2950: Cytora - How AI is Transforming the Insurance Industry

[00:00:00] Are you curious about how AI is transforming the insurance industry and the world of insure tech? Well, today I've got a very special guest who's at the forefront of this revolution. His name's Zaheer and he's the head of North America at Cytora and he's going to be joining

[00:00:18] us today to discuss how AI-powered solutions are redefining commercial insurance underwriting. He's got experience with major insurers from Allianz, Beasley and Markel and he brings a wealth of knowledge on how tech can drive innovation in traditional industries.

[00:00:36] So in our conversation today we'll explore how AI improves transparency, addresses changing real estate valuations and the future of AI insurance. But before I get today's guest on, I want to give a big shout out to the sponsors of

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[00:02:06] So buckle up and hold on tight as I beam your ears all the way to Atlanta, Georgia, where Zaheer's waiting to join us today. So a massive warm welcome to the show. Can you tell everyone listening a little about who you are and what you do?

[00:02:22] Thanks for having me, thrilled to be here. A quick intro about myself. I joined Sitora a couple of years ago to lead our North American operations. At Sitora, we partner with leading global insurers that enable them to move from manual

[00:02:36] risk flows that consume unnecessary capacity, so think of kind of low value work, to digital risk flows where we're saying 100% of that underwriter or claim handler, their capacity is on high value activities. My specific role is working closely with these US carriers to help them achieve whatever

[00:02:56] those risk digitization objectives may be. Prior to Sitora, I was with Piscoff US, which is a commercial insurance carrier here. I led their data analytics and automation capabilities. And before that, I was at Lenovo and a little bit earlier before that was a consultant with McKinsey.

[00:03:12] So my primary focus across McKinsey, Lenovo was that intersection of technology and enablement or service-based organization. So in normal speak, that means driving operational transformations that are spurred by a new technology, right? And today is a great time to be able to do some of that stuff.

[00:03:32] So yeah, professionally, always been passionate about how technology data can drive that innovation in traditional industries. And on the personal side, I'm enjoying life in Atlanta with the wife and two young kids, the stereotypical responsible rule following firstborn and then the rebellious, but easy-going second born.

[00:03:51] Excited to chat more. I've got to ask, have you got any siblings? Where do you fit in that? That's where the stereotypes are easier to connect, right? Because I have a younger sister as well. But I think the age gap was slightly different.

[00:04:08] So there was a bit more of, built some of that responsibility, but maybe over-inflated responsibility that I believed in. And I know you've listened to the podcast before. One of the things I always try and find out more about is my guest's origin story.

[00:04:23] What lit the spark and then put them in the path that they're on today? So I've got to ask you to begin with, why are you so passionate about technology? Is there a story there? I think it's just this thread across my life, I would say.

[00:04:38] When my dad, I think the earliest memory I have of my fascination was when my dad brought home our first computer. I think it was during like the 386 times or something like that. I just remember being immediately fascinated. So the technology excitement, fascination started on the hardware front.

[00:04:56] Tinkering with the computer, swapping out components, eventually building and overclocking my own machines. I remember in my college dorm at one point, my roommate used to complain quite a bit because my computer sounded like a loud vacuum cleaner because of all the fans and modifications that I'd made.

[00:05:15] I never actually used it to its potential, but just the fact that I could do it. And this fascination naturally evolved into this obsession with gadgets, carrying around these massive pocket PCs on my belt. Definitely did not fit in always during college with that.

[00:05:32] But eventually that turned to software, it took a bit longer to develop. And I think we had the DOS and gaming days, but really it wasn't Windows, it was Encarta. Do you remember Encarta? That encyclopedia? Yeah, about 300 CDs, wasn't it? Something like that. Exactly.

[00:05:50] I think that's when I truly began to conceptually appreciate what software can do to power of software. So I was in high school, I was programming applications and websites. And as you can imagine, this led to my academia path, which was computer science across bachelor's

[00:06:06] and something related and master's as well. So continued on my career working as a programmer into the world of code, but I quickly progressed and found myself more interested in the broader impact of technology on organizations.

