Predictive Analytics in Insurance

The use of predictive modeling has forever changed the way insurance works. The revolutionary technology tool allows insurers to design ever-more-sophisticated models that tap ever-more-detailed data sets to refine precisely how much each customer should be charged.

Claudine Modlin, a senior consultant at Towers Watson, laid out how far predictive analytics has advanced insurance pricing in the past decade at a recent meeting of the Casualty Actuarial Society in Phoenix.

At the end of the 20th century, Modlin said, insurers were still bound to mainframe computers and highly aggregated data sets. Rating plans were less sophisticated and it was easy for a company to understand its competitors’ plans. Also, rating plans were finalized based on the collective judgment of underwriters and actuaries, with little data-driven guidance on how and where to deviate from the expected costs.

Today, insurers use a variety of predictive analytic tools to hunt through gigabytes of data to find variables — sometimes non-intuitive ones — that offer clues to a customer’s riskiness and purchasing behavior. Generalized linear models (GLMs) have become the global industry standard for pricing segmentation.

The use of insurance credit scores has been one of the great new loss predictors over the last two decades and there is an ongoing search for the next great one. As insurers follow the information revolution, they are improving the quality and accessibility of their internal data, investigating third party data sources, and investing more computing power to harness the information.

In auto insurance, the revolution is moving even further, as insurers start to use telematics — gathering information about a customer’s driving behavior from a device attached to the vehicle. Information flows in virtually moment by moment, Modlin said.

But use of predictive models doesn’t have to end with ratemaking, said Steven Armstrong, a fellow of the Casualty Actuarial Society. Claims departments “swim” in a vast, vast pool of data that only awaits discovery — claims diaries, records on attorney involvement, and information on service providers and adjusters, according to Armstrong.

Predictive models could answer questions such as: If a damaged auto gets to the body shop a day sooner, will it affect claim severity? What sorts of claims are driving costs higher? What sorts of claims should be reported to the special investigations unit for potential fraud? Can one pick out potential fraudsters during the underwriting process?

The list of areas where actuaries could help insurers quantify and understand their operations seems limitless. “Wherever there is data, there is opportunity,” Armstrong said.

Technology is changing how the world operates. This reality is especially evident in the insurance industry.