Advanced Outcome Modeling for the Jet Set

Insuring most of the major insurance companies in the world against catastrophic losses is no small task. For Renaissance Reinsurance, one of the key players in the reinsurance industry, balancing billions of dollars in potential losses against premiums is a daily job. It may sound like a high-wire act, but Renaissance Re's success has been built on precision modeling of outcomes and custom financial structuring. @RISK is essential to the company's strategy.

Renaissance Re's president William Riker is a longtime @RISK user—in fact, he may hold some kind of Palisade record. He was first introduced to @RISK and risk simulation in 1987 at an executive forum at Duke University's Fuqua School of Business, and he has been using it to structure customer contracts ever since. "We're a global insurer, and our customers are exposed to any number of different types of risk," Bill says. "We have to think of our exposure in terms of clusters of liability. This makes for pretty complex contract structures." In addition, "These are very large transactions worth many millions, and what we are trying to do with our modeling is to get a better estimate of the outcome distributions. We're looking at premium adjustment activity, loss distribution, and—of course—profit."

Bill says the transactions Renaissance Re contemplates are too complex to fit into other decision support tools. In addition to its capacity, he finds it more intuitive than the other tools he has tried, including Crystal Ball. "It really helps sort out the influential factors."

But the real advantage of @RISK? "It works very well on airplanes." Bill jokes. But it is true that he travels a good deal, and he finds that "With @RISK, you can build and test a probability model in two to three hours." When he gets off the plane, he has made up his mind and he knows all the reasons he's made the right decision.

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