The reasons are fairly straightforward: First, nat-cat models are anchored to and focused on specific regional perils, such as Florida wind events or a California earthquake, while a CBI model would have to be far broader in scope. For example, the supply chain effects of the Thai floods were broader than many industrial firms and insurance companies originally anticipated.

Second, the exposures that nat-cat models quantify are generally very well defined — for example, the location of a property to a peril, as well as the property's physical dimensions and the quality of its construction, are fairly easy to discern, and thus can be subjected to intensive engineering analysis. In contrast, CBI exposures are less well-defined and therefore obfuscate classical analytical techniques, such as statistical and engineering analyses.

This is not to suggest that nat-cat models are in any way perfect. All models are simplifications of reality that are designed to facilitate decision-making. Nat-cat models help to facilitate a much more informed view of property underwriting and risk management than would be available without them.

With this in mind, we believe that a promising approach to modeling CBI risk is now at hand. At its core, assessing CBI risk is a network problem; many industrial firms can have a variety of supply chains that expose them to a variety of global CBI risks (natural and man-made). To assess the underlying drivers of these risks, a model employing agent-based modeling techniques, geographical information systems and industrial supply-chain information now can be constructed.

Many industrial firms currently perform some form of business-impact analysis to identify and assess supply-chain vulnerabilities. This information can be practically incorporated into an agent-based CBI model, the output of which can quantify – irrespective of peril – supply-chain vulnerabilities, both for specific firms and in the aggregate.

Output from the CBI model can help inform supply chain/CBI risk assessment in a manner similar to the output that helps inform nat-cat risk assessment for both insurance companies and their insureds. Examples of output include:

• Numerical loss estimates of CBI risk by supply chain network; and
• Vulnerability analysis, based on a computer program that serves as an “attacking force” against the supply chains, which disables certain links in a chain on a simulated basis to quantify material vulnerabilities.

An attacking-force program simulates literally millions of scenarios to identify the probable maximum losses from various global supply chain disruptions, and provides information on those losses by firm and disruption. This information can then inform industrial supply chain risk decisions and insurance CBI analyses.

Widespread adoption of a CBI model by both insurers and their insureds could result in added capacity to CBI insurance products, which could help to economically mitigate CBI risks for industrial clients. Moreover, a broader market for CBI insurance could provide a growth opportunity for certain P&C insurance companies, which could be significant in the current low interest rate/soft pricing environment.