Aflac aims to get the most out of AI for customers and claims

Before AI, our claims representatives would review every single claim and form. It would be submitted, maybe digitally, maybe not. Those would be reviewed one by one, eyes on glass, reviewing every item, every field of a claim. Now that process is much more automated, and our claims representatives are basically doing a final check for accuracy on a lot of our claims today. Think about all that time spent on the screen. That's really been shortened and so our time to pay for our claimants is drastically reduced. 

We have seen a direct correlation between the filing of a claim, the time to pay that claim and our customer staying with us. We have about 46% of our claims that are actually fully automated or what we call straight through processing, primarily in the area of wellness claims [which are rewards for regular checkups and preventive care].

About 49% of our manual claims volume today results in less complex claims with lower payout. That represents about 5% of our claims payouts. For all of these simple claims that don't require proof of loss, like our wellness claims, for example, our goal was to pay those as quickly as possible, so our customers can see very rapid value. We really don't want our specialists spending a lot of time on those. The goal is to free up specialists so that when they're dealing with claimants or policyholders that may have a more severe health-related issue, they can spend more time where insureds actually need to speak with someone in a more complex, challenging situation. 

Now we're actually rolling that out across multiple lines of business, so it is moving to a point of scale. We're also beginning to see our expected business benefits and value.

One area is the digital service center, focusing on our customer data and improving our overall customer service experience. We have a lot of internal data. We paired that with AI and machine learning to analyze that customer data in real time so that we can identify trends and then make more personalized recommendations. Those help improve that overall customer experience and overall operational efficiency. 

AI/ML can implement sentiment analysis to proactively tell that a customer is showing signs that they may be choosing to cancel a policy, based on interactions in conversations. We focus on customer persistence. We want those policyholders and those customers to persist and remain customers at Aflac so that we can continue serving them and giving them the value that they need. 

On the sales side, we elevated a guided selling tool that would assist customers in determining which products would best target and fit their needs. We did it on things such as a look-alike model, product attributes -- knowing that our agents who were very accustomed to being in person sitting across the desk doing that exchange with an individual, wouldn't be there. So we tried to mimic that desk experience by giving them those options. 

I have a team that works across the organization and they engage very directly with our business to define those customer journeys. They do everything from the journey experience all the way through the research design, UX as well. We go past just reacting in our communication, really focusing on proactive communication, pre-emptive personalized engagement at scale.

Our biggest focus in the next year is scaling the existing capability because we see great value. We're scaling some of our existing models to the larger ecosystem and across the other lines of business, including Japan. Our team is looking to drive value to our customers, starting with our enrollment experience. We're starting to segment pieces of our customer lifecycle journey where we can be more effective and efficient. We will have several of those stood up so that we can really focus on driving that value to improve our overall customer experience.