The Right Way to Look at BI Values
As a professional loss accountant with more than twenty years experience with business interruption valuation, I can understand why policyholders struggle with their BI values. 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. The fact is that BI values are an important requirement of the insurance process. The challenge is finding a repeatable, efficient system that produces an accurate measurement of your BI exposure.
Consider for a moment, just how important this information is to your underwriter. The numbers you report gives the underwriter the basis for writing coverage and calculating premium. Each renewal provides policyholders with the opportunity to present their unique BI exposure. Unfortunately, this opportunity is often squandered due to a multilateral misunderstanding of business interruption values and the exposures they represent. The point of this article is to share an alternative approach that is proven to help policyholders take control of their BI values reporting while maximizing the opportunity to enhance the value .
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 twelve 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 un-incurred cost categories. The ratable value is seldom a reflection of your exposures. A better way to assess your exposures are to examine your MFL and PML loss scenarios.
What is Maximum Foreseeable Loss?
The maximum foreseeable loss (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 hitting you 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 going up in smoke on Cyber Monday. The factors used to measure the ratable value would be used here to determine the business interruption value for this scenario. 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 due to the assumption that there is nothing left, but only partly saved in an MFL scenario.
What about the Probable Maximum Loss?
The probable maximum loss (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. This can help with decision making on purchasing extra expense coverage.
What happens in underwriting?
Though I’m not an underwriter, I’ve typically seen insurance company’s take an engineers approach to MFL and PML scenarios that vary only in duration. This is singular perspective and 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. If it were 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 it 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 effected for 6 months? The worksheet makes a blanket assumption of 12 months whether realistic or not. Coming up with various loss scenarios by location would flush out a more realistic representation of the impact of each particular loss. It would further flush out high risk locations along your supply chain which will not only add value to your risk management approach but may also influence business continuity planning.
An exposure analysis project is not only an accounting project, it’s a integrated business exercise offering multiple benefits to an organization. The goal is to identify and examine loss scenarios and the resulting the 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 personal 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 accordingly. How ever 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. If you’d like to discuss this topic or any others, myself or my partners would be delighted to hear from you.