[00:06:21] And so that's where I went into consulting and started working at that intersection where technology is transforming businesses. Fantastic. And one of the reasons I invite you on the podcast today was to explore the world of insure tech, so much going on there at the moment.

[00:06:37] So I want to ask what is the complexity that you see in commercial insurance today? Very often people talk about technology first. I'd love to hear more about the complexity and the problems and then go into the tech. So what are you seeing here?

[00:06:50] Yeah, yeah, it's a great question. I think personal insurance, we all are more familiar with the car lines and the home property. But when you get into commercial, it's slightly different and I would say much more complex and highly manual.

[00:07:05] So therefore you have within an organization suboptimal productivity and poor service. But if we step back on what insurance is, it's fundamentally about managing risk. So either you retain the risk or you transfer the risk. But today, this risk capture process is largely analog.

[00:07:23] So you have a lot of inefficiency that's vacant in the system. So, for example, 40 percent of every dollar that a customer pays today in premiums, that actually goes to that frictional cost of the carrier onboarding that risk.

[00:07:37] So what I mean is the expenses of managing and bringing that risk on board versus actually paying claims. So if I could illustrate that complexity a bit more from a scenario, so imagine you own a chain of restaurants and you're trying to figure out the right insurance products.

[00:07:56] You have no idea what you need. So which is OK because it's a vast and broad set of products. So you go to a broker, you fill out a lengthy questionnaire. The broker then sends that questionnaire and other related documents that has claims

[00:08:09] history and the details across all your properties. The multiple carriers, sometimes this can go up to hundreds of pages. Then this is primarily through email and sometimes with APIs and portals. Then each carrier receives that, assesses the full request.

[00:08:25] And more often than not, it might not even be within AppCite, meaning they don't cover it. This is not property or coverage that they write. So it's pure wasted effort. And if it is within AppCite, it may be missing some details.

[00:08:38] So underwriters, for example, spend significant time and capacity determining whether or not this is within their AppCite and what should be prioritized. And that's that wasted capacity, which of course leads to lost productivity and leads to long turnaround times to get quotes, resulting in poor broker service, customer

[00:08:58] dissatisfaction, especially when you compare it to the traditional purchase buying process that you have with like the Amazons or something else. All of this at the end is a net impact to the customer with higher costs. So these internal productivity and capacity limitations are quite limiting for the

[00:09:16] organization. But if you just, if I pause there and I say, let's put the internal side on one angle or one side and then the other, you look at the external side. And what you'll notice, these industry trends are risk volatility is increasing, risk

[00:09:32] variation is increasing across verticals that we know, right? Like climate. And then there's new verticals coming in like cyber, etc. So the implication of this is that there is just more and more protection gaps because of

[00:09:46] these new external trends, meaning these insurance products are not being sold because they're not profitable. However, if we go back to this inefficiency, this capacity, and you could cut, let's say, 20 percent or more. Now you have products that are more profitable that could be sold.

[00:10:01] And therefore, the implication being you reduce those levels of under-insurance and helping to close the coverage gaps that exist today. But well, something I've got to bring up, of course, is AI. That's the big buzzword at the moment. So how would you say AI impacts things like underwriting?

[00:10:17] Anything you're seeing there? Oh, yeah. And that's exactly where Sitoris focuses is kind of across the underwriting claims and how we leverage AI to address all this. So if I kind of bring it back into that explanation about the complexity.

[00:10:31] Yeah. Now, if you look at it from a commercial carriers perspective, they have the opportunity to now build customer protection gaps and improve satisfaction. But to do that, that means they need to handle more volume, provide additional

[00:10:46] coverages, like I said, for these new risk verticals and make insurance more affordable. That's quite monumental. And that's where Sitoris leveraging AI comes in. So what we try to do, specifically Sitoris, is we try to make the insurance value chain

[00:11:02] much more efficient. How do you reduce the incremental cost of every new risk that a carrier has to onboard? So how do you leverage AI to help insurers do more with what they already have?

[00:11:15] And how we do that is we create what we say digital risk flows that are tailored and specific to that carrier. We do it in three different ways, and that's how AI is kind of embedded across these three areas. The first we digitize the risk.

