Ten days after Japan’s 2011 earthquake and tsunami, the Japanese government estimated a total national economic loss of USD 200-300 billion. The natural disaster brought about a ripple effect across the world. Operations of multinational enterprises with manufacturing units in Japan were stalled or slowed down as their supply chain took a hit.
Meanwhile, in the insurance world, a specialized process called catastrophe modeling was engaged to calculate estimated losses as a direct and indirect result of this natural disaster. Catastrophe or CAT modeling tools are statistical tools which quantitatively overlay natural (flood, earthquake, etc.) and man-made disasters (terrorism) on man-made structures (buildings, railroads, dams, etc.) to assess property damages. These tools are primarily used by insurers to estimate risks associated with insuring businesses, large or small.
CAT modeling is engaged early in a policy life cycle, when a customer approaches an insurance company to buy an insurance policy for his property (say an office building or a factory). An underwriter (who estimates the premium for the policy) evaluates the property, by quantitatively accounting for various parameters of the property. This usually includes:
Accuracy of location depends on the geography since advanced countries have relatively precise geocoding resolution. For instance, chances of exactly placing an US address on Google maps are higher compared to an emerging economy. Accurate placement on a map provides an added advantage of better estimating catastrophe damage.
Once a location is well defined, it is the job of the CAT modeling tools to apply relevant catastrophe (mathematical) models, based on the coverage sought in the policy. The result of this operation is a set of relevant statistics that outlines probable loss – although intimidating at first sight, quite often the winning emotion is the regret of not paying enough attention during high school statistics classes. In the insurance world, the underwriter is able to calculate the risk borne by the insurer based on the results generated by the CAT tool.
By integrating the above process seamlessly into its daily operations, an enterprise is able to better measure its risk aggregation as a result of adding new businesses to its existing portfolio. Advanced analytics enables better understanding of existing risks at various levels. Understanding existing risks and potential risk appetite empowers risk management to make informed capital and business decisions for future growth.
Like they say – In God we trust, for everything else there is data!