The Next Phase of AI-Powered Life Insurance

Jennifer Smith: Hello! Welcome to the Sapiens Insurance 360 podcast. I’m your host, Jennifer Smith, Vice President of Product Strategy for Sapiens’ North American Life & Annuity division, and I’m so glad that you’re taking a few minutes out to listen. This is where we discuss the latest news, trends, and issues from across the insurance solutions and technology spectrum. Today, we’re going to keep the buzz of AI alive. AI has been a game changer for life insurance, from analyzing data sets, to creating personalized risk assessments, to developing more responsive and fair policies based on real-time data. The partnership between Sapiens and Atidot, an AI cloud-based platform, helps insurers integrate AI-based solutions into their core systems to reduce operating costs and uncover or discover new business value.

And since we’re now officially halfway through 2025, it’s a great time to ask what’s next for AI-powered life insurance? That’s the topic of today’s episode, and with me to answer this important question is return guest Dror Katsav, founder and CEO of Atidot.

Dror, great to have you back on the show!

Dror Katzav: Thanks, Jennifer. Always happy to be here. Great to be back!

Jennifer Smith: Well, we have so much to talk about today, so let’s just jump in and get started. Dror, can you explain what we’re seeing now in AI, what advances we can expect in the future, and why should insurers care about it?

Dror Katzav: It’s a great question. AI has been around for many years now, but we are seeing a significant change that happened in the last couple of years, compared to what we’ve seen before. We always had Netflix help us find the next movie to watch, and Spotify help us find the next song to listen to. Google maps helped us find the best route to drive from point A to point B, so we’ve had AI in our day-to-day lives since the early 2000s. But what happened in the last few years is that we got to this tipping point where there’s enough computer power, there’s enough storage, and there is enough data that AI has turned into something that is a little bit more than that. And we’ve seen that with the adoption of ChatGPT, and the like with generating images and generating text and generating capability, new capabilities that AI can bring into the table. The turning from a technology that is hidden in everybody’s devices to something that everybody is using on a day-to-day basis, and very aware of using AI. And we’re at the point where this AI is already on par with the performance of a human being. So we’re already at a point where ChatGPT can write text that is almost as good as a content writer. And image generation tools generate images that look like, still like a first sketch, but look like a very good first sketch of, of the graphic designer. And the code generation produces a code that is not like a super sophisticated, system architect would have generated, but it’s pretty good already. So we’re at the point where the AI is actually producing content and producing recommendations that are as good as a human being. And I think now we’re entering into this new era that is going to be even more exciting with regards to AI and the use of data. And across our lives, and especially for insurance.

Jennifer Smith: I absolutely agree with you, Dror. It’s been really fascinating to see some of the things that have been going on. And I mean, I know my kids and myself, I’m starting to use it more and more on a regular basis. It’s really becoming more of the norm. So what do you think right now we’re seeing as challenges, particularly with insurance companies? And what kinds of opportunities do you think exist in the traditional agent-led insurance sales cycle? And some of the solutions that we maybe need to consider for the hurdles that we’re seeing insurance carriers go through?

Dror Katzav: So I think insurance companies for years have always been adopting new technologies to adjust to new models. In insurance companies, you used to be one of the first ones to bring in statistics and probability. And then, you know, computers and so on and so forth. And I think by adopting AI, we’re going to see a new wave of how insurance is being done, it’s going to be very different than what we’ve seen so far.

I don’t know exactly how it’s going to look like yet, but I think insurance companies have to be in the forefront, because the ones that would understand first how to use AI for their needs are going to be the winners here. And there’s definitely going to be winners and losers, especially when you think about the current journey, the current buying journey, the buying journey that starts from the kitchen table and, the paper apps and even some of the e-apps, we’re getting used to [an] experience that is very different from the ChatGPTs of the world, Gemini, etc., that, you know, continuing with the same approach would work and would work for a few years. But it’s going to be slowly decaying while other approaches would emerge. I don’t know if you ever tried asking ChatGPT on your life insurance situation and what policy they would recommend to you. It’s not good yet, but it’s already pretty remarkable on the AI’s ability to read an illustration, AI’s ability to talk you through some of the concepts like, the IOL, second guarantees. The text part of it is pretty good. The math is not there. But these tools, evolve so quickly that eventually we’ll find ourselves in a world where, people don’t necessarily go to websites, when they can go to those tools and go through them and people will start expecting these experiences, where things are more conversational and things are more interactive, and relying solely on that distribution partner to bring in the business, it is going to be, is going to be very hard. Not to mention that the agents themselves, also people that live in the world and get used to these new experiences and are also expecting for these experiences to be more smooth and efficient and automated and personalized for themselves, as you know, part of the ecosystem.

Jennifer Smith: Do you think, Dror,  going off script a little bit here, that there is an opportunity to take some baby steps there and, kind of start with trying to encourage the traditional agents to really work with AI and more normalize it that way, and then learn from that, so that we can start to look at that kind of robo-advisor concept?

Dror Katzav: I think so, we’ve seen two examples, in the last..I have two examples from this morning, from conversations we had with different carriers. The first one was with a carrier where, what we’re helping them to do is take the data, break it into finer, tighter segments. So instead of sending one piece of marketing to all of the policyholders, they’re now able to send personalized messages to cohorts of people. And because of that, they get much better engagement. And because of that, those people get messages that are more targeted to them and more personalized and more appropriate to what they’re looking for. So that’s one way to get around this. The other way is, and we’ve seen that with another carrier, we had this conversation where they were saying, you know, the executives in the company and the mid-level management, they keep running into all of these problems with managing internal efficiencies. But when they come and ask those questions, they keep seeing that, the AI has already identified this as a probable issue and flagged that, you know, in a report that they get on a weekly basis with potential issues that might emerge this week. So when the companies will get more adjusted and companies that do get more adjusted to using these predictive reports and using these predictive segments and AI-generated content and so on and so forth, to know what’s behind the curve, they would be way more efficient.

