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Predictive Analytics Drives Profitability Gap In Commercial Insurance

When predictive analytics is used as a "good offense" in gaining market share and in identifying the most profitable business, competitors will be left scurrying to adopt predictive analytics as a "good defense" just to remain competitive.

By Jim Haley, Valen Technologies

Between 1995 and 2009 the top 10 carriers providing personal auto insurance grew their market share from 56 percent to 67 percent. This dramatic shift was at least in part the direct result of the use of predictive analytics in making strategic company decisions. Insurers who chose to sit on the predictive analytics sidelines, waiting until predictive analytics were considered proven and safe, are now scrambling for a piece of the ever shrinking market opportunity.While commercial insurers have been slower to adopt predictive analytics, their move to the technology has now begun in earnest, and there is no reason to assume that the impact in the commercial sector won't be as dramatic as that on personal auto insurers. The early movers will own the majority of the market and, maybe more importantly, they will own the most profitable business while avoiding the higher risk customers.

Predictive analytics are on track to become a major enabler in the success of commercial lines insurers, providing a competitive advantage over those that are either late-coming adopters or non-users all together.

Considering the Possibilities When applied to commercial insurance, predictive analytics offers a multitude of opportunities. A few to consider include understanding true exposures, precision pricing, and underwriting fraud detection and tier placement.

For example, workers' compensation insurers leveraging predictive analytics are able to identify policies where payroll is not being reported accurately or, at least, is questionable. This payroll discrepancy could be the result of natural growth or shrinkage, or possibly blatant misrepresentation in amount of payroll, payroll distribution in applicable class codes, or the use of subcontractors. Regardless of the root cause, without understanding the true risk, insurers are unable to either price for the real risk or decline risks that are outside their corporate thresholds. Predictive analytics provide the level of detailed understanding required.

Another good example can be found in personal and commercial property insurance - a line of insurance that has traditionally been marginally profitable, primarily due to properties being inaccurately insured to value or because of unreported condition hazards. Predictive analytics can effectively be used to predict those often less-than-obvious property characteristics that should be the target of proper inspections in order to determine the most appropriate underwriting action. This will often result in additional premium.

Getting the Price Right Employing less sophisticated pricing methods, many carriers find themselves competitive victims of those insurers better equipped with predictive analytics to understand and implement effective pricing strategies, and cherry pick the good business while avoiding the bad. In other words, predictive analytics can be used by insurers for precision pricing to minimize premium leakage and retain the most desirable business. More importantly, predictive analytics can be used to mask an insurer's pricing strategies to protect them against adverse selection, penalize and discourage bad risks, and reward the good. Through the use of validated and sophisticated predictive analytics, pricing decisions can often be automated for a wide range of risks, leaving the insurer's underwriting staff with the time to focus on the more complex policies where their experience and judgment is most needed.

Insurers who are early movers use predictive analytics to score policies based on associated risks, using the resulting scores in conjunction with defined business rules to set prices, apply schedule credits, or place policies in the appropriate rate tier.

Bottom-line, insurers making effective use of predictive analytics will see their loss ratios decline and their profits increase.

Claiming a Bigger Piece of the Pie Insurers with a desire to increase market share, either by expanding the risks they are writing or through expansion into new geographic areas, will need to be keenly aware of new exposures if growth is going to prove to be profitable. When predictive analytics is used as a "good offense" in gaining market share and in identifying the most profitable business, competitors will be left scurrying to adopt predictive analytics as a "good defense" just to remain competitive.

Understanding the Game of Risk Insurance is all about risk management, whether an insurer is protecting its position or growing market share. A thorough understanding of the risk and subsequently making the most appropriate decisions will determine an insurer's long term success.

Anyone who has been in the insurance game for any amount of time knows that the risks and the "best" decisions are not always obvious. With predictive analytics, insurers have a fighting chance of uncovering previously unrecognizable risk patterns and characteristics. Gone unnoticed, the negative consequences can be devastating.

A good example of what can happen when risks are unknown can be seen in the AeroTech Incorporated incident that occurred in Las Vegas in 2001. The company was underwritten as a sporting goods manufacturer when they were, in fact, manufacturing model rockets. Payroll was reflected in class codes that assessed premium at just under $10,000, when the appropriate class code included a special treaty and premiums over $95,000. More importantly, the insurer may well have elected to pass on writing the risk had they known the true nature of the business. By sheer chance of timing, a near disaster was narrowly avoided when a major explosion occurred during a lunch break. Predictive analytics used to identify policies at a high risk of exposure misrepresentation could have been used to identify the true risk, or at least raise the question prompting a closer look, so that the correct actions could have been taken during the underwriting process. 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?" 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 with a better understanding of the risks they are writing, price accordingly, and avoid unprofitable business.

About the Author: Jim Haley is chief marketing officer for Denver-based Valen Technologies. He can be reached at (303) 350-3730 or jim.haley@valen.com.When predictive analytics is used as a "good offense" in gaining market share and in identifying the most profitable business, competitors will be left scurrying to adopt predictive analytics as a "good defense" just to remain competitive.

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