Harnessing analytics: surviving in the 21st century will require capitalizing on the power of technology.

A gradual slowdown in property/casualty premium growth over the past 15 years has taken its toll on the industry, with nearly a third of P/C insurers serving U.S. markets vanishing. As premium growth slowed from 11.3% per year during the decade ending 1985 to 6% during the decade ending 1995, and to 5.1% during the decade ending 2005, insurers' average statutory rate of return dropped as well, from 12.6% to 10%, and then to 7.5%.

The consequences have been profound. The passing into history of Continental, the Home, Reliance and other once iconic insurers proves that scale doesn't guarantee survival. So what will it take to survive and thrive in the challenging times ahead?

Once, being a low-cost provider might have been enough. That formula might still be viable if the intensity of competition were the only thing that's changing. But the nature of competition is changing too, with breakthroughs in computing and analytics transforming competitive dynamics.

It wasn't long ago that sophisticated insurers began using predictive models based on credit information and territory or ZIP code-level data to help them price and underwrite personal auto insurance. The resulting market share gains motivated many other insurers to follow suit--a powerful testament to the models' effectiveness.

But personal auto underwriting and pricing are poised to take another great leap forward. Visionary insurers will soon deploy next-generation models based on hundreds and even thousands of variables for individual addresses.

Sophisticated insurers also are using advanced analytics to help them price and underwrite homeowners insurance and businessowners policies because the economics are compelling. With premiums for BOP policies averaging about $1,500, investing significant time underwriting individual policies is cost prohibitive. But with the right technology, an underwriter can enter some facts from a policy application and seconds later get a score indicating whether the application should be accepted.

The same analytics that now enable insurers to price and underwrite--far more precisely than ever before--also are being used by some to focus their marketing. P/C insurers' advertising expenses rose 74% in recent years, from $1.7 billion in 2002 to $3 billion in 2005. But success is no longer simply a matter of reaching more prospects by outspending the competition. Rather, sophisticated insurers are using analytics to target profitable risks.

Advances in analytics are transforming processes at every step in the insurance value creation chain, including loss adjudication and risk management. With claim fraud adding 10% to losses, leading-edge insurers are using scoring, predictive modeling, data-visualization tools and other advanced techniques to fight fraud and expedite payment of meritorious claims, taking a huge bite out of costs. And many insurers are now using catastrophe models to manage their exposure and optimize their reinsurance programs.

But a chasm is growing between those insurers using state-of-the-art analytics and those that aren't: the "haves" and the "have nots" Increasingly, the haves will be able to write business at the margins they target; the have nots will fall victim to adverse selection. Unfortunately, today's haves can't rest on their laurels. Any who do will become have nots, as new advances in analytics come online.

Keeping up in the intellectual and technological arms race that's transforming the insurance business will require much more than mastering sophisticated analytical software.

First, no matter how good analytical techniques are, the answers they produce are no better than the data fed into them. Success will require access to large volumes of high-quality data for developing and calibrating models. And, after models have been deployed, getting good underwriting and pricing decisions for an individual risk will require having high-quality data about the risk.

Second, correlation doesn't imply causality. Models based on causal relationships can provide a sound basis for business decisions, but models based on mere correlations are dangerous. Success developing advanced models will require teaming modelers with expert underwriters, actuaries and claims professionals who can distinguish meaningful causal relationships from coincidental correlations.

Finally, as markets soften consequent to recent profitability and increases in capacity, many insurers may have difficulty adhering to decisions based on analytics. Management commitment and effective command and control systems will be key.

In sum, advances in analytics are transforming what constitutes superior execution against the core fundamentals of the insurance business: underwriting, cost-based pricing, loss adjudication and risk management. But survival in the business will always require devotion to the fundamentals.

Frank J. Coyne, a Best's Review columnist, is chairman, president and chief executive officer of ISO. He can be reached at [e-mail].