[00:11:27] So when I spoke about these manual, complex, large documents that are coming through, we try to take that unstructured input, collecting all that necessary data and all the non-value work that consumes underwriting capacity today, and we digitize it. And we also augment that with external and internal data.

[00:11:47] And that in itself, as a first step is saving hours for that underwriter by avoiding the need to go through these documents and pages and pages. After we have that, now we can evaluate the risk that has been digitized.

[00:12:00] So what we do is we evaluate it in the context of the specific insurer's risk appetite and portfolio strategy. And so this is, again, a lot of inferring data. So oftentimes we might have some data fields that were captured when we digitized it.

[00:12:15] We tried to map that into a specific carrier taxonomy. So think of business activity. We want to know the carrier has defined these 200 different categories and 500 whatever days of business activities. How do we automatically classify it into there? Or think of claims, lost cause.

[00:12:33] But what the implication of that is now within minutes, the underwriter knows if this is a risk that's within their appetite or not. Finally, once we've evaluated it now, the risk itself is at the underwriter. Right. They know it's within appetite.

[00:12:47] They know this is something they want to write. So we try to provide the decision insights to accelerate that review process. So that's everything from kind of descriptive insights that point the underwriter to the

[00:12:58] right portion of the risk that needs attention or guidance or to more complex area like detailed analysis. How does the claim history compare to previous submissions? Compare to previous submissions, etc. Really, if you step back, what we're trying to do is we're enabling by Tora, we're

[00:13:17] enabling different pathways to these decision risks. So only deploying human intervention when it's absolutely necessary. So for an example, if something comes through and we realize it's a high complex risk and you route it to an underwriter or claim handler, while if it's a lower complexity

[00:13:33] risk and you can figure that out up front of the process, you could potentially completely bypass the human review altogether. And therefore this results in the available capacity being used for more productive tasks and they could focus on more complex decision ready risks.

[00:13:50] And then finally, the implication again goes keeps going. You get the faster turnaround times. Brokers are happier. Customers are happier. And the underwriting decisions are driving to a more superior risk selection that the carrier wants.

[00:14:03] So across those areas, I mentioned quite a bit, but we're leveraging AI across that to help go across that digitize, evaluate and decision inside journey. And that's what I love about your story here, proving that AI is more than a buzzword,

[00:14:18] you're solving real world problems, you're speeding processes up and transforming the insurance space with your work there by leveraging AI technology. But I've got to ask, where do you go from here? I mean, how do you see tech and the commercial insurance industry evolving over the

[00:14:34] next three, five or 10 years? And as I say that I know it's an impossible question to ask with just how much transformation we've seen in 18 months alone. But how do you see this progressing and evolving?

[00:14:44] And indeed, how will you continue to leverage this technology and lead the way? Yeah, it's a great question. It is and it is difficult, especially as you mentioned, right, in these past 18 months, the pace of development of things that have been going on.

[00:15:00] So if I start in 10 years, it gets quite blurry for me. But at least in the next three to five years, if you look at it from an efficiency perspective, we should be able to see a landscape where underwriters are not bogged down by these manual activities.

[00:15:14] Right. So today, the folks that we're working with are kind of the leaders ahead of the path. But by three or five years, I hope that this is across the industry where data is also flowing seamlessly across the value chain. So you can make decisions based on that.

[00:15:28] Humans that are engaged are focused solely on these informed decision making areas. From an AI standpoint, stepping back and looking at it from a tech angle, I do think that by that point in these next three to five years, we should be seeing more

[00:15:44] sophisticated insights at the point of underwriting. So when it gets there, it's already captured in a digital format. It's already evaluated from an appetite perspective. When they are making that decision, how do you accelerate? How do you help them make that decision?

[00:15:59] And I think the most interesting aspect is going to be some level of feedback groups that will be developed across these operations that leverage the AI and data capture. And what I mean by that is, how do you automatically look at that day to day human

[00:16:13] decisioning and actions that are happening all the way downstream in the underwriting and claims and change your entire process upfront and at the beginning, right? These feedback groups with AI and data will really help us, will really help these

[00:16:27] carriers and the industry overall be able to evolve more rapidly. And then that would be the implication of that is what we had chatted about earlier. You'll have insurance that's more affordable for the customers, and you'll be able to fill more of these protection gaps for the underinsured.

