Using Big Data to Capture Risk Volatility

By factoring in the Economic Cost of Risk, CFOs can capture the ups and downs of their companies' perils.

Companies have traditionally measured their exposure to risks through total cost of risk (TCOR) calculations. Definitions vary, but TCOR represent the sum of these larger elements: the insurance premiums a corporation spends, the cost of the losses it retains, and other items such as administrative costs, brokerage fees, and taxes and assessments.

The shortcoming of this line of thinking is that it places no value on uncertainty, rather treating losses as a known quantity. And yet the amount of losses at any one company fluctuates unpredictably from year to year. In fact, this uncertainty is the main reason that companies buy insurance and create loss control and mitigation programs.

The importance of measuring uncertainty and volatility has led to the creation of a new measure of risk: the economic cost of risk (ECOR).

ECOR is defined as the sum of:

  • Expected retained losses.
  • Premiums.
  • Other expenses (for example, claims-handling fees and the cost of collateral).
  • Implied risk charge.

Unlike TCOR, ECOR incorporates an implied risk charge (IRC) that evaluates the severity and likelihood of detrimental outcomes and their associated cost. Because no company is perfectly protected against the unexpected, every organization bears an implied charge for their unexpected risk.

Thus, IRC can be quantified for any insurance or mitigation structure. IRC incorporates a company’s capital costs and provides a direct linkage between insurance purchasing decisions and financial performance metrics. It also creates a necessary and more meaningful way for companies to strategically engage between their finance and risk management functions.

The Metric in Practice
Consider the two hypothetical companies (in the charts below) with the following loss history, assuming constant size over the past five years (“Ground-Up Losses” are a measurement of the original losses to a company):

Company A1

Company A

Each of these companies has an average loss of $10 million per year. Traditional TCOR analysis might suggest that there is no difference in the cost of risk for these two companies. But there is much more volatility in Company B’s losses.

Such volatility can lead to unexpected and unpleasant effects on a company’s earnings and performance. Intuitively, it feels as if the cost of risk for Company B should be higher than Company A because of the higher downside risk. The question then becomes: How do you put a value on this volatility?

ECOR measures this additional cost of risk through the IRC, which is computed as the capital at risk (expected losses above average losses) multiplied by the company’s cost of capital. Stochastic modeling is typically used to measure IRC. However, for purposes of simplicity, we can show how IRC for Company A and Company B can be measured based on their historical losses.

Company B1

Company B

For Company A, average losses are $10 million, and there is one year, 2011, with losses above that amount, of $11 million. Total losses above the average are thus $1 million ($11 million – $10 million). There is a 20 percent chance of experiencing losses above the average (one year out of five).  Therefore, expected losses above the average annually are 20 percent multiplied by $1 million, or $200,000.

For Company B, average losses are also $10 million. But there are two years with losses above $10 million, 2009 ($15 million) and 2011 ($18 million). Therefore, total losses above expected are $13 million (the sum of $15 million – $10 million = $5 million, and $18 million -$10 million = $8 million). Average losses above expected are $6.5 million (the average of $5 million and $8 million). There is a 40 percent chance of experiencing losses above the average (two years out of five).  Therefore, expected losses above the average are 40 percent multiplied by $6.5 million, or $2.6 million.

The IRC for Company A is $200,000 multiplied by the company’s cost of capital.  In relation to ECOR, this is a trivial amount, which should be the case when losses are highly predictable.

The IRC for Company B is $2.6 million multiplied by the company’s cost of capital. That becomes a significant cost component of ECOR and, in this example, is more than 10 times higher for Company B than Company A.

That should be the case when losses are highly volatile, particularly when considering that the capital is at risk for the lifetime of the claims rather than only a short period, such as 12 months. By measuring the cost of risk through the lens of ECOR, companies can now place a value on uncertainty.

Claude Yoder is head of Marsh Global Analytics and Dave Heppen is Marsh Global Analytics North American Leader.

2 thoughts on “Using Big Data to Capture Risk Volatility

  1. Interesting view. Recognizing that the focus of the article seems to be the IRC, wouldn’t insurers charge Co. B significantly a higher premium for coverage given the variability of their losses? Aren’t the factors creating the IRC baked into the premium and applying the IRC results in double counting? Or is the IRC considering only losses above the coverage limits, in which case increasing the limits might be a more economic way of financing the risk?

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