Challenges are growing as many commercial lines insurers face economic pressures and increased regulatory requirements. In a business world where many factors are out of insurers' control, in order to remain competitive, they are well advised to understand the types of risks most appropriate for a specific product set, and price those risks accordingly. In some cases, this results in a carrier turning down business that is not profitable, or even exiting an entire market that is not performing as expected. So how do they know what they know? How do they identify, segment and price the risks in such a way as to consistently improve loss ratios? The short answer is applying predictive models to the underwriting process, say experts, and insurers of all sizes are starting to realize the benefits of their use. "Insurers are saying 'we are in a defensive posture with this, and we have seen adverse effects based on doing what we normally do,'" notes Mark Gorman, principle with Minneapolis-based Mark Gorman & Associates. "The market leaders already have invested in this technology as a core competency. But this is no longer a debate about whether or not to use predictive analytics. Now carriers of all sizes, representing all lines of business, are confirming that it's a matter of when, not if." One line of business where predictive analytic models have already started to offer big paybacks is workers' compensation.
Bob Dove, CEO of Honolulu-based Hawaii Employers' Mutual Insurance Co., (HEMIC) relates that in 1996, HEMIC replaced the existing workers' comp assigned risk pool to provide workers' comp coverage for Hawaii employers, including those who unsuccessfully sought workers' comp insurance in the voluntary market. HEMIC currently writes 6,500 employers, making it the largest workers' comp insurer in Hawaii. As the insurer of last resort, however, HEMIC does not have the luxury of turning down business that is not profitable-instead, it must rely on effective pricing models. "We anticipated moving into a soft market, and we knew margins would be tested," says Dove. "We wanted to do a better job right-pricing the product." When the company began building out its predictive models a year and a half ago, it did so to replace tools that measured only two metrics at a time. Dove says the evaluation process became a matter of looking at account underwriting, which examines the characteristics, specs and experience of that account, as well as traditional underwriting, which takes into account broader classifications, such as NCCI data (National Council on Compensation Insurance Inc., Boca Raton, Fla., the nation's largest provider of workers 'comp data). "By using predictive models, commercial lines underwriters can review an even larger number of more specific underwriting variables to identify those policies that are most likely to produce losses over a period of time," notes John Lucker, principal in the insurance practice at Deloitte Consulting LLP, Hartford, Conn. Dove agrees. "We are guaranteed market, so the question is not 'do we want to quote this?' It's 'how much do we want to charge?' With predictive modeling, we are able to string data elements together to find enhanced predictability," he says. "We are still doing classic underwriting, but nine different data elements now go into the scoring system. This helps us price the risks we are taking on." This philosophy appears to be working: In July, HEMIC declared a $3.25 million dividend payable to qualifying policyholders.
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