Organisations cannot control the weather, but they may be able to mitigate its financial impact. Michelle Bradley of Sigma, and Scott Kehler and Mike McAndless of Weatherlogics discuss weather analytics and their relation to weather captives
Unlike most types of risk, which can be avoided, controlled, or financed when necessary, weather risk presents a continual and persistent obstacle to many companies.
Agricultural companies’ crop yields are directly affected by the state of the weather, retail operations can be impacted by heavy snowfall, and transportation companies must deal with road conditions resulting from all manner of elements. Even though organisations cannot control the weather, they may be able to implement strategies to mitigate its financial impact. Doing so will, at times, involve the use of insurance and other alternative risk financing programmes.
Since captive use is continually growing within the landscape of emerging and non-traditional risks, there is a strong possibility that companies will want to analyse captive implementation with regard to weather risk and examine how it aligns with their overall enterprise strategy.
Fortunately, many captives already insure risks related to traditional property weather perils or stand-alone, weather-sensitive coverages, making the examination process substantially easier. Property coverages relating to named storms, convective storms and hail are often insured in captives on a deductible buyback basis, but they can also be covered through parametric insurance, which does not indemnify the actual loss, but generally pays out a fixed amount in the event of a specific trigger. For example, if a category four named storm hits a certain location (within a specific mile radius) the contract would pay a specified amount. A crucial component of parametric insurance is ensuring the limiting of ‘basis risk’, a term describing potential disparities between the index trigger and the exposure being covered.
Analyses of traditional property coverages related to weather perils normally rely on standard actuarial techniques. The data for these risks is typically captured on traditional loss runs, and most organisations have credible data at lower severity levels. Because these types of coverages are short-tailed, standard incurred and paid loss development techniques can be used, as well as frequency and severity methods. If a captive wants to consider multiple retentions, or if the retention is large, simulation and modeling techniques may be required.
On the other hand, non-traditional weather-related risks, such as those listed in the examples above, pose distinct actuarial analytical concerns. From a data perspective, most organisations fail to maintain internal records of specific weather risks and how those affect revenue or tangible assets. As such, industry or public data related to the weather event or risk and limited internal information will normally be compiled as a first step for companies considering placing these risks in a captive. This information could be used to establish a preliminary coverage trigger (for example rainfall is less than a specified amount over a specified time) and to determine the frequency and severity for the event or risk.
While data gathering may seem like a trivial task, it can become quite complex. One of the first considerations for any weather element must be the source of the data. Did the instrument used to measure the weather conditions conform to international standards, such as those developed by the World Meteorological Organisation, or were the measurements taken by non-standard equipment?
This is critical, since some measurements, such as wind, are highly sensitive to the siting and height of the measurement instrument.
Measurement techniques can vary over time, which can alter the characteristics of data. One common example of this is the difference between automated and manual weather observations.
Today, many airports are transitioning from manual weather observations, taken by a human weather observer, to automated weather observations, taken by computer-based instrumentation. This change has both advantages and disadvantages. On the positive side, measurements can be taken more frequently and cost-effectively. On the negative side, measurements are sometimes less accurate, or less detailed, than human observations. For example, human observers who observe hail can take note of the hail’s diameter, while an automated observation may not be able to observe hail at all. Similar pros and cons can be noted for other types of weather observations. When using this data for risk analysis, it is crucial to understand the measurement methodology, including the degree of automation used.
In cases where the weather risk and contract structure are especially complex, advanced modeling and data-gathering by a firm specialising in weather data and consulting may be essential in determining a sound loss projection. Once a weather risk has been identified, attention then turns to selecting an appropriate dataset of historical climate data to assess the risk. A weather risk consultant could help confirm the appropriateness of company specific data or assist in determining the best industry data source.
Weather risk consultants can also provide the advantage of performing quality controls on the meteorological data source. A dataset may have been generated by a reputable weather station, but that does not guarantee the quality of data. With tens of thousands of weather stations worldwide, producing millions of data points daily, erroneous values frequently find their way into otherwise reliable data. In Canada, the National Climate Archives are a primary source of climate data. However, due to changes in quality-control practices over the years, much of this data now only undergoes limited quality-control. Weather experts recognise the need to repair this data and have already developed a process to identify and correct errors. The amount of erroneous measurements in the National Climate Archives can number anywhere from hundreds to thousands for a single weather station.
Most insurance risks feature an expected frequency greater than one, but the frequency of weather risks may be less than one, depending on how the associated contract is structured. In the named storm example, the probability of a category four named storm hitting the location may be 5 percent (or .05), meaning that it is expected to occur once every twenty years. The frequency and severity distributions can also be used to model the risk and determine confidence levels associated with the projection. This modeling process can also help review probable maximum losses or approximate worst-case scenarios.
Once a weather event has occurred, additional weather data may be needed to verify the intensity or severity of an event.
Again, it is critical to identify a reputable data source for verification. One example of a difficult phenomena to verify is convective storms that produce hail. Hailstorms are frequently verified using radar-derived maps of hail swaths. While these maps can be a useful tool, it is important to recognise that weather radar does not directly measure hail.
As such, these maps are only estimates of hail occurrence and can be subject to errors both in size and location. Weather consulting firms will likely have access to a database of real hail measurements which can be used to supplement hail maps produced by radar. This database contains actual ground-measured reports of hail, including size, time, location, and precision anywhere across Canada and the US.
By combining these actual measurements of hail with radar-derived estimates, verification is made more accurate. In short, meteorological data must undergo thorough verification before being used to assess weather-related risk.
Since this type of data data continues to emerge on a daily basis, captives writing weather risk policies should fluidly monitor their actual and industrial databases to ensure both are utilising up-to-date information.
The decision to mitigate weather risk is always an easy one. However, the process of properly identifying and analysing the key weather scenarios, developing the appropriate and cost-effective risk-control response, and determining how to implement this process into an overall enterprise strategy can quickly become complex and overwhelming. This complexity is compounded when the strategy involves forming a captive or introducing this risk to an existing captive.
As such, it’s very common for experts to partner for the successful formation and operation of a captive. Common captive structure involves a captive manager, tax expert, actuary, broker, asset manager, and customer who has an intimate knowledge of the exposure to be covered all working together, but companies wishing to mitigate the financial impact of weather risk with a captive will gain significant value by adding a weather risk expert to the process.
