One of the best aspects of the Insurance Unplugged podcast is getting to hear how industry thought leaders and practitioners working on the front lines are navigating the challenges and opportunities of AI.
Franklin Manchester understands that continuum very well. A self-described insurance super nerd, Manchester is a former insurance agent and underwriter turned strategy lead at analytics leader SAS, where he serves as the Global Insurance Strategic Advisor.
Last fall at ITC Vegas, he was part of the pre-event “Harmonizing Sustainability Summit” with Lisa Wardlaw where he kicked off the day with a keynote to talk about how companies can use AI and generative AI capabilities in a responsible way. Recently, they caught up again on the Insurance Unplugged podcast to talk about some of the top challenges the industry is facing, from the coverage gap to the role of AI, and much more.
Here are some of the highlights from their conversation.
The Coverage Gap: How AI Can Help
The coverage gap is a $1.8 trillion problem, and it shows up in different ways. Where I’m most concerned with the coverage gap and what AI can do to help solve it is around marginalized populations. When insurers develop their pricing models, they are basing it off of historic data—so backwards looking and data sources that are not informing an outcome of prevention but informing an outcome of ‘I need to raise rates, I need to tighten my underwriting guidelines, I need to leave this market, etc.’
My belief or hope is that with artificial intelligence is that you can start bringing in novel data sources. Pulling in imagery, data on weather patterns, and pulling in the idea of predictive analytics to bring that all together to prevent a loss from happening. Now, we’re never going to prevent every loss, but what that starts doing, as you reduce some of that loss frequency and severity, it starts infusing into your pricing premium that can be a little more affordable.
So now, folks don’t have to decide between paying their insurance premium or reducing coverage vs. paying for groceries. This is the value that I see.
McKinsey has been tracking what they call an AI quotient for organizations for five or six years. They’ve determined that leaders in the insurance space are delivering six times the shareholder return of laggards. And that’s great news for all of us because it is reinforcing the business case for AI.
Thinking like, ‘it’s too expensive; I don’t understand it; I don’t have people who can use it’ are being replaced with, ‘well wait a second, if we do invest in these tools, it is going to have a return.’ The leaders are leaving the rest of the market behind because the gap has grown by over 74% between laggards and leaders. This makes for a very different conversation with decision makers. You don’t have to take my word for it.
Advice for Your AI Strategy
Don’t make assumptions about what you know. You need to understand what is actually going on at the root of your organization on the frontline. Understanding the actual problems, not the outcome of the problems.
I think that starts with culture: a listening culture and an openness that your people can come to you with these real issues and you’re actually going to do something about it.
The technology can inform the solution. It is a means to get to an outcome, but again, it goes back to identifying the problem. What is the problem you are attempting to solve? You can have the best AI, the most flashy technology, the greatest digital interface, but if it’s not actually addressing your problem and carrying forward the strategy, it’s going to fail.
The Call to Action for Insurers
Insurance is a noble profession and it’s been around for a very long time in its original form to protect people, communities and society, and billions of people are looking to insurers to help them.
So the call to action for me is very simple: We need insurers. We need regulators, we need government entities who are providing insurance coverage to all come together and step up. Because retreating from markets is not the answer.
Collaboration and cooperation will be required for us to solve these very real challenges, and how we’re looking at data, how we’re doing analysis, how we use artificial intelligence, machine learning, computer vision, synthetic data, can get us there.
