Risk estimates are predictions or projections of the likelihood and potential consequences of risks. They are used to inform risk management efforts, such as measuring risk exposure and identifying strategies for reducing or mitigating risks.
There are a variety of methods that organizations can use to estimate risks, including probability analysis, impact analysis, risk assessment tools, risk analysis techniques, and risk management software. These methods can help organizations to understand the potential impacts of risks, to prioritize risks based on their likelihood and potential impact, and to develop strategies for managing and mitigating risks.
Risk estimates are an important element of effective risk management, as they help organizations to better understand and manage the risks that they face. By accurately forecasting the probability and impact of risks, organizations can make more informed decisions and allocate resources more effectively to mitigate or reduce risks.
A single estimate of probability and impact based on historical comparisons and/or the opinions of subject matter experts. For example, a product development team estimates the risk that a product will fail on the market as a 20% chance of a $100,000 loss. The risk exposure calculation is an estimate of the probable cost of a risk. It isn’t an upper bound on risk.
Risk Exposure = 0.2 x 100,000 = $20,000
A single estimate of probability and impact is often overly simplistic as there may be many levels of potential impact, each with a separate probability of occurring. A more accurate risk estimate can often be obtained with a matrix of probabilities and impacts.
A more detailed risk estimate can be represented with a smooth curve that gives you a probability for every possible impact.
Risk estimates that go beyond the educated guesses of subject matter experts to calculate risk probabilities and impacts using formulas or algorithms based on a number of parameters. Such calculations are industry and risk specific.
Reference Class Forecasting
Developing or validating risk estimates using data about historical losses that occurred with comparable strategies, operations or projects. For example, risk estimates for an infrastructure project based on a database of historical infrastructure projects of similar magnitude. If projects in your industry have a 20% failure rate and your risk estimate is 3%, you might be missing something.