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How to Assess Costs of Business Interruption

As a professional loss accountant with more than 20 years of experience with business interruption (BI) valuation, I can understand why policyholders struggle with finding a repeatable, efficient system that produces an accurate measurement of their BI exposure. Over the years, some of my clients recognized the issues with the traditional BI values approach, and decided to make a change. Unfortunately, too many companies continue doing what they have always done, even when there is a better way available.

BI

Consider for a moment, just how important BI information is to your underwriter. The numbers you report give the underwriter the basis for writing coverage and calculating premium. Each renewal provides policyholders the opportunity to present their unique BI exposure. Unfortunately, this opportunity is often squandered because of a misunderstanding of business interruption values and the exposures they represent. The point of this article is to share a proven, alternative approach.

Understanding BI Values

First, there’s the ratable value. It is the “big number” that is calculated for the business as a whole, assuming a 12-month, total shutdown of all revenue-generating operations. This worst-case and often unrealistic scenario is the information requested by the insurance company, usually in the form of a one-page worksheet. Without additional information, the underwriter will use this information to set limits and charge premium.

The ratable value calculated is somewhat meaningless, except that it establishes the base assumption that is used as the BI value in all other scenarios, such as unincurred cost categories. The ratable value is seldom a reflection of your exposures. Better ways to assess your exposures are to examine your maximum foreseeable loss (MFL) and probable maximum loss (PML) scenarios.

What Is Maximum Foreseeable Loss?

The MFL, as the name indicates, is the worst-case scenario. This is not as extreme as the ratable value scenario, but pretty close. The assumptions used here include a complete breakdown of protection and loss mitigating factors while you are hit where it hurts at the worst possible time. An example would be the loss of a unique distribution center to a retailer during the holiday shopping season — say the distribution center that handles online orders goes up in smoke on Cyber Monday.

The factors used to measure the ratable value would be used in this scenario to determine the business interruption value. Certain assumptions may change depending on the duration of the loss scenario. For example, labor expense may be considered completely saved in the ratable value scenario because of the assumption that there is nothing left, but only partly saved in an MFL scenario.

What About the Probable Maximum Loss?

The PML is the same as the MFL, except that loss mitigation efforts and protections work properly. The PML also takes into account pure extra expenses used to retain customers. The PML can help with decision making on purchasing extra expense coverage.

What Happens in Underwriting?

Although I’m not an underwriter, I’ve typically seen insurance companies take an engineer’s approach to MFL and PML scenarios that vary only in duration. This singular perspective does not account for the rest of the pieces of the puzzle. The other pieces are the finer details that actually occur during a claim. In a real claim, topics like seasonality, make-up and outsourcing would surely come up, but you won’t see them on any BI worksheet.

The MFL and PML should be based on realistic loss scenarios and measured as if they were a claim. Simply applying the ratable value to loss-period assumptions produces misleading and inflated numbers. This is precisely why it is in your best interest to develop your own valuation method based on real scenarios.

Why Create Exposure Scenarios?

If BI values are based on assumptions, and you are using the worksheet, then the assumption is a 12-month loss scenario. Can you imagine a scenario in which your operations would only be affected for six months? The worksheet makes a blanket assumption of 12 months whether realistic or not. Coming up with various loss scenarios by location would flesh out a more realistic representation of the impact of each particular loss. The scenarios would also highlight high-risk locations along your supply chain, which could improve your business continuity planning.

An exposure analysis project is not only an accounting project; it’s an integrated business exercise offering multiple benefits to an organization. The goal is to identify and examine loss scenarios and the resulting ripple effects.

It isn’t necessary, nor is it practical, to anticipate every possible loss scenario. It’s better to prioritize by perceived risk and probability. Then, develop a good sampling of loss scenarios from which you can determine the impact to operations and the mitigating actions that would be taken. Depending on the exposure, involve the appropriate internal personnel, e.g., operations, sales, business continuity, IT and accounting. The external experts you may involve are your broker, legal counsel and, of course, a forensic accounting firm that specializes in insurance work. Additionally, your company’s business continuity plan (BCP) and incident response plan should be factored in. However your scenarios play out, the loss accountants can calculate the business interruption as though it were an actual claim.

As you can see, this approach would produce a more accurate BI value by location and overall. It’s the right way to look at business interruption, so make it a part of your approach with underwriters.

