Transforming Life Insurance with AI and Predictive Analytics
The name of the game in life insurance is not just revenue growth but also operational efficiency, and AI and predictive analytics are making both possible. These technologies are automating routine tasks, identifying production bottlenecks, and personalizing customer interactions to streamline operations and secure customer loyalty. In this week’s podcast, Atidot founder and CEO Dror Katzav joins host Ankush Koul, Sapiens’ Data Insurance Practice Director, to explain why AI and predictive analytics are the tools that insurers need to navigate and succeed in today’s life insurance industry.
Ankush Koul|Dror Katzav
Ankush Koul: Hello! Welcome to the Sapiens Insurance 360 podcast. I’m your host, Ankush Koul, Data Insurance Practice Director at Sapiens, and I’m so glad that you’re out there listening; this is where we discuss the latest news, trends, and issues from across the insurance industry.
Would you believe that the life insurance business was once thought of as staid and conservative? With the growth of AI and predictive analytics, today’s life insurance industry is anything but. So how have these technologies transformed today’s life insurance industry? And how do they enable insurers to navigate the evolving insurance landscape, adapt to changing customer expectations, and make data-driven decisions for sustainable growth? That’s what we’ll be discussing in today’s episode, “Transforming Life Insurance with AI and Predictive Analytics.” And to help answer these questions is our very special guest, Dror Katzav, Founder and CEO of Atidot, an AI cloud-based platform. Prior to founding Atidot, Dror completed an 11-year career as a Team Leader & Project Manager with an elite IDF (Israeli Defense Force) Unit, leading state-of-the-art technology development in intelligence and analytics. After his military career, he brought his knowledge and experience to the commercial world as a consultant for many Israeli startups.
Dror, welcome to the program!
Dror Katzav: Thanks for having me, Ankush! Happy to be joining you today!
Ankush Koul: So Dror, let’s begin. The question on everyone’s mind: How is AI helping improve operational efficiencies within the life insurance market?
Dror Katzav: I think in the life insurance industry, AI and predictive analytics have been a trend for some time and it’s definitely picking up in the last year, two years. And so with this release of new technologies, insurance companies are already trying to be in the forefront of R&D and using these capabilities in order to improve their operation. What we see is that we’re in an industry where people buy their policies from agents and there is a turnover in the agency business. So you end up in this situation where customers own these complex products [that] they did not necessarily understand. Insurance companies never built the services to provide to these customers and the agents have already gone. And then you miss a lot of these opportunities to drive better engagement with these customers through retention, upsell, cross-sell, providing new products, and so on. And so in order to do so at scale, you have to have the AI and data and predictive analytics to understand the customer better and understand what to be, what needs to be presented to them and when, in order to drive this efficiencies on both in-force as well as new business and underwriting and drive better recommendations to these customers where they are in order to provide better service.
Ankush Koul: Thanks for that, Dror. So if we were to further expand on this, AI, as you rightly point out, has automated a lot of these activities, some of which were obviously done by agents, some [by] underwriters. The question that I really want to drill deeper into is what are some of the routine tasks such as data entry, policy issuance, and claims processing, that AI has made a huge impact in terms of automation?
Dror Katzav: I think one of the areas in which this has made a significant improvement, AI specifically and automation, is by being able to provide some of these digital journeys [to discuss]. We’re definitely in a world where, you know, post-COVID, where people started buying more policies online, they started using e-apps, started using more digital channels, we see some of the insurtech players on the P&C side, on the life side, providing some of these capabilities on a self-serve basis. In order to do that, and not rely on human entry, on the data side, on reviewing data, on analyzing the data, both new business as well as in claims, you have to have data and automation to transfer the data, [the] minds today to understand the data better, in order to know if that if you received what you needed to receive, and do it at the right scale and the right level to be able to provide this type of service to these customers.
Ankush Koul: Thanks for that, Dror. Just picking up on one of your points around over the last couple of years and obviously with the COVID as well, there’s been a higher adoption of digital channels by consumers. So one of the things I would like to understand from you is how do you think data and AI is helping personalize that experience for the customers, which otherwise, before COVID, tended to be more agent-specific, if you would, you would have agents calling up the customers and having those interactions. But in the new normal, if you will, after COVID, how do you think that interaction has evolved, particularly with leveraging data and AI capabilities?
