Predictive Modeling: Where Weve Been and Where Were Going
Professionals agree that predictive modeling has changed the way policies are priced, but what other potential does it have and where does this leave actuaries?
Insurance Networking News, June 18, 2012
Predictive modeling was a point of focus as last month’s Casualty Actuarial Society Spring Meeting in Phoenix, as it has changed the way policies are priced and the way actuaries work.
At the meeting’s session “The Revolution and Evolution of Predictive Modeling,” Claudine Modlin, a senior consultant at Towers Watson, laid out how far predictive analytics has advanced insurance pricing in the past decade. 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. Rating plans were finalized based on the collective judgment of underwriters and actuaries, with little data-driven guidance in 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 hold clues to a customer’s riskiness and purchasing behavior. Generalized linear models (GLMs) have become the global industry standard for pricing segmentation. This is due in large part to the multivariate framework, the multiplicative nature of rating plans, and the high degree of transparency in the results. “As an industry, we have really learned a lot,” Modlin said. “We have advanced our toolkit.”
As much of the industry has refined its approach to estimating loss costs, the use of science to understand customer demand lags behind. GLMs are a suitable technique for this as well. The challenge here is to capture customer attributes as well as price-related information (e.g., quote offered at new business or price change offered at renewal) that will provide useful insights into customer elasticity.
During the session, Steven Armstrong, a Fellow of the Casualty Actuarial Society, laid out a variety of ways tools and skills could improve insurance operations beyond the pricing function. For example, predictive models could likely help underwriters work more efficiently. Right now, underwriting tends to follow rules with limited flexibility. For auto insurance, for example, young drivers receiving good student discounts have to regularly turn in copies of their grades. Predictive modeling could show, perhaps, that some types of students don’t need to perpetually update, while others would.
Predictive modeling could also help marketing by researching what mix of social media grows the customer base or what brand attributes drive new business. The concept isn’t new to marketers, but the actuarial skill set can enhance understanding of the work.
And claims departments “swim” in a vast, vast pool of data, Armstrong said, that only awaits discovery—claims diaries, records on attorney involvement, and information on service providers and adjusters. 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” and “What sorts of claims should be reported to the special investigations unit for potential fraud?”
So, where does this leave the actuary? Insurance industry professionals, Alice Underwood, EVP at Willis Re, Mark Vonnahme, clinical professor of finance at University of Illinois at Champaign-Urbana, and Armstrong participated on a panel at the meeting. They agreed that the profession is changing, with heightened competition arising from various sources.
While actuaries have been thought of as the numbers experts, insurers are looking deeper into their databases for a competitive edge. As a result, they’re hiring newly minted graduates with advanced math or statistics degrees who build sophisticated predictive models. But this doesn’t spell the end of actuaries. Some actuaries will become predictive modelers. But if they don’t, actuaries have a role to play.
Actuaries have professional standards and a code of ethics that non-actuaries might lack, Armstrong said. So the partnership between modelers and actuaries can be a beneficial one. Actuaries also act as intermediaries. They can develop “a way to frame a problem” that modelers can understand, Underwood said, and then help management understand the modelers’ analysis.
The panelists also concluded that actuaries play a valuable role in enterprise risk management (ERM). With their training, actuaries would seem well suited to key ERM roles, such as chief risk officer. But actuaries haven’t always been at the forefront of the new discipline, Underwood said. That may change, as the CAS becomes one of several actuarial organizations conferring the new Chartered Enterprise Risk Analyst (CERA) designation.
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