Checklist to Prepare for Business Interruption

Business interruption (BI) losses are among the most confusing types of claims in the insurance industry. As claim specialists, we are often asked for a “checklist” filled with action items for when a loss occurs. A “checklist” isn’t practical because there are too many variables and “if/then” scenarios to map out. When you have a significant property damage and business interruption claim, only experience can guide the way to a fair recovery.

However, there are actions that can be taken ahead of a loss to ensure you are prepared. The following seven items represent such a “checklist.” It will not only help with your next loss but can have an immediate benefit to your risk management program.

1. Prepare accurate ratable business interruption values

The annual ritual of preparing the business interruption worksheet is often treated as an administrative nuisance.  It should be looked at as an opportunity to accurately account for the insurable risk for which you pay your premium and to accumulate annual values for future trending.

The worksheet provided by the insurance company is woefully inadequate to explain the nuances of most businesses. Go beyond the worksheet and explain your business more completely to underwriters. For an effective BI values methodology, solicit help from the specialists, such as an experienced forensic accountant. The results will be appreciated by underwriters and should translate into more appropriate coverage and possibly a more favorable rate. Once a system is in place, accuracy, consistency and efficiency should be improved.

2.    Analyze exposure scenarios and calculate MFL and PML

Once the ratable BI values are calculated, policyholders should explore realistic loss scenarios. The BI value is an annual number that does not factor in real-life responses that would generally mitigate a claim. To get to the actual exposure to risk, companies should determine the maximum foreseeable loss (MFL) and probable maximum loss (PML) measurements. The MFL measures a “worst case scenario” in which all of the loss-control protections fail. The PML is the more realistic loss scenario, in which mitigation systems work and contingency plans are executed properly. In both cases, the property damage and business interruption effects would be calculated as if they had occurred.

Loss scenarios should be postulated in detail, e.g. by location and by occurrence, considering all factors. These numbers should not be measured by simply applying a daily “BI rate” to an engineered loss period. It is more realistic to prepare as if presenting a claim, exploring all “what if” possibilities. Insurers may offer some assistance in this process, but remember, their version will be from their perspective. As with any claim, you should always prepare your own scenarios and your own calculations according to your understanding of your operations. An independent forensic accountant will have prepared claims just like your scenarios and would be able to accurately value the losses.

3.   Analyze contingent risks

Concurrent with the MFL and PML analysis, you should work to understand contingent risks to your business. Knowing what your suppliers’ and customers’ exposures are is important. Policyholders should involve leaders in operations, procurement and sales to help identify contingent exposures. If you have a sole supplier, your contingent exposure may be greater than anticipated and should be examined.

It is important to understand how your current policy language would respond to the contingent loss scenarios you’ve identified. For example, if suppliers in your policy are referred to as direct supplier,” make sure you understand how this would be interpreted in a claim. If “direct” means only those suppliers with whom you have a direct contract, and an indirect supplier, i.e. a second-tier supplier, has a loss that affects you, would you be covered? These scenarios should be discussed with your broker and underwriter to ensure your policy will respond as expected.

Once the values and scenarios are updated, you will be better able to make informed decisions about your insurance coverage, limits and terms.

4.    Business interruption vs. extra expense

Another common discovery from performing an exposure analysis is which type of time element coverage is the best risk transfer solution. Considering each location, if the risk is a lost of sales, BI would cover the lost earnings. If sales are not the risk or they can be sustained at an extra expense, extra expense coverage would be more appropriate. If sales are at risk but can be mitigated to the degree contingency measures are enacted at an additional expense, it’s a combination loss exposure.

It’s of value to risk managers to know what the exposure truly is because, if an exposure can be covered by extra expense coverage, it may eliminate or reduce the need for BI insurance. For example, if you are a distributor with multiple warehouses whose inventory is insured at selling price, what’s at risk? If you have alternative space or can quickly secure temporary space, the likelihood of experiencing a sales loss that exceeds the sales value of your lost inventory is remote. How much BI coverage should you buy vs. extra expense? Exploring your loss scenarios and subsequent contingency plans would allow you to better quantify your risks and select the option best suited to your needs. Extra expense is a more “tangible” risk than BI, making it easier for underwriters to rate, and it generally will cost less.

5.   Gross earnings, gross profit and business income

The names are different, but the intent is the same – to protect earnings lost because of damage or loss of use of insured property. The history of each of these forms would take a separate paper to detail, but, in a nutshell, gross earnings is a form commonly used in the U.S. with a basis in manufacturing risks, while gross profit is used throughout the world and has its basis in mercantile operations. Business income is the term used for the current ISO forms. Today, all forms have been modified to accommodate almost any business — however, there are some situations where one form may be preferable. The terminology and the mechanics of calculating business interruption loss varies among the forms, but the answer should be the same, regardless.