And it’s a great way to ease our way into this. If I know, for example, which agents might have an influx of policies in the next couple of weeks and which agents might have a lapse issue. And what could be a good way to overcome that? And so on and so forth. We could be providing better service to the agents or to the customers, or to the internal team, and through that, get them more comfortable with us, providing them [with] AI insights on a regular basis. And ease our way into this.

Jennifer Smith: Yeah, really good insight, Dror.  I think that’s very, relatable for a lot of the insurance carriers who are looking for ways to help to, you know, gain more traction, get more business, and ultimately reduce the protection gap that’s out there and cover more people more effectively and efficiently. And as you mentioned, it really does come down to the data and the data that the insurance companies have had for, you know, decades and decades. We’ve been so data rich, but so information poor, and they’ve struggled to use it effectively. How would you say Atidot’s approach can change the game to utilizing data and AI? How can it solve the issue of how to efficiently and effectively use the data?

Dror Katzav: So first let’s take a half-step back and talk about what’s our approach. Our approach has been to go industrywide. So we work with different carriers and we work with different carriersand we work with different product lines. So term-life, whole-life, universal life and MYGA and fixed index and so on and so forth. And across these areas. we’ve seen no more than 60 million policies. I think the number today stands on 55; [it] depends on when we release this podcast. And because of that, because seeing 55 million lives across different situations, what policy did you take when you took this policy? How frequently did you pay the premium? Did you have premium gaps? Did you have payment issues? Did you exercise the rider? Did you decide to take partial surrender? Did you decide to lapse? And so on and so forth. Our models are very effective in providing insights that the carriers would not be able to produce themselves. So we come in and we already have a good understanding to what data points we might need. So it’s not, give us everything. Obviously as a data company, it’s always give us everything, but what we know what we want and we know what we can get the most use out of. We are industry-specific, so we can avoid some of the privacy and security challenges around that. And then we’ll produce insights that are very accurate. And I think that’s the biggest piece here, because although insurance is very much about statistics when it comes to engagement, people are still very sensitive on being able to make decisions, reach out to people, or give people recommendations when it’s very much statistics based. So if I’m able to produce insights of who is likely to lapse, who is likely to purchase, who’s likely to be underwriting at the 20% hit rate, or that 90% hit rate, it’s a very different story. And although, you know, we’re dealing with a world where you know, everything about insurance is statistics, still being able to get to this 80, 90% hit rate benchmarks, enable companies to feel more comfortable to go and engage in this, on these activities.

Jennifer Smith: Then position Atidot’s AI tools to insurers who are saying, there’s no way I could do that. I’m on these old legacy policy admin systems. And it’s going to just take forever to get this approved. How would you approach that with those, all those companies that just don’t think they’re able to do it?

Dror Katzav: So that’s exactly the reason why we build partnerships. We deal only with insurance companies. And as you can imagine, the tech stack is a nice hybrid of super modern and also super antiquated. And the way we approach this is through partnerships like this one, where we come in and tell the company, you know what? What if you don’t need to think about where the data is and where the integration is and how the infrastructure works, leave it for us, Sapiens and Atidot, to figure that out, understand where the data is coming from, where it needs to go to, how the insights can get back into your workflow within the policy admin or the underwriting benchmark or, the underwriting workbench, or the e-app solution, or whatever that is. Leave it to us. We’ll figure it out. We’ll take the data, put it back in exactly where you need it, and don’t think about integration and all of these pieces. Obviously, it’s hard, but between the two tech companies, it’s a much easier solve than having the carrier be in the middle. And also for us, you know, we build it once and we can redo it, you know, multiple times rather than come in and figure out what the, what the situation is every time from the start.

Jennifer Smith: Absolutely, [it] certainly is good to have a good partnership and for integrations. I know we’re running short on time. But I’m just curious to know around some of the results that you’ve seen with the insurers who’ve used predictive analytics and AI in their workflows. Can you comment on any of those types of results?

Dror Katzav: Yeah, 100%. We’ve seen that companies that use that to drive point-of-sale recommendation increase their conversion rate at the top of the funnel by 30%, meaning that, the likelihood of a person to actually end up buying the policy and paying the first premium increases dramatically. Through the work we do with Sapiens on UnderwritingPro, we’re able to improve efficiency dramatically for the underwriters that are using the platform, because we identify where the bottlenecks would be and help the company drive people into the right places. So we address those bottlenecks and shorten the cycle times on app underwriting. On the enforce side, we help companies reduce dramatically their lapse rates, all the way up to [a] 60% retention rate by being able to identify who is lapsing, why they’re lapsing, when they’re lapsing, how to engage with them, with which content, and help them drive this opportunity to improve, improve efficiency. So across the board, there are great results on improving, improving efficiency, as well as generating new revenue either from driving retention or driving, new app or upselling cross-sell across the board.

Jennifer Smith: Thanks so much, Dror. We really appreciate you coming back on the show to share your thoughts. AI and innovation is just such a fascinating topic right now. And when you couple that with what it can do for life insurance carriers, well, we obviously could have a whole other podcast on this topic and continue on as I’m sure we will.

So you’ll just have to come back again for another episode in maybe six months or so to cover the latest developments and what’s going on with Atidot. To our listeners, as always, we love hearing from you. So if you have any comments or would like to follow us on social media, please reach out to us and our channels and don’t forget to subscribe to the podcast. We’ve got so much more coming, so stay tuned to our Sapiens Insurance 360 podcast. Bye for now!