Tag Archives: marine risk

No More Guessing Games for Marine Insurers

Huge ports mean huge amounts of cargo. Huge amounts of cargo mean huge accumulations of risk.

As a guiding principle about where marine insurers are exposed to the highest potential losses, it seems reasonable enough. But in fact, as RMS research has proven this week, this proposition may be a bit misleading. Surprisingly, a port’s size and its catastrophe loss potential are not strongly correlated.

Take the Port of Plaquemines, LA which is just south-east of New Orleans. It is neither well known nor big in comparison with others around the world. Yet it has the third highest risk in the world of insurance loss due to catastrophe: our analysis revealed its 500-year marine cargo loss from hurricane would be $1.5 billion.

Plaquemines is not an isolated case. There were other smaller ports in our ranking: Pascagoula, MS in the United States ranks 6 on our list with a potential $1 billion marine cargo loss due to storm surge and hurricane; Bremerhaven in Germany (ranked 4th at $1 billion) and Le Havre in France (ranked 10th at $0.7 billion).

Asia-Pacific ports featured less frequently, but worryingly one Asia port topped the list: Nagoya, Japan was number 1 ($2.3 billion potential losses) with Guangzhou, China a close second ($2 billion). Our analysis modeled risk posed by earthquake, wind, and storm surge perils in a 500-year return period across 150 ports – the top ten results are further down this blog.

Ports At Risk For Highest Lost
(500 year estimated catastrophe loss for earthquake, wind, and storm surge perils)

Estimated Marine Cargo Loss in Billions USD
1 Nagoya, Japan 2.3
2 Guangzhou, China 2.0
3 Plaquemines, LA, U.S. 1.5
4 Bremerhaven, Germany 1.0
5 New Orleans, LA, U.S. 1.0
6 Pascagoula, MS, U.S. 1.0
7 Beaumont, TX, U.S. 0.9
8 Baton Rouge, LA, U.S. 0.8
9 Houston, TX, U.S. 0.8
10 Le Havre, France 0.7

* Losses rounded to one decimal place.

Our analysis demonstrates what we at RMS have long suspected: outdated marine risk modeling tools and incomplete data obscure many high-risk locations, big and small. These ports are risky because of the natural perils they face and the cargos which transit through them, as well as the precise way those cargos are stored. But many in the marine sector don’t have these comprehensive insights. Instead they have to make do with a guessing game in determining catastrophe risk and port accumulations. And with the advanced analytics available in 2016 this is no longer good enough.

Big Port or Small – Risk Can Now Be Determined

Back to that seemingly simple proposition about the relationship between port size and insurance risk which I began this blog with. As the table above demonstrates, smaller ports can also present a huge risk.

But the bigger ships and bigger ports brought about by containerization have led, overall, to a bigger risk exposure for marine insurers. Not least because larger vessels have rendered many river ports inaccessible forcing shippers to rely on seaside ports, which are more vulnerable to hurricanes, typhoons, and storm surge.

The value of global catastrophe-exposed cargo is already huge and is likely to keep growing. But the right tools, which use the most precise data, can reveal where the risk of insurance loss is greatest. Leveraging these tools, (re)insurers can avoid dangerous cargo accumulations and underwrite with greater confidence.

Which means that, at last, the guessing game can stop.

In a box: Our ranking of high risk ports used the new RMS Marine Cargo Model™, with geospatial analysis of thousands of square kilometers of satellite imagery across ports in 43 countries. RMS’ exposure development team used a proprietary technique for allocating risk exposure across large, complex terminals to assess the ports’ exposure and highlight the risk of port aggregations. The model took into account:

  • Cargo type (e.g. autos, bulk grains, electronics, specie)
  • Precise storage location (e.g. coastal, estuarine, waterside or within dock complex)
  • Storage type (e.g. open air, warehouse, container — stacked or ground level)
  • Dwell time (which can vary due to port automation, labor relations and import/export ratios)

Cracking the Cargo Conundrum

The smoke from nearby forest fires drifting across the entrance to the Port of Singapore wasn’t unduly worrying the captain of Titan, arriving from Shanghai with 10,000 containers on board. He had clocked the oil tanker off his starboard side and was content that, after obviously having a few navigational hiccups, the pilot of that vessel was now holding a course and speed safely out of Titan’s way. It was as he relaxed back in his chair and looked out across the bow that the smoke thinned out and he saw it. Another tanker. Huge. Q­max class carrying liquefied natural gas.