The exception to this has to do with the period of indemnity — the gross profit form is usually limited to a specific time, while gross earnings will continue until repairs are (or should be) completed with “due diligence and dispatch”; there is the ability to add an extended period to recover sales. It is important to make sure the form you have would cover your potential loss period. For example, if you have a manufacturing company with specialized production equipment that have long lead times to replace — longer than the period that a gross profit form would cover — you should probably have a gross earnings form. If you do not see a scenario that would exceed the gross profit period and you cannot accurately predict an extended period required to add to gross earnings, the gross profit might be a better option. If there isn’t any scenario that would create a loss that exceeds the gross profit period of indemnity and you are comfortable that you can cover that time to recover sales, than either form would work. There are new options that allow you to pick which form you would like to use up until the closure of a claim — these forms eliminate the need to determine which form is right for your business. Just make sure you have a form that will cover your worst-case scenario.

6.   Professional fees coverage

Most policies now include professional fees coverage. Insurers recognize the need for dedicated claim preparation experts and are willing to pay for it as part of the claim. Often, this coverage is subject to limits that can be negotiated. If you are not familiar with this coverage or do not have it, you should discuss with underwriters. For the most part, this coverage can be included at some level just by asking. The benefits of having specialized claim preparation experts available as a resource for a claim can make the difference between a successful claim and a headache.

7.  Organize your claim team

In addition to forensic accountants, a claim may include forensic engineers, attorneys and others. It is a good idea to know those you want to use before needing their services. Meet with the various providers beforehand and select those that fit best for your organization. Typically, paperwork associated with hiring someone can be completed before needing their assistance (i.e. non-disclosure, purchasing, W-9, etc.) so that if something happens they can begin work immediately. Additionally, there may be an opportunity for the provider to help with reporting issues on business interruption values.

While no business wants to suffer a loss of earnings, the more prepared you are the better the results will be. The steps shown above may take years to fully develop and should be evaluated annually to account for changes to your business.

If these recommendations are incorporated into your insurance program, there’s no need for a claim checklist. Your risk management team will be prepared for any worst-case situations with the best-case solutions.

How CAT Models Lead to Soft Prices

In our first article in this series, we looked back at an insurance industry reeling from several consecutive natural catastrophes that generated combined insured losses exceeding $30 billion. In the second article, we looked at how, beginning in the mid-1980s, people began developing models that could prevent recurrences of those staggering losses. In this article, we look at how modeling results are being used in the industry.

 

Insurance is a unique business. In most other businesses, expenses associated with costs of operation are either known or can be fairly estimated. The insurance industry, however, needs to estimate expenses for things that are extremely rare or have never happened before. Things such as the damage to a bridge in New York City from a flood or the theft of a precious heirloom from your home or the fire at a factory, or even Jennifer Lopez injuring her hind side. No other industry has to make so many critical business decisions as blindly as the insurance industry. Even in circumstances in which an insurer can accurately estimate a loss to a single policyholder, without the ability to accurately estimate multiple losses all occurring simultaneously, which is what happens during natural catastrophes, the insurer is still operating blindly. Fortunately, the introduction of CAT models greatly enhances both the insurer’s ability to estimate the expenses (losses) associated with a single policyholder and concurrent claims from a single occurrence.

When making decisions about which risks to insure, how much to insure them for and how much premium is required to profitably accept the risk, there are essentially two metrics that can provide the clarity needed to do the job. Whether you are a portfolio manager managing the cumulative risk for a large line of business or an underwriter getting a submission from a broker to insure a factory or an actuary responsible for pricing exposure, what these stakeholders need to minimally know is:

  1. On average, what will potential future losses look like?
  2. On average, what are the reasonable worst case loss scenarios, or the probable maximum loss (PML)?

Those two metrics alone supply enough information for an insurer to make critical business decisions in these key areas:

  • Risk selection
  • Risk-based pricing
  • Capacity allocation
  • Reinsurance program design

Risk Selection

Risk selection includes an underwriter’s determination of the class (such as preferred, standard or substandard) to which a particular risk is deemed to belong, its acceptance or rejection and (if accepted) the premium.

