Calculated outcomes

The future is filled with both uncertainties and promises. With its in-house expertise, RSI can project potential scenarios, comprehensively outlining the challenges and the rewards that lie ahead. Armed with such foresight, institutions can navigate their journey with increased confidence, always ready to safeguard assets and seize the valuable growth opportunities that arise.

Developing calculated outcomes generally involves a series of steps that include identification, quantification, and analysis, leading to probabilistic or deterministic results. These results can help in decision-making by allowing organizations to prioritize actions and allocate resources.

The first step is the identification phase, where all potential risks within the predetermined scope are listed. This stage lays the groundwork for further calculations and often uses methods like interviews, surveys, and document analysis to identify risks.

Next comes the quantification of risks. Two main parameters usually used for this are the likelihood (probability) and the impact (consequence) of each risk. Several techniques can be employed for quantification, including statistical methods, expert judgment, or simulation models like Monte Carlo analysis. In some cases, historical data, if available and applicable, can provide a basis for these calculations.

The third step involves synthesizing these quantified factors into calculated outcomes. A risk matrix is one common way to do this. Risks are plotted on a grid, with the likelihood on one axis and impact on the other. Alternatively, more advanced methods like Value at Risk (VaR) for financial risks or Quantitative Risk Assessment (QRA) for operational risks might be used. These methodologies produce a calculated value that represents the potential risk outcome, often in financial terms or other relevant metrics like time, quality, or safety measures.

Another RSI approach is to use probabilistic risk assessment models, which use simulations to predict a range of outcomes, offering a more dynamic view of risk that includes different scenarios and their probabilities. For example, Monte Carlo simulations can model the range and likelihood of potential financial losses based on varying input parameters, allowing for more nuanced decision-making.

Once calculated outcomes are developed, they often require validation to ensure they are as accurate and realistic as possible. This might involve sensitivity analyses to understand how small changes in assumptions can affect outcomes, or it may require external expert reviews.

Lastly, as part of our managing risks practice, these calculated outcomes can serve as inputs to the risk mitigation and management process, where strategies are developed to deal with the identified and calculated risks.

In summary, developing calculated outcomes for understanding risk involves a structured approach that starts with identification, moves through quantification, and culminates in analytical methods to produce outcomes that inform decision-making. These calculated outcomes are then subject to validation and finally used for planning risk mitigation strategies.