Over the past two decades, the use of computer modeling to estimate potential future catastrophe losses has become standard practice among insurers and reinsurers. Today, modeling technology is increasingly being used by the corporate risk manager to help assess risk and develop strategies to manage it.
Risk management strategies may include transferring the exposure to another party, accepting some or all of the consequences of a particular risk, or mitigating its effects. One thing that should be at the forefront for all corporate risk managers, however, is the need to account and plan for losses resulting from catastrophes.
Whether natural or man-made, catastrophes are unique in their ability to severely and immediately impact a company's profitability. Hurricanes, earthquakes, tornadoes and terrorism can destroy property, interrupt business, injure workers and throw an otherwise profitable company into turmoil.
While catastrophes are generally infrequent, their financial impact can be devastating. It is essential, therefore, to be fully informed about the likelihood of these events and their potential effects.
Recent hurricane seasons have made it abundantly clear that the assumption "it won't happen on my watch" can be badly misguided. For the past 30 years, in fact, losses from natural catastrophes have been steadily increasing.
The trend is largely due to the increase in the number and value of properties in areas of high risk–and the trend is likely to continue. For this reason, sound risk management practice calls for sophisticated catastrophe risk management.
Fortunately sophisticated tools are available to help risk managers better manage catastrophe risk and confidently answer such questions as:
o What are my company's potential catastrophe losses?
o What is the likelihood of a catastrophe affecting my facilities?
o How much insurance or reinsurance coverage do I need?
Catastrophes impact every U.S. region, putting virtually all properties at risk. While risk managers are aware of the obvious risk from regional perils like hurricanes in the Gulf and Southeast, earthquakes in California, severe thunderstorms in Tornado Alley, and terrorism in major urban centers, they may not be fully cognizant of the risk in other regions.
In 1812, for example, one of the largest earthquakes to strike the continental U.S.–estimated to have been around magnitude 7.7–occurred in the New Madrid Seismic Zone, or NMSZ, located in the Mississippi River Valley. If an earthquake of similar magnitude were to occur in the same region today, our estimate is that insured losses would exceed $88 billion, with most of the losses concentrated in the urban areas of Memphis and St. Louis.
Or consider the F4 tornado that tore across central Massachusetts in 1953. If an equivalent tornado were to touch down and follow the same path today, we estimate that insured losses would likely exceed $1 billion.
Focusing on the more common and familiar perils at the expense of less frequent but potentially more catastrophic events is common.
For example, a national real estate company might commission a catastrophe loss modeling analysis to prepare for an upcoming insurance renewal. A glance at the company's property values at a state level would have led one to believe that its current coverage was appropriate.
However, the detailed catastrophe loss analysis suggested that the company was buying far more wind coverage than needed, and only half the earthquake coverage necessary to match its tolerance for risk.
This company did not have access to the information needed to understand the loss potential of its portfolio until completing a fully probabilistic catastrophe analysis. In this case, the unexpectedly large earthquake loss potential stemmed from the number of company-owned properties concentrated in the NMSZ.
Furthermore, the age of the company's properties made them exceptionally vulnerable to damage caused by seismic activity. With these new insights into the impact that regional seismicity could have on the company's facilities, the risk manager was able to re-align insurance coverage to reflect the company's true loss potential.
Until the development of catastrophe modeling, risk managers had little choice but to rely on subjective rules of thumb, or actuarial studies based on sometimes limited historical data, to quantify their insurance needs. Over the past few years, however, modeling has helped risk managers to take greater control of the risk-transfer process.
When faced with escalating premiums and a hardening market, risk managers turned to modeling to help determine their true coverage needs. Many realized they were buying more insurance than they needed and were able to offset rising premiums by eliminating superfluous coverage.
Some of these risk managers also were required to take on higher retentions. Models served to quantify the likely financial impact of these new retention levels. With modeling analyses performed at the address level, multiple retention scenarios can be considered.
It becomes a simple exercise to evaluate in dollar terms the effectiveness of portfolio-based versus location-based deductibles, or percent-of-loss versus percent-of-value deductibles. Risk managers are thus able to identify the most cost-effective retention structure for their portfolio. Savings also can be realized by leveraging the credibility of catastrophe modeling.
By sharing model results with underwriters during renewal negotiations, risk managers help eliminate much of the uncertainty that underwriters would otherwise factor into their premium quotes. Even if the insurer also uses a catastrophe model in its underwriting process, it can seldom match the level of detail that can be achieved with an in-house risk analysis.
Finally, risk managers can use catastrophe models to perform cost-benefit analyses of a variety of mitigation and retrofit measures. For example, models can be used to quantify the impact on average annual expected loss of installing hurricane shutters or impact-resistant glass.
Catastrophe models combine physics, meteorology, engineering, statistics, actuarial sciences and other disciplines to provide the most comprehensive depiction of the likelihood of losses from extreme events.
The purpose of catastrophe modeling is to anticipate the likelihood and severity of potential future catastrophic events and estimate their impact so that companies can prepare appropriately.
In view of the limitations of historical data, catastrophe modelers have developed alternative methodologies based on sophisticated stochastic simulation techniques. These techniques generate a large catalog of potential future events that reflects the characteristics and occurrence patterns of a peril–such as hurricanes, earthquakes and severe thunderstorms.
The resulting computer-generated simulated events are then merged with information on property holdings (including property value, construction type and occupancy class) and insurance policy terms. The results provide a distribution of various levels of loss along with their associated probabilities; that is, models provide information concerning the potential for large losses before they occur.
Catastrophe modeling technology has evolved in terms of detail and robustness since its introduction 20 years ago. The technology has provided reliable loss estimates for actual events in real time, as well as probabilistic loss estimates for events that have not yet occurred.
But widespread use of this technology beyond the (re)insurance industry has only come about relatively recently. Today, financial institutions–including investment firms, hedge funds, banks and mortgage providers–are all using catastrophe models to help them make smarter investment and business decisions.
Catastrophe modeling offers enormous value to risk managers that continues to increase as the technology evolves and as property values increase in risky areas.
Catastrophe modeling enables proactive decision-making and strategic planning. It should be an essential component in any company's efforts to manage its risk.
As modeling analyses become more accessible–whether in-house or as a service through brokers or directly from modelers–risk managers are embracing this technology and using it to further their companies' competitive advantage and make scientifically-sound decisions about their catastrophe risk management programs.
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