Parametrics, alternate risk for outbreaks, and a Heartbeat in the Fog
by: Pat Kelahan
March 12, 2020
It’s not often that a nexus of insurance/finance, and tech factors rises in prominence and promise, but seems the time is now. Blockchain- parametric- captives- business interruption – AI/ML- coverage gaps- ILS- front and center! Supply chain disruption due to collapse of integral parts of the pre-existing arrangements has rippled strongly through the global economy, and clever, innovative risk management and financing programs are now available for application, however from a previously under-utilized area of insurance.
Patrick Kelahan is a CX, engineering & insurance consultant, working with Insurers, Attorneys & Owners in his day job. He also serves the insurance and Fintech world as the ‘Insurance Elephant’.
The common theme of news in the world is not surprising- COVID 19. For the insurance world the outbreak has reminded us that absent direct physical loss, not many insurance policies provide cover for loss of sales, event cancellations, lost productivity, force majeure actions, etc. Various estimates of the economic disruption caused by the outbreak run from $1.5 trillion to $4 trillion; if one considers the effects of the drop in global equity values over the past week the amount clears the $10 trillion mark. The risk of economic loss has for the most part been too difficult to wrap one’s arms around, the basis risk is easy to see but sufficient data have not been available to build a predictive model for underwriting the risk. Perhaps until now.
Traditional indemnity insurance cover has facilitated risk agreements between parties and carriers- what property, what value, what risks insured against, what prompts coverage, how is it estimated, how it’s paid, etc., the,
“There must be a demonstrable and calculable loss that can then be used to justify a payment, in compensation for that amount.”
While the authors of that article were focused on intangible assets (more on that later), the concept stands as a significant barrier to reimbursement for losses being experienced globally due to disruptions in supply chains, transportation, retail, healthcare, services, and other factors that do not have a demonstrable loss in the traditional sense, and the losses are more difficult to calculate as there is no direct loss to a physical property.
The BI cover concern is not without precedent; business interruption cover has existed for a long time, but BI cover generally triggers when a covered direct loss occurs, e.g., loss or reduction in sales for a retail store that suffers a direct loss that causes sales to drop due to the claimed damage. Or, perhaps, a promise to award a hole in one winner causes an event organizer to have to purchase the winner an auto. Can these existing examples of indirect loss be adapted to addressing coverage gaps identified by the COVID 19 outbreak? Seems so, with the tools that tech is bringing to insurance.
Looking at the business disruption effects of the COVID 19 outbreak and understanding that indemnification models do not apply well, we’ll just jump right into potential options before us (it took two smart gents, John Donald and Dr. Marcus Schmalbach , thirty one pages to explain just the concepts of intangible assets and parametrics, it would take yours truly more pages for the rest than the reader would want to tackle.)
These are some examples of what is not practical, but are there practical options for reimbursement of costs for indirect loss such as what is being experienced?
In reading the “Heartbeat in the Fog” the discussion of intangible assets (IAs) having a growing impact on companies’ balance sheets, and the lack in the marketplace of insurance products that would protect companies in some fashion for reduction of value in intangibles prompts the extension of thought to economic loss due to COVID 19.
The authors of the article had a clear premise- if intangible assets are a significant portion of firms’ balance sheets, and some event/occurrence caused a reduction in the value of the intangibles, there would be a substantial effect on a respective firm’s business. Loss or reduction in brand value due to an occurrence has a calculable cost- at some future time. Loss of value of a firm’s IAs could affect collateral agreements, customer confidence, stock price, etc., and the firm’s stock price, or perhaps its ability to borrow. Consider the recently announced agreement between insurers Aon and Willis Tower Watson; each organization held significant balance sheet value in IAs. Aon will pay for the value of WTW’s IAs, and in anticipation of that agreement the market responded with a drop in Aon’s stock value. A post-purchase stock price might recover, but the post-purchase IAs value for the new company is projected to comprise 50% of the firm’s projected capitalized value. What does that have to do with insurance? Well, if Aon had the availability of or motivation for parametric insurance to protect loss of IA value due to an identified trigger event, perhaps that more nebulous but important asset value would carry more weight in total valuations.
On the street the value of a supply chain is intangible and difficult to quantify, but it’s a key aspect of value for millions of firms across the globe. Disruption of supply chains due to COVID 19 clearly has a cost, and few firms have any recovery options for the economic loss. Supply chain integrity is an intangible strength of a firm, part of a risk assessment, and in retrospect, a factor that will ultimately be measured in billions if not trillions of USD.
Can this ‘fog’ of intangible assets and agreements become insurable? With the growing availability of data in granular form, the tools needed to make the data ‘clean’, and to run the data through AI algorithms there is higher probability of having economic backstop for the next pandemic, or even for a less major disruption. Firms like ElectrifAi or Intellect SEEC who have marketable capability to aggregate structured and unstructured data in a form that can be uniformly assessed, and can apply AI methods to identify tendencies that might support creation of parametric triggers provide optimism for alternate risk products. Identification of patterns and dynamic improvement of analysis through machine learning will assist in placing recovery amounts on the products based on customized analysis of a firm’s business model and relationships.
Taking the thoughts another step, having concrete parameters, triggers, verifiers, and payments allows easy integration of Blockchain DLT into the process. All parties would have transparent understanding of the factors to an agreement, payment can be immediate, and payment platforms can be utilized to facilitate reimbursements. Claim costs would be significantly lower and support lower premiums.
Insurance companies will be interested in the products, but since the programs are in some ways more capital agreements than insurance, alternate risk funding options such as ILS will become more commonplace for financing of the agreements. ILS funds can create risk transfer vehicles that distribute the risk across multiple players (not derivatives as there is a need for an element of loss). If the reader would like more to read on ILS and risk transfer, see artemis.bm, and the writings of Steve Evans, e.g., this, or this.
COVID 19 caught the business world without an economic backstop, it’s that plain to see. Technology, methodology, and capital availability and interest provide a full spectrum of options for new insurance products and financing vehicles. Collaboration of data firms, carriers, Blockchain designers and rei/ILS platforms have a trillion-dollar opportunity in front of them. Parametric premiums will be less costly than the cost of disruption, and the products can even be leveraged by governments.
No action for future alternate risk response is fiduciary suicide. Start with grasping how indemnity models don’t fit, and how alternate risk financing just might. The articles noted within are fine places to start.
 “Heartbeat in the Fog”, John Donald, Dr. Marcus Schmalbach, RYSKEX Gmbh
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