According to Maersk, “Everything that can be digitized will be digitized,” and there is no doubt that the world of shipping is undergoing a digital revolution. Marine insurers cannot afford to be left behind. From vessel movement data to port statistics and engine diagnostics and data, the Internet of Things (IoT) empowers insurers to segment and optimize their existing portfolio, identify new sources of risk and opportunity and offer truly connected policies.
Having operated in the IoT space since 2012, our (Concirrus’) turning point came in 2016, when we were approached by a marine insurer, which was interested to know whether data and IoT technology could be applied to commercial marine insurance. Unlikely? Perhaps, but we could see that the maritime industry was bursting with data that, with some clever technology, could be repurposed, to offer invaluable insights to marine insurers.
Having now spent the best part of 18 months living and breathing marine insurance, my team and I have developed a view on where the marine insurance industry is going, and it is a far cry from the traditional market model that is currently under so much pressure.
A new marine model
There are three primary characteristics in the current market. The first is rating factors. For the past 200 years, marine policies have been rated on risk, using a standard set of rating factors - vessel type, age, place of build, flag, tonnage, etc.
See also: How Is Marine the Heart of Insurtech?
The second is policy type. In the current environment, policies are global in nature, with wide coverage and few exclusions. Finally, placement, which is managed primarily via brokers using paper as their main form of conveying risk and getting contracts and documents to and from customers. The way insurers go about managing those three characteristics is undergoing a seismic shift.
It’s now clear that demographics alone are not an accurate indicator of risk. Add behavior to demographics, and you get a far better picture. We know this intuitively. For example, if we gather 10 people of any demographic pool, we know that, while they are all equal on paper, their driving styles vary dramatically. We have also seen this proven in other areas of insurance. This approach holds true for marine insurance, too, but how do we get there?
The answer is the combination of vast quantities of data, combined with machine learning algorithms and interpreted with insurance domain expertise. It is entirely possible to find out which behaviors correlate to claims, and the behaviors that cause claims. Because these are causation factors, they can be used as lead-indicators, meaning the insurer can see the claim before it’s happened, leaving the insurer to potentially intercept, mitigate and reduce the quantum of the claim overall.
Not everybody might need the global policies that are provided today, which means that, even in a soft market, some risks are overpriced and some underpriced. There is a market for policies that are "fractional" and provide elastic coverage. A simple example of this is war zone coverage. Today, this requires the customer to notify the broker, who, in turn, notifies the insurer that war zone coverage is required.
In the future (well, actually, it’s possible today), this process can be managed automatically by placing an IoT device on the vessel to detect an incursion into a war zone -- with insurance technology that automatically turns on the coverage, collects the premium and issues the appropriate documentation to the customer.
This zonal type of insurance also means that specialist insurers can narrow their focus to a given set of perils, whether that be geography or a specific aspect of the marine insurance spectrum. A zonal approach also means that insurers can assess their exposures and tightly tailor their reinsurance program.
With the current competitive pricing climate, it is widely recognized that the marine customers, who have invested heavily in safety technology, are not reaping the requisite insurance benefits, whereas other fleets are paying too little. That balance needs to be addressed.
This is last aspect of change that I see as being key to unlocking the greatest benefits. There is undoubtedly a role for a real-time placement platform for commercial risks, such as the Lloyds PPL and other solutions from EY, B3i, etc. Compare these with the current, paper-based and manually intensive process; real-time placement will allow the insurance market to operate much like the stock market does today, with much greater visibility and speed.
It’s hard to believe (or maybe it’s not), but it’s been said that by 11 am on the first day of trading for the year, the London stock market will have processed more transactions than the Lloyd’s market will in an entire year of operation.
We need only to look to other markets to see how real-time trading platforms have enabled an entirely new business model. One interesting analog of a market changed by a real-time placement platform is that of the betting industry. While it has always been possible to bet on who might win the Masters golf tournament, it is now possible to place a bet in real time on whether Tiger Woods might hit the next shot into the water. This type of bet is only possible because the technology allows the odds to be calculated, priced and placed in real time.
See also: Global Trend Map No. 7: Internet of Things
Given that is estimated that only 10% of the world’s risks are currently insured, micro bets may offer direct insight into how the insurance market could work, when you combine behavioral technology, fractional policies and a real-time placement platform. Our belief is that this new operating model would open large swaths of the market, which are currently uninsured, to innovative insurance products.
A major concern for insurers and reinsurers is the need to assess and forecast exposures and losses. Events such as the Tianjin explosion and hurricanes Harvey, Irma and Maria challenged insurers to understand accumulations and losses and in some cases caught them by surprise. The latest IoT technology will allow insurers, reinsurers and businesses actively managing risks to monitor their assets, in real time, and be more prepared for events – such as conflicts, natural disasters and machinery failure.
The Internet of Things and connected technologies mean that mobile devices and sensors offer up constant streams of data, and, within this data, lies insight into the behaviors and risks associated with that asset. At a basic level, this means a business can be aware of which mobile assets (e.g. vessels and cargo) are in what location at any time. By cross-referencing connected data sets, the total monetary value of those vessels and freight units can be determined automatically.
Using algorithmic technology, the latest technology can take things a step further. For example, companies can act whenever total exposure levels (the total insured value of its assets) reach a certain threshold, within a geographical zone. Today’s best-of-breed platforms, supported by data science teams, allow users to extract and unlock behavioral insight from their assets, leading to the creation of better products, pricing and profits.