Standard & Poor’s Ratings Services’ Insurance 2007 conference, held June 3-5 in New York City, has yielded some valuable advice from three industry leaders on how to properly employ models.
S&P noted that the increasing use of predictive modeling tools has produced “reams of data that can help property/casualty insurers set prices, sharpen their underwriting criteria, and get a handle on market dynamics in everything from typhoons to auto coverage.” However, the models alone aren’t enough to “turn a weak insurer into a strong one.”
S& P said “models can be “used or misused,” if they’re not accompanied by “pricing discipline, a sound strategy, and an understanding of the market.” Panelist Stephen L. Way, former CEO of HCC Insurance Holdings Inc. and founding partner of SLW International LLC, noted that “discipline may be more important than all the modeling in the world.” The best model being “your own experience.”
Forecasts derived from models are most useful in “predicting loss levels in lines of business where data is plentiful and underwriting standards are comparable, such as the workers’ compensation or automobile lines of business,” said S&P. They are still of use, but less so, when it comes to predicting catastrophic losses from hurricanes, earthquakes, and similar disasters. “Predictive modeling works best with large amounts of data and where there is homogeneity in terms of policies,” explained Dale A. Thatcher, CEO of Selective Insurance Group Inc. He cited the “boost” in Selective’s workers’ comp business as an example.
Peter Nakada, senior vice-president and managing director of Risk Management Solutions reminded the audience that “the models are only as good as the data going into them” (i.e. ‘garbage in garbage out’ in computer terms). He stressed that modelers must be sure that the type of data they choose to collect is correct, that the data entered is valid, and that underwriters who use the information can double-check the data and incorporate any changes into the final model.
Missing and/or incorrect data can have a very serious effect when it comes to catastrophes. “After [Hurricane] Andrew in 1992, everybody used catastrophe models,” Nakada explained. “Everybody runs them, but they don’t always use them well. You have to have some intuition around how you use the model and incorporate it into executive thinking. Hurricane Katrina, however, was a big example of where modeling fell down. The models were pretty good with wind damage, but they definitely missed the flood in New Orleans.”
Somewhat paradoxically, modeling is frequently “uncertain for those rare catastrophic events that cause the most claims,” the panel agreed. Thatcher noted: “The heavy use of predictive modeling, in fact, began when markets were hard, while insurers were making money and prepping for the next cyclical downturn. It started when markets were hard, to be able to [minimize rate] reductions when markets turned soft. Predictive modeling was laid on top of historical profitability.”
However, just because a model indicates one set of rates, doesn’t mean that an insurer actually charges those rates; they can be different from what they should be, based on predicted losses, even when the models seem to make actuarial sense. “Unless modeling makes common sense and you can explain it to the customer, it won’t work,” Thatcher added.
S&P also noted that modeling tools continue to evolve. “Some catastrophe bonds are now modeled so that they are triggered not on total damage loss but on wind speeds. And new ones could be based on things as esoteric as a flood in London or the threat of terrorism at the Beijing Olympics,” said the bulletin.
Even with all of the new tools available, S&P’s panel stressed that “the best risk managers will have to use the elusive mix of common sense, intuition, and experience.” Otherwise, Way indicated, they will be caught in the same trap that always bedevils insurers. “The losses don’t decline as much as the rates,” he added. “Modeling is just one tool you use. Then you have to decide how conservative you want to be.”
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