Spring World 2018

Conference & Exhibit

Attend The #1 BC/DR Event!

Fall Journal

Volume 30, Issue 3

Full Contents Now Available!

kerney-hurricane.jpgThe term megacatastrophe has continued to gain prominence in the insurance and reinsurance worlds over the last few decades. Typically used to describe the havoc wreaked by major disasters such as Hurricane Andrew in 1992 and Hurricane Katrina in 2005, megacatastrophe has also been used to illustrate the comprehensive and devastating effects of the four hurricanes to strike Florida in 2004 that caused billions of dollars in damage.

Another such event was Hurricane Ike. A strong Category 2 storm when it struck the Gulf Coast in September 2008, the storm surge created by Ike was similar to that generated by a Category 4 hurricane. The storm’s remnants traveled north, carving a path of destruction from the Gulf Coast through the Midwest and up to the Great Lakes region. Property owners were then left to clean up damages totaling billions of dollars from a storm many never expected to reach them.

The consequences of such megacatastrophes are often far-reaching and unique. In the aftermath of any catastrophic event, underinsurance or a total lack of insurance can prevent some homeowners and business owners from ever truly recovering. The interruption or loss of jobs, local commerce, and tax revenues can worsen the impact of adverse economic conditions. Moreover, the widespread damage incurred during a major disaster can drive up demand for the labor and materials needed to rebuild, leading to rising replacement costs and longer construction timeframes.

Thus, risk managers are not only faced with the daunting task of identifying and planning for the physical risks of a catastrophe, but they must address those less tangible risks and circumstances that occur and linger long after. To help meet such a challenge, risk managers are increasingly adopting advanced catastrophe modeling and analysis to assess risk and make informed risk management decisions more effectively across the enterprise before, during, and after catastrophic events.

Advantages of Catastrophe Modeling

Using the results of catastrophe models, corporate risk managers can evaluate and implement various risk transfer strategies to make cost-effective decisions for their enterprises. Furthermore, those risk managers who embrace the value of catastrophe modeling are poised to help advance their organization’s competitive advantage.

Modeling can help risk managers identify the effect of extreme events as well as quantify the amount of insurance coverage necessary to suitably protect the organization from adverse financial, infrastructure, and operational impacts. For instance, sophisticated catastrophe models can simulate natural and man-made events (e.g., earthquakes, hurricanes, severe thunderstorms, wildfires, acts of terrorism, etc.) and overlay those events on existing property exposures. Models can then determine the likelihood that one or more events will result in a potential loss, which risk managers can use to decide how much insurance coverage to buy, what deductible level to choose, and at what cost.

As a result, risk managers can align their insurance coverage more closely with their organization’s risk tolerance level and avoid unnecessary coverage. Risk managers may allocate risk transfer dollars more effectively by customizing their corporate risk management programs to reflect the realistic probabilities of loss from specific perils.

Models allow risk managers to get a better understanding of how – and how often – a potential catastrophe is likely to occur, which can help them avoid overbuying or underbuying insurance. While many companies purchase insurance based on the probable maximum loss (PML), the PML typically does not include information about the probability that an event will occur. By using catastrophe models, risk managers can get a better sense of the various levels of potential losses, insure against the chances they can occur, and develop more effective plans for disaster recovery and operations planning.

Catastrophe models are also used by risk managers to improve the quality of information on which they base critical business decisions, including the marginal impact of a property acquisition or sale, the geographical consolidation or dispersion of operations, and the allocation of insurance costs among business units.

kerney-top10-2.jpg


The sale or acquisition of a property affects an organization’s risk profile. Considering the location, value, construction type, and existing portfolio relationship of properties may significantly change the organization’s overall exposure. Risk managers are able to use catastrophe loss analysis to determine whether a renegotiation of insurance coverage is warranted, then quantify the appropriate rebate or preferred additional coverage.

