation to the debt profile of the region of default, LGD models provide an estimation of retained economic value in investment products experiencing stressed scenarios. Ultimately, a grade is produced that represents an expected and estimated debt recovery range. Regulators have actively encouraged financial institutions to adopt systems of this kind, not least because of the practical benefits such extensive risk management affords. The Basel Committee on Banking Supervision has issued a number of recommendations to national regulators - known as the Basel accords - that confer significant advantages in terms of capital reserve allowances, to financial institutions that operate sophisticated risk management systems. What represents “best practice” for credit risk management? To ensure best practice, fund managers must first conduct a full and frank assessment of the nature of their portfolio. Asset-class, size, diversity, exposure to emerging markets, future extension into data-poor environments - these are all aspects to consider. Next, define the methodology and data necessary to improve risk management capabilities. We recommend holistic frameworks that consider all asset classes in combination. This leads to an understanding of how asset classes interrelate, and how they can influence and be influenced by each other. Certainly, signals in the equity market can lead to significant arbitrage opportunities in the corporate bond market and vice versa. Indeed, the relationship between probability of default and market or liquidity risk, and the spread on corporate debt versus a company’s share price can provide actionable insights for both investors and risk managers. How would you advise a company looking to implement or enhance a credit risk assessment process? A recent deal with Standard Bank provides a typical example of our working methods. Their one-off purchase of our recovery data and modelling framework began with bespoke implementation according to their individual requirements. It is also worth noting that Standard Bank – as with our other recovery data and modelling clients – use our capabilities as cogs in a larger risk management machine. Indeed, risk management systems can only be effective if checked and used in combination with others.