Beneficial disruption: smart insurers use predictive modeling to boost strategic and tactical decision-making.

Insurers that comprehensively deploy predictive modeling are primed to realize the greatest business gains. Emerging property/casualty leaders in both profit and growth are using innovative analytics to optimize every decision across their enterprises.

The justifications for predictive modeling are well established, but successful implementation is more complex. It takes accessing and managing sufficient data, capturing a thorough understanding of the business, leveraging specific knowledge of regulations, recognizing the competition and integrating it all into a decision-support framework.

For those who get it right, this leads to highly beneficial disruption. No other words better describe the effect of predictive analytics on an inefficient market. Witness the recent growth of a few direct-to-consumer personal automobile insurers. They have effectively proven the value of implementing predictive modeling systems.

Many personal-lines insurers today can underwrite virtually any automobile risk because proficient modelers are using tools and data that validate risk-assessment decisions. The next wave is following quickly for homeowners business; and commercial lines carriers are establishing similar strategies.

Unlike one-dimensional rating algorithms of years past, today's predictive analytics are multidimensional with simultaneous interacting variables that span countless possibilities. They are constructed with cutting-edge data assimilation techniques and advanced variable-selection methods that are indispensable in Frank J. Coyne, a Best's Review columnist, is chairman, president and chief executive officer of ISO. He can be reached at [e-mail] pinpointing underlying intelligence from complex, often disparate data sources.

By applying such modem, sophisticated predictive analytics, companies are growing and staying profitable.

Historically, insurers and agents grew market share by ramping up distribution and advertising in specific areas. Today, each line of insurance can leverage decision engines where sales costs can be measured and balanced with other metrics for expense, risk and profit.

Insurers can use lifetime value, price elasticity and channel-preference models for new business, while boosting renewal book performance with portfolio management, database marketing, retention programs and operational improvements.

Claims handling also has progressed, from individual case management to the deployment of advanced models to better forecast ultimate reserves, detect suspicions claims, identify liability and subrogation potential, estimate repair costs and more. Agents are offered diversified products, and their underwriting authority is more proficiently driven by multifaceted information about potential catastrophes, population growth, demographics, econometrics and business mixes in their markets.

Predictive modeling offers opportunities to boost strategic and tactical decision-making. Insurers are looking to use more and better data--especially geospatial information. They are applying keener know-how to pricing, marketing and operations. And they are implementing and executing in a decision-support framework.

The available analytic talent pool is thin, but providers, consultants, and third parties are sprouting up with productive predictive-modeling solutions to give even smaller entities a fighting chance.

Insurers must increasingly take advantage of computing power, business-rules engines, extensive data access, talented analysts, advanced mathematics, and modeling technology to deploy insightful decision-making in core business, distribution and operation areas. Otherwise, they are likely to get out-segmented, outrun and outflanked by competitors.