Bank and corporate managers are demanding a new generation of risk modelling that goes beyond transparency and enhances their ability to see competitive opportunities as well as to recognise the indications of economic change. New tools are available, but in order to make the organisation truly resilient, managers must radically change their approach to risk decision making. They should pursue a proactive and strategic approach, using insight and foresight to identify and mitigate emerging risks.
Risk modelling uses a variety of techniques incorporating internal and external data in order to analyse portfolios, produce forecasts and conduct stress testing and scenario analysis. The fast changing environment continuously requires that bank and corporate managers have quality data and analysis available to pro-actively sense possible risks in the organisation and to respond appropriately in order to mitigate risks and optimise profits.
A risk model is a mathematical representation of a system, commonly incorporating probability distributions. Models use relevant historical data as well as “expert elicitation" from people versed in the topic at hand to understand the probability of a risk event occurring and its potential severity.
Risk management models serve several purposes in the banking environment and especially at Bank Windhoek. A variety of models are required by regulators, which are focused on ensuring that banks have sufficient reserves to allow for possible losses and that banks pro-actively monitor and report risks to central banks. Accounting standards also require a high degree of financial modelling, especially for the calculation of the expected credit losses of the bank. Furthermore, risk modelling allows for the pro-active identification and mitigation of risks in various areas, including credit risk, market risk, liquidity risk and capital risk. When correctly applied, risk models can pre-empt risks and allow for the modelling of different scenarios in order to test the outcomes of decisions made. Risk models are also developed to allow for, not only portfolio level insights, but client level insights in terms of credit grading of clients that allows the effective management of the credit risk in the bank.
Risk management in banking has transformed over the past decade, mainly due to continuous changes in regulations and accounting standards. The regulatory environment is continuously changing with the aim of allowing banks to better manage and mitigate risks. The future of risk management indicates broader and deeper regulatory requirements, necessitating an even better understanding of the banking environment as well as forward-looking views on the banking portfolio. Included in these processes are increased modelling to allow automation of processes and to eliminate human errors.
In line with the changing regulatory environment, technological advancements and changing global environment, there is a requirement for faster and more accurate processes to assess client risk and to respond to client queries. This increases the need for risk models to be integrated into business processes at the core of decision-making.
Advanced analytics and understanding of client data is an increasing trend in banking and organisations globally. This increases the need for analytics that arise from risk models, forecasting and scenario analysis. The changing trends create internal and external customers that want to see the risks, understand them and know what decisions can be made to act on the risks. Modelling and analytics in the risk environment can increasingly add value to an organisation if effectively applied to the bank's portfolios. The modelling of tail-events also become increasingly popular in order to derive model outputs that indicate possible stressed events that could be a risk to the bank. This could allow for the pro-active management of the risk to prevent losses for the organisation.
Risk modelling is an evolving environment in banking and creates a vast network of opportunities for analytics, forecasting, scenario analysis and risk mitigation for the optimal integration of risk management in business and decision-making.