Catastrophe modeling provides valuable insight into a company’s geographical distribution of exposures as well as the regions, perils, and businesses with the largest marginal impact on losses. Organizations can use such information to adjust growth strategies in an effort to mitigate catastrophe loss potential, such as determining where a business can expand without increasing loss potential or where a business may be particularly vulnerable to catastrophe losses. Additionally, risk managers can use the models to allocate the cost of insurance back to individual locations or business units, allowing the organization to evaluate the benefits of geographically diverse operations or assets.

Working with their insurance brokers, risk managers often use models to compare the probable cost of certain risk transfer and/or retention strategies to help decide on a reasonable policy deductible and how best to apply the deductible — such as whether it should be a percentage of site replacement cost, a percentage of aggregate loss, or a fixed sum, as well as whether it should be applied for each single event or capped annually.

Most large property/casualty insurers use catastrophe modeling when underwriting policies. This can work in favor of those insurance buyers who include model results with their submissions. Organizations that share detailed model results with their insurers can use such data as a compelling and influential negotiating tool when renewing their policies.

Using Data to Mitigate Risk

Though it is difficult to plan fully for all consequences of a megacatastrophe, the more information a risk manager has, the better prepared an organization will be when the time comes to rebuild. Almost five years after Hurricane Katrina devastated the Gulf Coast, the rebuilding process continues. Residents have moved to different neighborhoods while their homes are rebuilt, businesses have relocated, and many of those who kept their jobs through the tragedy have had to make new arrangements for getting to and from work.

One of the unique consequences of a megacatastrophe is “demand surge.” The phenomenon is defined as the increased cost of construction due to the large number of properties needing replacement or repair following a major disaster. After Hurricane Charley hit southwest Florida in 2004, many property owners were informed it would take at least three years before their properties could be replaced because of a severe shortage of construction materials and labor. During the back-to-back record-breaking storm seasons of 2004 and 2005 labor costs increased by more than 34 percent in the southeastern states of Louisiana, Mississippi, Alabama, and Florida. The installed cost of common asphalt shingles in those states rose by 39 percent.

Those are just a few of the many costs and factors involved in property insurance repair that risk managers must consider when planning an effective catastrophe risk management program. In recent years, new technology, research, and services have given risk managers the ability to access critical construction and claims cost information that can have a significant impact on risk management decisions. For example, risk managers can use such data to create an accurate insurance-to-value estimate, which helps match insurance premiums with the calculated property risk.

Claims costs are also useful risk management tools, helping risk mangers track and compare national, regional, and local trends with their ¬under¬lying risk calculations to determine the need for timely adjustments. What’s more, trend data can be used to help risk managers set reserves and monitor the impact of rising costs, which is particularly important for risk managers seeking to model risk during the current and challenging economic downturn.

For example, a significant economic downturn in 2008 led some to expect a decrease in construction costs. Instead a spike in oil prices dramatically increased the cost of many materials. Petroleum-based composition shingles — a common material used in areas hit by wind and hail damage from hurricanes, severe thunderstorms, and tornadoes — increased by more than 71 percent when compared with the previous year. Risk managers with access to this type of claims cost information are immediately alerted to changing risks and can make immediate adjustments.

Over the last several years, catastrophe modeling technology has grown into a powerful and reliable tool risk managers can use to assess potential risks across the enterprise and make better, more informed business decisions. Risk managers can no longer afford to plan solely for the physical threats a catastrophe poses to a single location. Rather, the modern risk manager faces the tremendous challenge of planning for and mitigating the far-reaching physical and intangible risks associated with a major catastrophic event. Those who adopt advanced modeling and data analysis tools will be able to help their organizations operate successfully in the face of potential megacatastrophes.

Gary Kerney is assistant vice president of ISO’s Property Claim Services® (PCS®) division, Bill Churney is a vice president at AIR Worldwide, and Jim Loveland is president and CEO of Xactware Solutions. ISO, AIR, and Xactware are subsidiaries of Verisk Analytics.