Using Analytics to Augment Underwriting
Carriers can leverage predictive technologies to price risks with greater precision.
Insurance Networking News, 08/01/2010
For many years the practice of underwriting was more art than science. Now, insurers can use predictive analytics to wrap a layer of statistical rigor around the traditional wisdom of underwriting. Stated another way, predictive models quantify underwriters' years of observations to make that knowledge manageable, transferable and more valuable.
"Predictive modeling is building a framework to help support decisions traditionally made by gut instinct," says Lisa Diers, head of technical price and predictive modeling operations for Schaumburg, Ill.-based insurance carrier Zurich in North America. "It's the scientific proof supplementing what underwriters instinctively know after doing this for 15 or 20 years."
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Predictive analytics can foretell the future to some degree, reliably forecasting the frequency and severity of events, insurance executives say. Armed with that information, carriers can choose the right risks, and then price the coverage of those risks accurately. That enables them to take on customers they might otherwise have turned down, while charging lower premiums than low-tech competitors.
"The number of potential customers increases and, frankly, the number of price points available in the marketplace explodes," says Jose Trasancos, SVP of research and product development for Pawtucket, R.I.-based Narragansett Bay Insurance Co.
Predictive analytics is taking hold in many industries, but the insurance business seems ripe for the technology because of a long-time emphasis on data, observers agree.
"It's an industry that is just so rich in information and data that has incredible predictive powers that we realized we could put that data to work and harness it as one among several tools that we use in the underwriting process," says Kevin Toth, SVP and chief underwriting officer for Harleysville, Pa.-based Harleysville Insurance.
Gaetan Veilleux, senior director of predictive analytics at Denver-based Valen Technologies Inc., says perhaps 90% of personal lines carriers now use predictive analytics. "The better companies are moving in that direction," Veilleux says, noting that in commercial lines, the big companies are using predictive modeling, the mid-sized carriers are in flux and the smaller ones are trailing.
HOW IT WORKS
Predictive analytics works much like the system described in the book "Moneyball, The Art of Winning an Unfair Game," Diers says, referring to the 2003 bestseller by Michael Lewis.
In "Moneyball," Lewis describes how baseball's Oakland Athletics assembled winning teams despite a relatively paltry payroll by de-emphasizing well-known statistics, such as batting averages and number of stolen bases. Instead, the A's crunched latter-day data with greater predictive power, such as on-base percentage and slugging percentage, to combine players who could compensate for each others' weaknesses and capitalize on quirks of opposing pitchers.
Similarly, underwriters who once awarded a merchant a generic credit for being in business three years or more, can now use predictive models to determine when to begin and end the credit, while calculating the proper size of the credit at any point along the timeline, Diers says.
Traditional underwriting views data in isolation, while predictive analytics examines how risk characteristics operate in combination with one another, says Toth. Models can encompass "hundreds and hundreds" of factors, says Diers.
The automation underlying predictive analytics also ensures consistency, says Toth. "We're able to ensure that accounts and risks with similar characteristics are treated similarly across the underwriting cycle," he says. Consistency "was a big part of the reason Harleysville started to go down this road."
Predictive analytics can automate much of the underwriting for smaller, standardized commercial lines policies, says Julian Pelenur, CTO for Bedford, Mass.-based vendor FirstBest Systems Inc. Larger, more complex and more unusual cases still require the human touch of an underwriter, he says.
But automation does not make human underwriters obsolete, even when it takes over some underwriting functions in routine cases, says James McCully, product marketing manager for San Mateo, Calif.-based Guidewire Software Inc., a vendor of core IT systems.
In fact, observers agree predictive analytics make underwriters more valuable to their companies. "It casts the underwriter's role as that of a portfolio manager," working closely with agencies to evolve products, improve marketing efficiency and help retain customers, says Trasancos. Until now, he says, the role of the underwriter has been mainly focused on accepting or rejecting risks.
Diers adds that models should serve as tools for underwriters, and underwriters should not become subservient to the models. "There's definitely a point where you have to say, 'I know this is what the science says, and it just doesn't make sense,'" she says.
