In today’s rapidly evolving and increasingly uncertain business environment, risk analytics are fundamental to empowering corporate decision-making throughout the risk lifecycle. At each stage—understanding, measuring and ultimately reducing the cost of risk—analytics enable risk managers to obtain a data-driven, forward-looking view of risk issues. This better equips management to make strategic decisions that are objectively aligned with the organization’s performance goals. The ability to tap into vast quantities of data to form business insights is spurring companies to generate new efficiencies, create new products, enter new markets and disrupt traditional approaches.
Key to this view of risk is a new metric: the economic cost of risk (ECOR). This is a more comprehensive measurement than traditional risk assessment metrics and is better suited to help organizations manage risk decisions today. Since the amount of losses at any one company fluctuates from year to year, ECOR incorporates an understanding of the potential volatility of risks and the uncertainty an organization may face, which is crucial in making strategic capital decisions to manage risk and ensure growth.
Like total cost of risk (TCOR) calculations, ECOR incorporates the sum of expected retained losses, insurance premiums, and other expenses such as administrative costs, fees and taxes. ECOR adds a new metric, however: the implied risk charge. By assessing the severity and likelihood of detrimental outcomes and their associated cost, the implied risk charge places a value on volatility for each company. Even the best-prepared can face unforeseen events, so every organization bears an implied charge for the unexpected.
Through the ECOR metric, management can better understand how key factors such as volatility and cost of capital influence the expected pricing of risk mitigation. Comparing a company’s financial profile against its potential risk exposure creates a tailored view of the enterprise’s risk-financing options.
Applying Economic Cost of Risk
In practical application, organizations can use the detailed analysis of the insurance markets that is provided by ECOR to potentially reduce premium costs and improve return on investment. Key to calculating and applying ECOR insights, the implied risk charge can be quantified for any insurance or mitigation structure. This implied risk charge incorporates a company’s capital costs and values its capital at risk to provide a direct link between insurance purchasing decisions and financial performance metrics. It also creates a necessary and more meaningful way for companies to strategically engage their finance and risk management functions.
For example, two companies have total claims of $30 million in a five-year period, each with an average of $6 million in annual claims. But Company A experienced claims of up to $10 million one year, and as little as $1 million another year, with two years surpassing its average claims total. In this five-year span, there is a 40% chance Company A’s total losses will surpass its average. If we multiply the total losses above average during those two years by 40%, we get a better understanding of Company A’s implied risk charge. If Company A’s insurance program did not account for the volatility of its loss history, then claims of up to $10 million likely impact the company’s financial position or key performance metrics in that year.
By contrast, Company B experienced steady levels of total claims during this five-year period, with only one year exceeding its $6 million average claims total, when it registered $7 million in claims. In this time period, therefore, there is a 20% chance Company B’s total losses will surpass its average. Multiplying the 20% risk of surpassing its claims average by the $1 million above its average calculates Company B’s implied risk charge.
In addition to the companies’ historical losses, which drive an implied risk charge, the modeling exercise to support ECOR incorporates a future view of losses that may occur. Blending historical losses with other data creates a more robust and future-facing risk profile. This provides companies with a deeper look into loss potential and the best ways to mitigate those losses.
While this is a simplified process compared to the predictive modeling and analytics used in actual ECOR calculations, it is clear from this scenario that Company A experiences greater volatility than Company B and will likely require a different insurance program to reflect this uncertainty. This understanding of implied risk and volatility levels would not have been possible through a traditional measurement of average losses. ECOR enables organizations to take a holistic view of risk exposures and empowers better-informed capital allocation decisions based on these risk insights.
Optimizing Risk Financing
In an environment that places extraordinary pressure on business results every quarter, maintaining levels of risk protection while gaining important capital is invaluable. Detailed analysis of ECOR can help a company improve its risk financing and potentially see significant savings on insurance premiums. With insurance markets pricing programs based on inherent volatility levels, accounting for that volatility provides a direct link between insurance purchasing decisions and financial performance metrics.
For example, a Fortune 500 retail organization sought a more objective, data-driven evaluation of its insurance program. The company wanted to know more than how other retailers set up their programs and to obtain a better understanding of its specific risk exposure that reflected changes in expected losses, volatility and premium. Through an ECOR study of its loss history, the company identified data-driven price points and specific changes in its insurance program, which resulted in more than $1 million in savings and a 50% improvement in program efficiency.
In another situation, the board of directors of a global financial institution asked its risk management team to evaluate the adequacy of its D&O programs. The board rejected a standard benchmarking comparison to its peer group as insufficient. By using data analytics and performing an ECOR study, the risk management team ran simulations for various scenarios that looked at the organization’s loss history coupled with relevant global claims and loss data. ECOR analysis enabled a more effective conversation with the board and the selection and approval of a more efficient insurance program.
By using economic cost of risk calculations, a company can gain a clearer picture of its unique risk profile, volatility of risks and total loss potential, and insight into the pricing of its risk transfer programs. This helps management teams strike the right balance between risk-taking and growth, enabling corporations to more confidently deploy capital and achieve success.