Risk-analytics techniques make it possible to measure, quantify and even predict risks with more certainty.
In the past, companies relied heavily on the leaders at the business-unit level to monitor, assess and report risks to their senior management teams. In today’s world of multiple data sources, however, it becomes virtually impossible to manually search through large amounts of data and gather the critical risk information needed for an enterprise-level risk perspective. Such a view, spanning the totality of a company’s risks and spanning many different parts of a corporation, is impossible without using risk analytics.
Risk analytics sets a baseline for measuring risk across the organization by pulling together many different types of risk data into one definitive system. It gives a company’s decision-makers a clear method in identifying, assessing, understanding and managing the company’s risks.
In general, a risk-analysis report can be either quantitative or qualitative. In quantitative risk analysis, an attempt is made to numerically determine the probabilities of various adverse events and the likely extent of the losses if a particular risk event takes place.
Qualitative risk analysis, which is used more often, does not involve numerical probabilities or predictions of loss. Instead, the qualitative method involves defining the various threats, determining the extent of vulnerabilities and devising countermeasures should a risk event occur.
Almost all sorts of large businesses require a minimum sort of risk analysis. For example, commercial banks need to properly hedge foreign exchange exposure of oversees loans, while large department stores must factor in the possibility of reduced revenues caused a global recession. Risk analysis allows professionals to identify and mitigate risks, but not avoid them completely. Often, it includes mathematical and statistical software programs.
How can you use risk analytics to anticipate and avoid risks, as well as take smart risks to drive value in your company?
Traditional analytics can be instrumental when it comes to better understanding past events or risks that occur with a high degree of frequency. But for forward-looking “what-if” scenarios and strategic risks, risk modeling can deliver valuable insights.
What’s the difference between risk analytics and risk modeling? The type of data they use. Risk analytics can give organizations visibility into many kinds of systemic risks, from credit risk and market risk to operational, reputational and cyber risk. It can help leaders deploy capital and manage their supply chains at a level that matches their risk tolerance. For organizations exposed to significant regulatory risk, it can be an important tool for helping to achieve compliance. And that’s just the start.
Risk modeling organizes bits and pieces of information drawn from a wide range of similar scenarios that have already played out to create a big-picture view of scenarios that are likely to occur in the future. This can be particularly helpful when weighing strategy-level risks that may shape the future of an organization. The more abstract the risks, the more modeling may be of use.