Consider two homes: a $1 million wood frame home and a $1 million brick home both located in Los Angeles. Which home is riskier to the insurer?  Before the advent of catastrophe models, the determination was based on historical data and, essentially, opinion. Insurers could have hired engineers who would have informed them that brick homes are much more susceptible to damage than wood frame homes under earthquake stresses. But it was not until the introduction of the models that insurers could finally quantify how much financial risk they were exposed to. They shockingly discovered that on average brick homes are four times riskier than wood frame homes and are twice as likely to sustain a complete loss (full collapse). This was data not well-known by insurers.

Knowing how two or more different risks (or groups of risks) behave at an absolute and relational level provides a foundation to insurers to intelligently set underwriting guidelines, which work toward their strengths and excludes risks they do not or cannot absorb, based on their risk appetite.

Risk-Based Pricing

Insurance is rapidly becoming more of a commodity, with customers often choosing their insurer purely on the basis of price. As a result, accurate ratemaking has become more important than ever. In fact, a Towers Perrin survey found that 96% of insurers consider sophisticated rating and pricing to be either essential or very important.

Multiple factors go into determining premium rates, and, as competition increases, insurers are introducing innovative rate structures. The critical question in ratemaking is: What risk factors or variables are important for predicting the likelihood, frequency and severity of a loss? Although there are many obvious risk factors that affect rates, subtle and non-intuitive relationships can exist among variables that are difficult, if not impossible, to identify without applying more sophisticated analyses.

Regarding our example involving the two homes situated in Los Angeles, catastrophe models tell us two very important things: what the premium to cover earthquake loss should roughly be and that the premium for masonry homes should be approximately four times larger than wood frame homes.

The concept of absolute and relational pricing using catastrophe models is revolutionary. Many in the industry may balk at our term “revolutionary,” but insurers using the models to establish appropriate price levels for property exposures have a massive advantage over public entities such as the California Earthquake Authority (CEA) and the National Flood Insurance Program (NFIP) that do not adhere to risk-based pricing.

The NFIP and CEA, like most quasi-government insurance entities, differ in their pricing from private insurers along multiple dimensions, mostly because of constraints imposed by law. Innovative insurers recognize that there are literally billions of valuable premium dollars at stake for risks for which the CEA, the NFIP and similar programs significantly overcharge – again, because of constraints that forbid them from being competitive.

Thus, using average and extreme modeled loss estimates not only ensures that insurers are managing their portfolios effectively, but enables insurers, especially those that tend to have more robust risk appetites, to identify underserved markets and seize valuable market share. From a risk perspective, a return on investment can be calculated via catastrophe models.

It is incumbent upon insurers to identify the risks they don’t wish to underwrite as well as answer such questions as: Are wood frame houses less expensive to insure than homes made of joisted masonry? and, What is the relationship between claims severity and a particular home’s loss history? Traditional univariate pricing analysis methodologies are outdated; insurers have turned to multivariate statistical pricing techniques and methodologies to best understand the relationships between multiple risk variables. With that in mind, insurers need to consider other factors, too, such as marketing costs, conversion rates and customer buying behavior, just to name a few, to accurately price risks. Gone are the days when unsophisticated pricing and risk selection methodologies were employed. Innovative insurers today cross industry lines by paying more and more attention to how others manage data and assign value to risk.

Capacity Allocation

In the (re)insurance industry, (re)insurers only accept risks if those risks are within the capacity limits they have established based on their risk appetites. “Capacity” means the maximum limit of liability offered by an insurer during a defined period. Oftentimes, especially when it comes to natural catastrophe, some risks have a much greater accumulation potential, and that accumulation potential is typically a result of dependencies between individual risks.

Take houses and automobiles. A high concentration of those exposure types may very well be affected by the same catastrophic event – whether a hurricane, severe thunderstorm, earthquake, etc. That risk concentration could potentially put a reinsurer (or insurer) in the unenviable position of being overly exposed to a catastrophic single-loss occurrence.  Having a means to adequately control exposure-to-accumulation is critical in the risk management process. Capacity allocation enables companies to allocate valuable risk capacity to specific perils within specific markets and accumulation zones to minimize their exposure, and CAT models allow insurers to measure how capacity is being used and how efficiently it is being deployed.

Reinsurance Program Design

With the advent of CAT models, insurers now have the ability to simulate different combinations of treaties and programs to find the right fit, maximizing their risk and return. Before CAT models, it would require gut instinct to estimate the probability of attachment of one layer over another or to estimate the average annual losses for a per-risk treaty covering millions of exposures. The models estimate the risk and can calculate the millions of potential claims transactions, which would be nearly impossible to do without computers and simulation.

