Q&A: Nationwide Chief Analytics Officer Shannon Terry Talks About How AI/ML is Transforming Insurance

In 2010 we had a team at Nationwide that began working on data science, which included artificial intelligence & machine learning (AI/ML). At this time, data science was starting to enter the workforce more broadly and across different industries. By 2016 data science was becoming essential to our industry and was appearing across popular media.   

If you think about Nationwide’s business for the past 99 years, our products are built on data and math, so it’s really no surprise that we got in early with this technology. The products we sell are, in a sense, virtual and built from constructs of how we assess and price risk. Given that, it was natural for Nationwide to explore AI/ML. The emergence of cloud computing makes AI/ML more ubiquitous and available, allowing us to tap into and analyze decades of data to find solutions.  

The Enterprise Analytics Office (EAO) is a team of data scientists that consists of more than a hundred seasoned practitioners, some of whom have actuarial credentials, and many of them have PhDs. They come from different disciplines with focuses that range from mathematics and statistics to behavioral psychology and engineering. Our EAO team partners with many other teams across the entire Nationwide enterprise including all our business units (that drive our priorities) and key delivery partners in both Technology and Enterprise Innovation and Digital.     When you’re building technology tools, humans must always be in the loop and in control. We have people with skills across multiple disciplines because we’re looking at ways to apply AI/ML to various roles. Leading with customer outcomes in mind, the various perspectives help us achieve this and carefully contemplate the things we could do with the technology versus what we should do when deciding the solution. This can range from how we look at quoting our products all the way through how we evaluate and settle an insurance claim. 

The financial services business is heavily regulated and financial professionals need to be licensed to sell and service these products. They can have obligations and responsibilities to make sure the products are right for their clients, and this makes accurate information retrieval critical.    

When it comes to financial services and generative AI, of course the concern is accuracy, and it is the paramount focus. At Nationwide, our internal sales teams are using AI to assist financial professionals by finding accurate information more quickly, which improves advisor performance by delivering a better customer experience. For example, we want to make it easy for our internal sales associates to quickly locate clear explanations of product features and riders that might be available and appropriate – such as a death benefit option – with respect to one of our many product offerings or perhaps locate current index offerings for a fixed product.  

One way to think about it is not replacing work, but rather making work a little more fluid and faster. The technology can make it easier to access a wealth of information and details across many different documents and formats.  

When we think about life insurance, it’s a complex product that has hundreds of variables that need to be evaluated. At Nationwide, we are a protection company, and we want to help our customers get the best protection for their situation and make it easy for them to do so. We leverage data our applicants authorize us to use to potentially accelerate their application upon review.  

Because we use advanced analytics including AI/ML to review the applicant’s data quickly and couple that with our skilled underwriting professionals that adhere to professional standards, we can sometimes accelerate decisions for customers with a “fluid-less” experience that does not require a medical exam, or the collection of bodily fluid as traditional life insurance does. When individuals need life insurance, we want them to have a great experience. An experience that is faster and easier, with faster underwriting decisions and policy pricing is what our customers and distribution partners expect from us. 

If you think about the wealth of history and expertise that we have at this nearly 100-year-old company and you bring that together and provision everybody with this technology, it can be something that will make your life a little bit easier and speed some things up. It can be thought of as sharing that wealth of collective knowledge, and we see it as a marriage between the best of AI/ML with the best of our human skills.   

For a customer, when you are filing an insurance claim at the time of a loss, you want to interact with someone with good empathy and listening skills (which technology does not provide). You also want them to have access to help without being encumbered by looking things up or searching for the right information and digesting it.   

Our distribution partners choose to do business with Nationwide. If we can use this technology to be easier to do business with, that makes our brand more appealing for these partners to recommend Nationwide products to protect their clients.   

We want all our associates to be equipped with the best information to serve all these groups of people. We also want to offer ways for our employees to show up as the best humans, while being able to access the best math and data we have. What's going to happen is humans are going to have better tools to work with to make both daily and strategic decisions. It's going to make your life a little bit easier sometimes and speed things up.  

I think you're going to see AI/ML further integrated into how we do business across the entire insurance and financial service value chain and it’s going to seem normal. I always like to say, good AI doesn't need to announce its presence to the world. It actually works best when it's in the background, just helping us do what we do.  

How I see it impacting professionals in our industry, from underwriters to actuaries and everyone in between, is that they will continue to do the work they do today, but the technology will advance to make our work easier and more efficient. For example, there will be quicker access to information, less switching between screens, shorter hold times for a customer. All these things will naturally speed up and get a little bit more effortless. Empathy, relationships, and the human connection will become even more important. We’ll let the computers do what the computers are best at while we do what humans are best at—I think that is where it goes.