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Analysts Vs. Actuaries: Managing the Conflict
March 31, 2016 News

Insurance companies are investing heavily in data science and analytic functions to find an edge, but some within the enterprise are feeling left out as a result.

In its “Data Science and Actuarial: Managing Potential Conflict” brief, Novarica says that actuarial functions who had traditionally been the keepers of data models are finding themselves at odds with the new entrants.

There are several reasons for the conflict, says study author Mitch Wein, who interviewed several carrier representatives on the analyst firm’s Research Council to gather information for the brief. First, though actuarial science, like data science, is based on drawing conclusions about future risk based on certain information, the practices come from different underlying assumptions about the nature of the input.

“Actuarial science is based on a constraint around the availability of data: You can’t know everything about all the possible data points you can gather to understand and price risk, so you need statistical methods to predict it,” Wein explains. “But data science sees data as very available, and even though it’s large volumes of data it can be processed and interpreted.”

That different view is reflected in several other ways. Actuaries tend to have bachelor’s degrees in statistics or economics, and then certifications from actuarial societies, and have been trained on legacy technology like C++ or spreadsheets. Data scientists often have advanced degrees in technology disciplines and are using new technologies like Python and R.

“So you have these two groups, they’re culturally different and have different training. How do you bring them together?” Wein asks.

Wein suggests a system of “enforced collaboration” centered on transparency in business responsibilities and processes. Insurers should work to integrate data scientists into all business areas – including actuarial, but also claims, marketing, underwriting and more – while creating clear responsibilities for each group.

For example, actuarial science is required for regulatory reporting. Therefore, actuaries can continue working to their strengths in rate reserving and reporting while data scientists work on identifying new uses for big data.

“Actuarial science isn’t going away, and the idea is to get better and better and pricing by supplementing with data science, so they complement each other,” Wein says. “The key is having a committee of senior execs with ownership over data. Once you establish that organizational control over data, you can talk about how you want to structure.”

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