Minimizing risk using analytics
By definition, actuaries deal with data, but the insurance industry isn’t known for being on technology’s cutting edge. An actuary since graduating from Northwestern’s Weinberg College in 2005 with a degree in math and economics, Kim Lacker decided to explore predictive analytics when her company, CNA, wanted to find out more.
“My career was definitely moving in the direction of predictive analytics,” she says. “It was important to understand its value to the insurance industry and implement these types of models.”
Lacker earned her MS in Predictive Analytics in 2013 and currently works in the healthcare segment, providing insurance to doctors, dentists, nursing homes, and hospitals. Predictive analytics allows the company to leverage its vast amount of internal data and knowledge, combine it with data from other sources, and develop appropriate insurance prices.
“Using more advanced analytics, when you look at two different hospitals you can understand how risky each one is and how much loss we expect them to have based on their specific characteristics,” she explains.
CNA recently built a new predictive model to help drive how it prices insurance for nursing homes based on the risk characteristics inherent in that type of facility. “It gives us a better ability to understand what’s driving losses and can also help our customers change those factors if they’re controllable,” she says.
An important lesson for Lacker was how to capitalize on her industry expertise when developing models.
“You can develop something that’s statistically excellent, but if you don’t incorporate something that makes sense to your business, you’re not going to have much success,” she says.
“One of the key takeaways for me is my ability to understand the more complicated math,” Lacker says. “The power of predictive analytics helps me influence colleagues to adopt this new way of thinking. I serve as that intermediary, because I gained the expertise that allows me to drive the implementation of these models across the company.”
Even with that progress, Lacker thinks the industry is just scratching the surface of predictive analytics. “There’s just so much data. As a first step we’re focused on risk analytics, but thinking beyond that, into web and social media analytics — maybe we’re starting, but we definitely haven’t reached our full potential. There’s a lot more that we’ll be able to leverage.”