Before the 1600s, the black swan wasn’t even considered a ‘rare bird’, but something impossible, since it was assumed that all swans had white feathers. Or did they? The discovery of one swan with black plumage introduced an age of doubt, and the ‘black swan’ became a symbol for things once thought impossible that can suddenly fly over and upset conventional thinking.

Black swan events also exist in the realm of managing insurance risk. After two quiet decades of tropical cyclone activity in the United States, for example, in 1992, Hurricane Andrew caused insured losses previously considered by many to be impossible, catapulting the catastrophe modelling industry into new prominence.

Since then, each large and unexpected catastrophe has prompted a re-examination of existing risk management practices. With traditional statistical tools unable to capture either the frequency or severity of black swan events, how should companies prepare for their impact? How can a company place a bell around the black swan’s neck?

Understanding catastrophes

Catastrophe events can be classified as known-knowns, known-unknowns, or unknown-unknowns. Those events for which there is abundant data and historical precedence are considered known-knowns, and they can typically be accounted for using past experience. Known-unknown events are rarer and have more severe repercussions. Companies employ catastrophe models to prepare for these events. While there is inherent uncertainty associated with known-unknowns, a robust model uses what has occurred in the past to infer what is possible in the future. Although it is not known when or exactly where the next inevitable major earthquake will strike in California, catastrophe models can account for the probabilities associated with a full spectrum of loss outcomes.

Jan-Mar 2016 Issue

AIR Worldwide (a Verisk Analytics business)