It's time to rethink the limits of parametric insurance.

The concept behind parametric insurance is not new. Insurance payouts automatically triggered by an event exceeding an index level have existed for decades. It's traditionally been applied to events like natural catastrophes that are relatively straightforward to define and for which an index or scale can be substituted for the actual loss sustained. When an earthquake reaches a certain magnitude as measured by the Richter scale, for example, insurers pay the policyholder an amount agreed to ahead of time under the terms of the contract.

Scientists have used the Richter scale to measure the size of earthquakes for more than 80 years. Today, however, data scientists can produce a fairly accurate index for far more abstract events, like customer sentiment on Twitter, or a change in shopping habits following an infectious-disease outbreak. Parametric insurance has also been applied to operational risk for large financial institutions.

Big data and related technologies are driving insurers and customers to rethink what's possible with a parametric insurance model.

But the rise of parametric insurance has bigger implications for an industry rapidly reacting to an influx of data sources and new technologies to analyze them. It's worth taking a closer look at where parametric insurance is headed and how a non-traditional, innovative mindset can influence the approach to pricing risk in today's world.

Parametric Insurance vs. Traditional Insurance

Parametric insurance differs from traditional insurance in a few critical ways. Whereas traditional insurance often involves a complex rating formula and detailed policy wording with the objective of indemnifying the insured for actual losses incurred, parametric insurance is far more straightforward—and somewhat limited. Parametric insurance is essentially a distilled if-then statement. If rainfall during a hurricane exceeds a stipulated amount, then a payout of a specified amount will be triggered.

This structure upends common insurance functions by streamlining underwriting and claims processes. Those efficiencies are highly attractive to insurers looking to reduce expenses, and they help insurers remain competitive and can be passed on to customers. Those efficiencies are at the heart of parametric insurance's benefits for customers as well, where straightforward coverage options and quick claim payments allow for an agile approach to transfer risk. Quick payouts provide increased liquidity to the customer, which can be critical for an organization's survival immediately following a catastrophic event.

But it's important to note that parametric insurance differs significantly from traditional insurance. It does not indemnify for actual losses. That creates a different kind of risk, called basis risk, for organizations that may find the payout is too small to cover their actual losses. On the flip side, the payout could also be larger than actual losses under the parametric model. For example, if the parametric insurance agreement stated a $10,000,000 payout for a hurricane of a certain magnitude. If that hurricane occurs, but only causes $9,250,000 in losses, the payout would be higher than the actual losses.

As organizations expand their risk management tools, they will look beyond traditional insurance to transfer a growing number of risks, including emerging risks such as climate change risk. This is creating opportunities to employ parametric insurance. A CFO might view it similarly to purchasing any other derivative that has basis risk but helps reduce the organization's overall risk level.

Realizing Parametric's Potential

The sources of potentially catastrophic risks and our ability to quantify them are changing. The world's largest insurance organizations as well as insurtech startups, risk consulting firms and outside disruptors are exploring parametric offerings for cyber-security, pandemics and terrorism threats. Evolving ways of capturing and analyzing data, including machine learning and neural networks designed to recognize patterns in the same way the brain does, are creating opportunities for businesses and governments to better quantify the impact of these events.

For example, an online retailer could purchase parametric insurance to cover an event in which a cyber-attack drives down website visits. That same retailer could use parametric insurance to offset expenses associated with restoring its reputation following a recall by measuring consumer sentiment on social media. A hotel could purchase a policy that's triggered if room-occupancy rates drop below a certain level following an infectious-disease event. At the same time, extreme weather events are becoming more common around the world, leading to increased interest in using parametric insurance as it has traditionally functioned to address risk related to natural disasters.

The potential for these parametric products is limited only by risk professionals' creativity and access to meaningful data. As aerial imagery, sensors, connected devices and other technologies continue to become more sophisticated, they will create newer and more accurate streams of data ripe for analysis and application. Any time you can meaningfully connect risk to a given data set, there's an opportunity to employ parametric insurance.

Parametric Coverage and Distributed Ledger Technology

Of all the technologies creating new opportunities for the expansion of parametric insurance, distributed ledger technology (DLT) – and more specifically the blockchain – is perhaps the best suited to do so. There's a natural connection between parametric insurance and DLT, particularly when it comes to smart contracts. Smart contracts enable automatic, immediate transactions that are independently verified without the need for third parties to do so.

The automatic payments built into parametric insurance require trusted data sources and secure transactions. DLT and smart contracts can ensure this trust while automating the payment and speeding up the process even further.

Rethinking Risk with Parametric Insurance

Parametric insurance requires a mindset shift for risk professionals. But as emerging technologies and the data they generate reshape risk in every industry, that new mindset could not be more relevant. Parametric insurance is a microcosm for a new risk reality in which large data sets mined to reveal previously unknown trends and patterns result in a straightforward and efficient risk transfer product.

Many risk and insurance organizations are investing heavily in the technologies that support this new mindset. Understanding parametric insurance's potential role in this new reality is an important step.

On the buyer side, risk managers should familiarize themselves with the new risk-related data generated by their organizations and work with brokers and underwriters to develop unique parametric insurance products to transfer selected risks.

For risk professionals eager to expand their understanding of how technology is affecting insurance and risk offerings, parametric insurance is something worth keeping an eye on.


About Michael W. Elliott

Michael W. Elliott, CPCU, ARM, AIAF, is senior director of knowledge resources at Malvern, Penn.-based The Institutes, which provides educational activities for the risk management and property-casualty insurance sectors. He is a subject matter expert specializing in risk management, data analytics, accounting and finance. He previously worked in risk management for a large global insurance broker.