This is not the plot of a blockbuster book or the climactic scene of a Hollywood disaster movie. It is one of a number of plausible scenarios in the new RMS report on the challenges facing insurers because of the huge growth in marine cargo.

The report “Marine Cargo Catastrophe Modeling: Navigating the Challenges, Charting the Opportunities” examines the outdated techniques and incomplete data that marine insurers have had to make do with in order to estimate their cargo cat risk and port accumulations. Put simply, for too long knowing how much exposure they’ve built up in enormous international ports has been a guessing game. And two recent CAT events, which caused multibillion dollar losses because of huge concentrations of cargo, have exposed this weakness to an uncomfortable scrutiny.

The risks of global trade

Whereas the risks for land or property are essentially static, cargos are constantly moving and so the risk variables might seem unfathomably complicated. It’s not just the number of ports the vessel will go through and the CAT­ risks in those locations: hurricanes, storm surges, earthquakes, and terrorist attacks. Consideration needs to be given to the geology in that region, the construction of the ports in that country, and the level of disaster­ preparedness that exists.

As we saw during Superstorm Sandy, loss outcomes can be influenced by factors such as the exact location of cargo storage (In containers? In warehouses? Stacked?). Equally important is the vulnerability of the products. Are they fragile like electronics (ruined by water) or more resilient like jewelry (which can more easily be salvaged)?

The new RMS report in combination with the soon-to-be launched RMS Marine Cargo Model will bring clarity to these issues.

A purpose-built model for the industry

The RMS Marine Cargo and Specie model will be generally available this May, with the launch of RiskLink version 16.

To develop the model, the RMS geospatial team analyzed thousands of square kilometers of satellite imagery of top global ports and created a proprietary technique for allocating risk exposure across port terminals and storage structures. The port Industry Exposure Databases (IEDs) included in the RMS Marine Cargo Model, also incorporate important information on “dwell time,” or how long cargo spends at a given location. This variable, which is critical in determining port accumulations, can be highly influenced by variables such as weather, port automation, import/export ratios, and labor relations.

Covering almost 80 countries and three perils (wind, storm surge, and earthquake), the new marine model will provide 11 high-resolution and 150 medium resolution port industry exposure database, enabling the best insight on cargo vulnerability and global port accumulation currently available to the industry.

Tianjin Is A Wake-Up Call For The Marine Industry

“Unacceptable”  “Poor”  “Failed”

Such was the assessment of Ed Noonan, Chairman and CEO of Validus Holdings, on the state of marine cargo modeling, according to a recent report in Insurance Day.


China Stringer Network/Reuters

The pointed criticism came in the wake of the August 12, 2015 explosions at the Port of Tianjin, which caused an estimated $1.6 – $3.3 billion in cargo damages. It was the second time in three years that the cargo industry had been “surprised”—Superstorm Sandy being the other occasion, delivering a hefty $3 billion in marine loss. Noonan was unequivocal on the cargo market’s need to markedly increase its investment in understanding lines of risk in ports.

Noonan has a point. Catastrophe modeling has traditionally focused on stationary buildings, and marine cargo has been treated as somewhat of an afterthought. Accumulation management for cargo usually involves coding the exposure as warehouse contents, positioning it at a single coordinate (often the port centroid), and running it though a model designed to estimate damages to commercial and residential structures.