Dror Katzav: So I’ll give an example. We do a lot of work on in-force and in-force, one of the challenges is that you see certain behaviors, say somebody not paying their next premium. These behaviors, if you don’t know how to differentiate them, you treat them all equally. And if you treat them all equally, you lose all of these customers because some are not going to pay the premium because they need the money. And some are not going to pay a premium because they’re now shopping for a better product. Maybe during COVID, they bought what could be done without, you know, meeting a physician. And now they’re looking for something that is fully underated and has different traits. And if you are able to understand with the data and AI and do the segmentation and say, oh, this type of customer needs, this type of thing, this customer needs the money, that customer is shopping for different products, you can put them on different journeys. One journey is more around how can we provide you benefits that would allow you to get some cash out of the policy? They can give you a loan, get a premium suspension, etc., etc. And the other one is focus more on the benefits you get from the policy and how you could easily upsell within the family. You could gain infinitely more value and retain these two customers instead of losing them both.
Ankush Koul: Brilliant. Thanks for that, Dror. Now just moving our conversation further, I’ve wanted to focus a little bit on underwriting challenges that the insurers face and how AI and predictive analytics can help address those challenges within the underwriting process.
Dror Katzav: So underwriting is a super interesting concept here, because different people buy policies for different reasons and some, you know, they bought it for an estate planning or for some tax planning or something. And it doesn’t really matter how much time it’s going to take for the policy to be underwritten. For some people, the fact that it takes 20, 30, 40, 50 days, even more. You know, it is what it is. But [for] some it definitely matters. They bought the policy, they understood the need, understood what they were going to pay for it, and so on and so forth. And now between the point [that] they apply for the policy and the point to get it back, it could take easily 30, 40, 50 days. And in these days, they’re seeing competitive offers. They see other things that they could do with this money. The money might not be there when you come back to collect the premium. And this is all because it takes us more time to underwrite a policy. And if we’re able to reduce the cycle time, you increase the probability of these policyholders buying those policies, substantially. And we definitely spend a lot of time trying to understand how much time this policy is going to take to be underwritten and what’s the impact on conversion. If I can reduce the cycle time, would it improve the placement ratios? And what is driving this increase in cycle time? Is this because the product is just because [of] the process? Is this too many requirements, not enough requirements that are being asked, maybe its the agent, maybe all of my issues were with the same distributor, maybe it’s on me because, you know, I’m seeing issues all across the board and using these analytics to be able to stack cases based on, not based on who is taking the longest, but who cares the most about the extra day that it would take, allows companies to increase their patient ratio and being more efficient with returning to results as they’re needed, back to the customer.
Ankush Koul: Thanks. That was quite interesting, Dror. We have seen what some of our customers, you know, automated underwriting is a huge differentiator. So the Sapiens platform and obviously added on AI models [are] helping our customers reduce those cycle times. I think we have time for one last question. Does it take a lot of time for underwriters and adjusters, particularly the ones with a lot of experience who have been in the industry for, let’s say, 15, 20, 25 years, to gain an understanding of and trust in these new technologies? How can insurers help ease what may be a pretty steep learning curve when it comes to embedding of AI in these processes?
Dror Katzav: I think that the last few years with the introduction of more tools, get people more comfortable with AI. Now that we have ChatGPT and Midjourney and all of these tools, people get more familiarized with AI and start to understand it better. Similarly, regulators start creating regulation that makes things more clear to what you can do and what it cannot, such as the regulation coming out of Colorado and coming out of New York and some other places, the NAIC model standards and so on and so forth. Now on top of all of that, part of the beauty is in a partnership like the partnership where with Sapiens, is that if the AI is embedded in the platform, you don’t necessarily have to know and care about it. If I can tell you who is more likely to be under it and less likely to be under it and cycle time impact, etc., etc., and all of these insights automatically integrate into the Sapiens workbench and automatically change the priorities, change the staffing, change the assignment of cases allows to create these efficiencies automatically while the underwriters themselves, they do the same thing they’re used to do without knowing that the AI behind the scenes shuffle the cases in order to create overall better service and better performance, without them needing to use this tool themselves.
Ankush Koul: Thank you, Dror. It was wonderful hearing your insight, particularly how AI can be leveraged within the insurance industry across a range of functions, [such as] underwriting, customer experience, claims, and also using it to drive operational efficiencies. I know I speak for our listeners when I say I can’t wait to see how AI and predictive analytics will continue to transform operations within the life insurance market and help drive profitable growth.
To our listeners, as always, thank you so much for spending your time with us today. 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 on our channels. Don’t forget to subscribe to the podcast and thank you once again for listening. We’ve got more coming, so stay tuned for our next episode of Sapiens Insurance 360.
