I recently wrote a news story for insurancenetworking.com that struck a chord with more people in the industry than I expected, becoming the most-shared news story from our site in June. While I take great pride in our work here, the popularity of this specific story produced a flurry of theories.
The lead behind the story "Predictive Modeling: Where We've Been and Where We're Going," came from the Casualty Actuarial Society's (CAS) Spring Meeting. 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, insurers were still bound to mainframe computers and highly aggregated data sets, she said. 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.
But a lot has changed. 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. The industry has advanced its toolkit, Modlin said.
Sure, this is all great and promising for the industry, but—and I'm positive this is what struck that chord in my story—is this the end of actuaries? Are recent advanced math or statistics graduates who build sophisticated predictive models the new risk assessment experts for the industry? No. Actuaries have skills, training and rare knowledge needed in the industry; they will just have to readjust it. Some actuaries will become predictive modelers. If they don't, actuaries still have a significant role to play in the process.
Alice Underwood, EVP at Willis Re, took to the stage at the CAS event to speak to this transition. Actuaries and modelers will need to create a working relationship, wherein actuaries can act as intermediaries. Actuaries can develop "a way to frame a problem" that modelers can understand, and then help management understand the modelers' analysis. Underwood also pointed to enterprise risk management (ERM) as a potential focus for actuaries. With their training, actuaries would seem well-suited to key ERM roles, such as chief risk officer.
So, no, predictive models/analytics don't signal the end of the actuary as we know it; it's a call to action for actuaries to adapt to a changing role. They'll need to step out of their comfort zone and tout their knowledge and skills. They will need to make it known that they're needed to interpret predictive modeling results. It doesn't stop with actuaries though. Organizations will need to take notice of actuaries' capabilities and re-evaluate, create and foster these new roles.
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