In today’s ever-changing environment, business success requires a company to be able to anticipate opportunities and effectively understand and respond to risks.
Organizations are feeling pressure on all sides to enhance their risk management practices. External stakeholders—such as regulators, ratings agencies, investors, lenders, and trading partners—often seek data or performance metrics related to an organization’s risk management framework. They may incorporate this information into their assessments of the organization’s resilience and its likely future financial performance. At the same time, various internal stakeholders—such as boards of directors, audit committees, executive management committees, and employees—are exerting greater influence on risk leaders to integrate an understanding of the organization’s myriad risks into key strategic decisions.
Organizations with higher levels of risk management maturity typically work around three key fundamental statistical modeling concepts relating to risk for their organization: understanding the organization’s ability to take risks; understanding the organization’s underlying risk profile through stochastic modeling; and evaluating the efficiency of various risk transfer strategies for the organization. In companies that have successfully incorporated these statistical modeling concepts into organizational decision-making, this approach supports financial objectives and helps with management of volatility in key risk areas. On average, these organizations identify savings of approximately 5 percent of their total cost of risk, or premiums plus retained loss.
The most mature organizations typically incorporate concepts around risk and return into their strategic decision-making at the highest levels. A great example is how organizations continue to evolve the extent of risk analysis they apply to capital investment decisions. The basic concepts of risk and return have been around for many years, but we see organizations with higher levels of risk management maturity having greater awareness of the quantitative modeling techniques available to them to support analysis of risks and returns. Simple, rule-of-thumb approaches were long ago replaced by more complex models. Techniques such as sensitivity and scenario analysis, risk adjusted discount rate, and probability analysis are increasingly used by organizations to support key capital investment decisions. In many cases, organizations use a multiplicity of techniques in a complementary fashion.