It is now well-known how soft the current reinsurance market is. Alternative capital has been a major driving force, but we consider the maturation of CAT models as having an equally important role in this trend.

First, insurers using CAT models to underwrite, price and manage risk can now intelligently present their exposure and effectively defend their position on terms and conditions. Gone are the days when reinsurers would have the upper hand in negotiations; CAT models have leveled the playing field for insurers.

Secondly, alternative capital could not have the impact that it is currently having without the language of finance. CAT models speak that language. The models provide necessary statistics for financial firms looking to allocate capital in this area. Risk transfer becomes so much more fungible once there is common recognition of the probability of loss between transferor and transferee. No CAT models, no loss estimates. No loss estimates, no alternative capital. No alternative capital, no soft market.

A Needed Balance

By now, and for good reason, the industry has placed much of its trust in CAT models to selectively manage portfolios to minimize PML potential. Insurers and reinsurers alike need the ability to quantify and identify peak exposure areas, and the models stand ready to help understand and manage portfolios as part of a carrier’s risk management process. However, a balance between the need to bear risk and the need to preserve a carrier’s financial integrity in the face of potential catastrophic loss is essential. The idea is to pursue a blend of internal and external solutions to ensure two key factors:

  1. The ability to identify, quantify and estimate the chances of an event occurring and the extent of likely losses, and
  2. The ability to set adequate rates.

Once companies have an understanding of their catastrophe potential, they can effectively formulate underwriting guidelines to act as control valves on their catastrophe loss potential but, most importantly, even in high-risk regions, identify those exposures that still can meet underwriting criteria based on any given risk appetite. Underwriting criteria relative to writing catastrophe-prone exposure must be used as a set of benchmarks, not simply as a blind gatekeeper.

In our next article, we examine two factors that could derail the progress made by CAT models in the insurance industry. Model uncertainty and poor data quality threaten to raise skepticism about the accuracy of the models, and that skepticism could inhibit further progress in model development.

A Nuisance Form Can Be Your Friend

If you are like most companies, the annual ritual of filling out the business interruption worksheet is a nuisance administrative task. The worksheet is generally required by the insurance company to track changes in the business and may be used as the basis to price your program.  Along with general industry knowledge, this worksheet may be the most important item that underwriters have at their disposal to price your risk. However, the worksheet is woefully inadequate to explain the intricacies of most businesses and is biased to err on the high side – which usually means a higher premium for you. For a routine that is regularly glossed over, the results can have some pretty substantial consequences.

The worksheet is meant to estimate the business interruption exposure for the policy period by estimating a value for the coming year. The business interruption value (BI value) is revenue minus certain specific direct variable costs, possibly adjusted to account for the payroll of for skilled wage employees who may be retained even if operations cease for a period. The result is an annual ratable BI value that assumes a complete outage for 12 months with no mitigation.

Only by coincidence can this BI value number come close to a realistic exposure to business interruption loss.

What does the ratable BI value tell the underwriter? In theory, the premiums required to cover the risk. How can this be when the number used is so unrealistic?

The underwriter would like to know more about your business. His problem is that he needs some mechanism to measure your risk against others in your industry. The BI values worksheet is an attempt to do this.

But, if the worksheet is so far off, what else can you do to tell your story?

You need to supplement the ratable BI value with information to differentiate your business from the pack. Developing realistic, worst-case loss scenarios, known as maximum foreseeable loss (MFL) and probable maximum loss (PML), and measuring them using a methodology that would actually be used in a claim is a better way to present your exposure. Measuring MFL and PML exposures will allow you to highlight your ability to mitigate losses through business continuity planning (BCM).

Just as improved physical safeguards generate lower premiums, adequate business continuity planning should also result in premium savings. This step is completely missed when providing the worksheet alone.

The effort to identify and measure exposures can be challenging — after all, it is impossible to predict everything that might happen. History of actual claims and current industry experience can be very helpful. In most cases, it is best to tackle this project in manageable pieces and try not to do it all at once. For example, start with the largest or most troublesome businesses or locations and work down from there.

This may end up being a multi-year project that will require some dedicated effort from you and third parties. But chances are the cost of a project like this will be justified by allowing you to make more precise decisions on coverage and possibly reducing premiums.