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A Big Data Solution to Coverage Gaps

Events that trigger non-physical economic loss threaten to bring insurers' operations to a standstill, unless they are prepared.
By: | July 6, 2017 • 6 min read

Insurance is the vital backstop that keeps the business world spinning.

To make it work, underwriters need a clear loss history caused by well-defined risk events. But businesses are finding that loss comes in many forms that don’t always fit into neat parameters.

“There is a gap between the economic loss that an insured suffers from a given event, and the amount their property policy actually covers them for,” said Jamie Miller, Managing Director, Swiss Re Corporate Solutions. Increasingly, more damage is done to a company’s bottom line through business interruption, drops in revenue, and other non-physical impacts.

The challenge, from an insurance perspective, is the lack of metrics to assess the full scope of non-physical losses and pinpoint their triggers. The traditional underwriting process can’t be applied without this data.

It’s a situation where the promises and possibilities of Big Data can be applied in a concrete way.

“We’re starting to see data points emerging that can serve as indices to measure the probability of events that trigger non-physical economic loss,” Miller said.

Armed with new information, insurance innovators are creating policies built to bridge the coverage gap using an expanded category of triggers.

The Widening Gap

Globalization and reliance on modern technology have brought businesses closer together. IT systems are increasingly interconnected through the cloud. Supply chains are more complex, branching off into further flung parts of the world. While these trends yield more business opportunities, they also expose companies to greater risks and increase the likelihood of suffering a loss.

Losses indirectly incurred from events in other regions are difficult to anticipate and effectively prepare for. And their full impact can be even more difficult to measure.

“Superstorm Sandy’s impact on myriad businesses offers an example,” Miller said. “The storm halted travel to and from anywhere on the East Coast. That caused significant economic loss in the northeast to many businesses that suffered no physical damage.”

In addition to natural catastrophes, events like pandemics or terror attacks could have similar widespread impacts.

While they’ll have no damage to repair, these entities will still have expenses to pay, and revenue interruptions can have significant long-term impact on the bottom line. Traditional property policies, however, either might not respond to a loss with no physical damage, or may not cover the full extent of economic loss.

Big Data Sources

Big Data and analytics enables companies to create new metrics to assess the potential economic impact from virtually any type of event that hurts their cash flow without causing physical harm.

Airlines, for example, are using flight tracking data from companies like FlightAware to measure capacity on incoming flights. This information can then be paired with other data points to estimate the scale of potential economic loss if those flights are cancelled.

In the hospitality industry, Swiss Re Corporate Solutions is also tracking dips in bookings through revenue per available room (RevPAR), a performance metric calculated by multiplying a hotel’s average daily room rate by its occupancy rate. It helps hotel operators assess whether they are filling available rooms at their average rate.  A falling RevPAR means either the hotel’s daily rate or occupancy rate is falling. Demonstrating drops in occupancy or revenue helps to show the economic impact of a travel-impeding event like Sandy.

Collecting this type of data enables development of probability indices needed to construct insurance products predicated on new triggers.

A Data-Enabled Action Plan

“There has been a lot of talk and hype around the possibilities of Big Data, but little done to actually capture its potential and use it with real impact,” Miller said. “Using Big Data to identify new triggers and write new products is a very concrete application.”

On a case by case basis, Miller and his team at Swiss Re Corporate Solutions are building policies for clients where there is exposure outside the realm of a traditional policy. By focusing on individual, unique triggers rather than events, the coverage expands the realm of what is considered an insurable loss.

The policies are similar to parametric coverages that Swiss Re Corporate Solutions provide for severe weather events and natural catastrophes. Parametric coverage is built around specific, easily defined characteristics of an event, rather than characteristics of a loss. Big data is helping to identify those characteristics that act as policy triggers.

“We ask our clients, ‘what triggers economic loss for you?’ The answers are not always obvious,” he said. There can be anywhere from one to 10 triggers.

“Then we’ll see if we can tap into Big Data to measure those impacts that haven’t been measured before,” he said. “It’s a practical field application of predictive risk modeling. The underwriting process doesn’t change; we’re just applying new information.”

For a policy covering economic loss from pandemic, for example, loss payout could be contractually defined based on a double trigger and reputable data from one or multiple sources, like an alert from the CDC or a terror alert issued by the government, combined with an insured’s subsequent RevPAR data showing drop-off in revenue.

“The triggers don’t define the actual, local event. We’re not looking at the actual scale or location of the pandemic,” Miller said. “We’re looking at the characteristics of the event that will cause economic harm to the insured by restricting travel and demonstrating lost revenue.”

As with traditional policies, clients identify the level of risk they are willing to retain. Beyond that, probability analyses and loss triggers derived from Big Data allow underwriters to structure terms and limits without relying on historical loss experience.

Strengths of a Trigger-Based Approach

The primary benefit of a parametric policy is that it can help to cover the growing gap between what a traditional policy covers and the full extent of economic loss. The way they are underwritten – with specific, unambiguous triggers and set payout amounts – also streamlines claims adjusting and provides quicker payments.

“When the triggers are met, payments are made. It’s that simple,” Miller said. “Speed of payment is a key advantage of parametric-type polices.”

With its access to capital and expert knowledge and a large appetite for aggregate risk, Swiss Re Corporate Solutions is uniquely positioned to build these policies.

Given the increasing interconnectedness of business sectors, tech platforms and supply chains, events that cause non-physical economic loss will become more and more common. Businesses and their insurers need a more efficient way to pay these expenses and return to normal operation quickly.

“I can foresee an evolution where a parametric element becomes standard,” Miller said.

To learn more about Swiss Re Corporate Solutions’ innovation risk products, visit https://corporatesolutions.swissre.com/innovative_risk/parametric/.

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This article was produced by the R&I Brand Studio, a unit of the advertising department of Risk & Insurance, in collaboration with Swiss Re Corporate Solutions. The editorial staff of Risk & Insurance had no role in its preparation.




Swiss Re Corporate Solutions offers innovative, high-quality insurance capacity to mid-sized and large multinational corporations and public entities across the globe.

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