The Rigg Darlington Group B2B Newsletter Vol. 44, Issue 4
Marsh Analytics - CFO com
1. DO THE MATH BEFORE YOU
BUY INSURANCE
B Y C L A U D E Y O D E R A N D D A V E H E P P E N , C O N T R I B U T O R S
USING BIG DATA TO CAPTURE
RISK VOLATILITY
B Y C L A U D E Y O D E R A N D D A V E H E P P E N , C O N T R I B U T O R S
Claude Yoder is head of Marsh Global Analytics and Dave Heppen leads the Marsh Global
Analytics North American unit.
2. When it comes to deciding how
much property-casualty insurance to buy for their
companies, CFOs and corporate risk managers face
an essential dilemma. On one hand, they don’t want to
buy more insurance than their companies need because
the cost depletes capital that could be used for other
business purposes. On the other hand, if they buy too
little insurance, they could put their company’s balance
sheet at risk if a significant loss occurs.
Difficult as deciding how much insurance to buy
is, it can be even harder when a company has little
or no history of large losses on which to base such
decisions. The use of analytics can help executives make
more informed decisions about how much insurance
coverage is appropriate to buy during their spring
renewal, however.
Although it may be tempting for CFOs and/or risk
managers who have never experienced a large loss at
their firms to discount the possibility of one occurring
in the future, the exposure still exists. Just as insurers
must price the coverage to match their exposure to risk
to succeed in the long term, executives should consider
the possibility of a large event when evaluating the
appropriate amounts of coverage to buy from insurers.
One need look no further than the aftereffects of
recent large retail data breaches and catastrophes like
Superstorm Sandy to know why.
Quantifying a Big Exposure
Quantifying a company’s exposure to large losses
involves two steps: estimating the likelihood of a large-
scale event occurring, and estimating the cost of such
an event if it does occur.
For a company with little to no loss history, it’s useful
to consult large losses experienced by other companies
in the same industry. That requires a robust source of
industry loss data. A threshold representing a large event
needs to be selected for this exercise, such as all industry
DO THE MATH BEFORE YOU BUY INSURANCE
Deciding how much insurance to buy can be tough when a company has little or
no history of large losses on which to base such decisions.
B Y C L A U D E Y O D E R A N D D A V E H E P P E N , C O N T R I B U T O R S
cfo.com
loss events above $10 million.
By considering the number of such events across
the industry (on an annual basis), in combination with
the company’s size relative to the size of the industry,
one can estimate the company’s likelihood of a large
event occurring. These initial estimates can be further
refined based on company-specific considerations that
include loss control measures, nature of exposures and
business models.
Using that data, analytics can then help provide
a reasonable basis for estimating the potential costs
associated with such an event.
It’s important to recognize, however, that although
industry losses serve as a useful guide, they’re not a
definitive statement of loss potential. For example, if
the largest industry event is $100 million, you shouldn’t
assume that this is the maximum loss the company
could experience. Instead, CFOs and their staffs should
consider a range of potential loss outcomes if a large
loss occurs, over and above the range suggested by the
individual losses.
One way to accomplish this is through loss
distributions. Each individual large loss in a data set
can be viewed as a result drawn at random from an
underlying loss distribution. By fitting a curve to its
losses,acompanycanestimatethepotentialdistribution
of costs above and beyond what has already been seen
from the available data.
Consider the following example: Company XYZ,
which has a 1 percent market share of its industry, is
beginning its insurance-renewal discussions. Let’s say
the company, which has never experienced a loss above
$10 million, wants to figure out if $75 million in general
liability insurance is the best amount for it to buy.
Upon further investigation, the finance
department learns that 50 general liability losses of
more than $10 million have occurred in the industry
over the last five years. Insured losses from those
4. Companies have traditionally
measured their exposure to risks through total cost
of risk (TCOR) calculations. Definitions vary, but
TCOR represents 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 perfor-
mance metrics. It also creates a necessary and more
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.
B Y C L A U D E Y O D E R A N D D A V E H E P P E N , C O N T R I B U T O R S
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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):
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.
Suchvolatilitycanleadtounexpectedandunpleasant
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.
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 theCompany A