This approach is inaccurate for several reasons: first, ports are large and often fragmented. Tianjin, for example, consists of nine separate areas spanning more than 30 kilometers along the coast of Bohai Bay. Proper cargo modeling must correctly account for the geographic distribution of exposure. For storm surge models, whose output is highly sensitive to exposure positioning, this is particularly important.

Second, modeling cargo as “contents” fails to distinguish between vulnerable and resistive cargo. The same wind speed that destroys a cargo container full of electronics might barely make a dent in a concrete silo full of barley.

Finally, cargo tends to be more salvageable than general contents. Since cargo often consists of homogenous products that are carefully packaged for individual sale, more effort is undertaken to salvage it after being subjected to damaging forces.

The RMS Marine Cargo Model, scheduled for release in 2016, will address this modeling problem. The model will provide a cargo vulnerability scheme for 80 countries, cargo industry exposure databases (IEDs) for ten key global ports, and shape files outlining important points of exposure accumulation including free ports and auto storage lots.

The Tianjin port explosions killed 173 and injured almost 800. They left thousands homeless, burned 8,000 cars, and left a giant crater where dozens of prosperous businesses had previously been. The cargo industry should use the event as a catalyst to achieve a more robust understanding of its exposure, how it accumulates, and how vulnerable it might be to future losses.

New Risks in Our Interconnected World

Heraclitus taught us more than 2,500 years ago that the only constant is change. And one of the biggest changes in our lifetime is that everything is interconnected. Today, global business is about networks of connections continents apart.

In the past, insurers were called on to protect discrete things: homes, buildings and belongings. While that’s still very much the case, globalization and the rise of the information economy means we are also being called upon to protect things like trading relationships, digital assets, and intellectual property.

Technological progress has led to a seismic change in how we do business. There are many factors driving this change: the rise of new powers like China and India, individual attitudes and even the climate. However, globalization and technology aren’t just symbiotic bedfellows; they are the factor stimulating the greatest change in our societies and economies.

The number, size, and types of networks are growing and will continue to do so. Understanding globalization and modeling interconnectedness is, in my opinion, the key challenge for the next era of risk modeling. I will discuss examples that merit particular attention in future blogs, including:

  • Marine risks: More than 90% of the world’s trade is carried by sea. Seaborne trade has quadrupled in my lifetime and shows no sign of relenting. To manage cargo, hull, and the related marine sublines well, the industry needs to better understand the architecture and the behavior of the global shipping network.
  • Corporate and Government risks: Corporations and public entities are increasingly exposed to networked risks: physical, virtual or in between. The global supply chain, for example, is vulnerable to shocks and disruptions. There are no local events anymore. What can corporations and government entities do to better understand the risks presented by their relationships with critical third parties? What can the insurance industry and the capital markets do to provide CBI coverage responsibly?
  • Cyber risks: This is an area where interconnectedness is crucial.  More of the world’s GDP is tied up in digital networks than in cargo. As Dr. Gordon Woo often says, the cyber threat is persistent and universal. There are a million cyber attacks every minute. How can insurers awash with capital deploy it more confidently to meet a strong demand for cyber coverage?

Globalization is real, extreme, and relentless. Until the Industrial Revolution, the pace of change was very slow. Sure, empires rose and fell. Yes, natural disasters redefined the terrain.

But until relatively recently, virtually all the world’s population worked in agriculture—and only a tiny fraction of the global population were rulers, religious leaders or merchants. So, while the world may actually be less globalized than we perceive it to be, it is undeniable that it is much flatter than it was.

As the world continues to evolve and the megacities in Asia modernize, the risk transfer market could grow tenfold. As emerging economies shift away from a reliance on a government backstops towards a culture of looking to private market solutions, the amount of risk transferred will increase significantly. The question for the insurance industry is whether it is ready to seize the opportunity.

The number, size, and types of networks are growing and will only continue to do so. Protecting this new interconnected world is our biggest challenge—and the biggest opportunity to lead.