Finding the right answers to these questions is becoming critical for most insurers, resulting in insurers searching for new tools and strategies. One tool garnering a lot of attention and gaining acceptance in the marketplace today is predictive analytics. Used effectively, predictive analytics is able to assist insurers in gaining and maintaining a competitive edge--a differentiator that is becoming increasingly difficult to achieve in today's market.

In the earliest stages of adoption, predictive analytics was predominantly used in claims within the commercial property and casualty space. Today, it is quite common to see insurers use predictive analytics to support claims reserving issues as well as to identify fraudulent activities within active claims. Much of the impetus for this predictive analytics adoption has been due to the continual rise of health care costs. Rising claim costs, coupled with declining investment income and an economic meltdown, has put tremendous pressure on insurers to secure operating profits from core business activities.

Encouraged by the early success in claims, more insurers are now turning to predictive analytics to support decision making focused directly at underwriting disciplines in an effort to generate a fair return from the business written.

The focus of insurers as they look at underwriting and policy management operations are primarily issues of understanding risk, pricing properly, and collecting the premium due. As companies continue to take a progressive approach to business (or at a minimum work to maintain the business that they currently have), predictive analytics is now often being factored into the strategy.

Today, more and more insurers are using predictive analytics to profitably grow market share, retain existing clients, minimize premium leakage, flag potentially misclassified risk, price with better precision, and provide protection against adverse selection.

Those companies willing to invest in predictive analytics early on, when it was still considered new and unproven, are seeing dramatic results, which encourage them to expand its use and continue to look for new opportunities for predictive analytics applications. The use of credit scores closely aligned with the use of predictive analytics is one common and easily understood example.

In our own industry, the use of predictive analytics has played a significant role in the dramatic increase of market share in the personal auto insurance vertical. In personal auto, where predictive analytics was first implemented in insurance, it's no coincidence that the top 10 carriers grew their market share from 56 percent to 67 percent between 1995 and 2009.

There is no question that predictive analytics will become a key component and a major strategic factor for insurers competing in the commercial insurance space. The question is "to what extent?"

As we investigate the potential for predictive analytics in underwriting, the options vary dramatically. Progressive insurers are using validated and sophisticated predictive analytics to expand the dimensions of straight through processing capabilities. Additionally, the same insurers are using predictive analytics to score policies, taking into account associated risks, and using these scores in conjunction with defined business rules to set prices, apply schedule credits, or place policies in the correct rate tiers and/or companies.

While measuring the ROI of predictive analytics is often more qualitative and subjective than quantitative, the benefits appear to be significant. And an attractive byproduct of the use predictive analytics is the ability to avoid adverse selection by deploying pricing strategies that are not easily recognizable by competitors.

If the personal auto insurance market is any indicator of what will happen in the commercial insurance space, it is likely that those insurers who invest early in predictive analytics will end up with the lion's share of the available business. More importantly, they will improve profitability because they better understand the risks they are writing, price accordingly, and avoid unprofitable business.

(Jim Haley is CMO of Valen Technologies and can be reached at either jim.haley@valen.com or (303) 350-3730.)

For more information on how Pinnacol Assurance uses the predictive analytics tool from Valen, click on this Tech Decisions article.

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