December 4th, 2014

Casualty Catastrophe Risk Modeling: Part II

Posted at 1:00 AM ET

Casualty catastrophe occurrences have become increasingly common over the past decade. The recent 2008 financial catastrophe is the easiest to cite, due to its sheer size and the fact that it continues to unfold even today. But, there have been many others. The collapse of the “dotcom economy” led to scandals around initial public offering laddering and equity analyst conflicts of interest. Accounting firms were not alone in suffering financial loss related to such debacles as Enron, WorldCom, Tyco and Adelphia. While insured losses did not reach those of property catastrophes, economic damages were profound. Enron’s loss of USD66 billion in market capitalization alone - not including the economic damage caused to other companies - was more than double that of Hurricane Ike (approximately USD30 billion). The financial catastrophe is estimated to have caused economic damage of above USD1 trillion, with more likely to follow. When considered in the context of the Deepwater Horizon industrial accident, the casualty catastrophe that unraveled from the largest US offshore energy event over the past 40 years was by no means remote. Beyond the initial property loss of the actual drilling rig, liability risk in paying claims continues to extend and ripple throughout the supply chain involved as well as the environmental impact to numerous coastal and commercial businesses. Asbestos litigation, perhaps the longest casualty catastrophe on record, has paid out over USD70 billion and by some accounts may be entering its third wave. Therefore, asbestos is an emerging crystalizing risk that needs to be continuously monitored, measured and modeled for those who continue to be exposed to it.

Casualty catastrophes, unfortunately, do not follow patterns - unlike property catastrophes. The geographies, natural conditions and other indicators of hurricanes, earthquakes and other property disasters offer some relative sense of predictability. A hurricane on the Florida coast is not unusual. Casualty catastrophes, however, rarely arise from the same conditions - or whose triggers emanate from the same companies or industries - as their predecessors. In fact, many potential casualty catastrophes (especially those in the broad “technological” emerging risk category) are still considered “black swans,” to at least some of the (re)insurers that cover them. Some can appear out of nowhere and wreak havoc quickly.

Just about every large public or private company and its service providers (manufacturers, pharmaceuticals, technology firms, investment banks, law firms, accountants and consultants), strategic partners and supply chain participants are potential flashpoints. The data set is vast, and when casualty catastrophe indicators appear, it may be typically too late to take preventive action. Therefore, casualty writers need to be proactive in regards to these unknowns. As amorphous and complex as these risks may appear, the minimum expectation is that carriers need to proactively and systematically understand these emerging and casualty exposures and be able to measure the directional impacts they will have on their portfolios’ exposures and aggregate limits.

Uncertainty is always a factor in insurance risk and capital management decision-making. Targeted, supported assumptions applied to available data using thoroughly researched and carefully designed models are intended to counteract the unknown, at least to the extent possible. Thus, to protect their capital from the casualty catastrophe risk, carriers have needed tools and models that can probe a portfolio to apply potential new and emerging realistic disaster scenarios, identify likely exposures and map how liability would spread from the epicenter to other industries, jurisdictions and lines of business. Not only has the modeling technology been challenging to develop, it tends to be exasperated by the lack of available essential data and the prevailing practice of siloed risk management.

Link to Part I>>

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