[00:16:43] And in a more personal note there, someone who has come from McKinsey to Sitora and transitioning from your role at McKinsey leading to going on and leading Sitora's efforts across North America. Are there any unique perspectives that you think that you might have brought to your

[00:17:01] current role, particularly in driving adoption of AI solutions in insurance? Are there a few synergies between those two worlds? Yeah. Yeah, it's a great one, right? I mean, transitioning from McKinsey to Sitora has been a lot about bringing together the best of both of these worlds.

[00:17:20] So you have that consultative approach versus the kind of startup mindset or just startup mindset of just figuring it out. But how do you combine the technology adoption with effective change management? One key perspective I could speak on is the importance of aligning the technology

[00:17:37] with the business objective. It's crucial to understand what the technology can do, but within the specific context of the organization's goals and objectives. And that is then takes it to the next level of how do you transform that into the current ways of working?

[00:17:55] So clients appreciate a more tailored approach of how to adopt, for example, Sitora within their organization versus this kind of we have a technology software, it's a cookie cutter, one size fits all. You can't take that approach anymore today, nowadays.

[00:18:09] And I think once you get that, once you have technology aligned with business objective, the next piece is that change management, which is equally as essential. And that's all about helping the organization adapt to these new ways of

[00:18:22] working and ensuring that we balance the introduction of new tech with the realities of an operational deployment. This means basic stuff like not biting off more than you can chew or avoiding the temptation to focus solely on this art of possible from day one.

[00:18:39] Instead, how do you take that step by step approach, gradually integrating these AI solutions and ensuring they are practical and effective before you take the next step? And you've also been working with some of the biggest names in insurance, too, from Allianz to Beazley and Markel.

[00:18:56] So how do you at Sitora tailor your AI solutions to meet what I would imagine to be diverse needs of such varied partners? You probably can't share too much, but are there any examples of a successful

[00:19:09] implementation that you can share just to bring to life everything we're talking about here today? You might think that large insurance carriers are difficult to change. That's the initial perspective. But several of the ones that we've worked with actually have a strong vision of what

[00:19:25] they want to achieve. And that top-down strategic vision is critical, especially when you're trying to create a large organization that drives the urgency and that drives the alignment across the organization. So we've seen impressive results in relatively short periods.

[00:19:41] I could go into a couple of let's start with one example on Markel. Yes, so they, as we talked about, had a clear business objective of bringing up this capacity across the underwriters and improving how they interact and their responsiveness with the brokers.

[00:19:58] So the first step in this, in the same vein of what we chatted about in a step-by-step manner, the first step was automate their triage process. So once they had that, then the next step was how do you understand and classify what is actually the auto appetite?

[00:20:14] And once they had that done, then they were comfortable with what was out of appetite. Then you take the next action, which is how do you auto decline some of these? So that means as it hits the door, you know that this is not part of your risk

[00:20:25] appetite and you feel comfortable to automatically decline that to the discussion we had earlier. No human touch at all in some cases. This was over the course of a year, but the result was pretty significant. We had 100 percent increase in underwriting productivity.

[00:20:42] So what that means is that the underwriters were able to write double the premium that they were writing today. And there was a reduction in the quote turnaround time. So instead of taking days for a quote to get back to the broker, to reach out to the

[00:20:56] customer, it was within a couple hours. Well, that's absolutely phenomenal. And we started our conversation today talking about your origin story. What excites you about tech? And we've now almost come full circle.

[00:21:11] So I'm going to take you back in time one more time and ask you what is the one insight or piece of wisdom that you wish you'd maybe known when you were starting out? What is that one thing?

[00:21:22] Can you remember what that would be or something that you would advise? Yeah, that's a great question. I think the biggest piece that comes to my head is and this takes time to learn. So it's a bit about knowing and understanding, which are two different points. Right. Yeah.

[00:21:42] So during McKinsey, I remember I was quite driven by the end objective. Right. You join in on these projects and say, what am I going to accomplish? What impact am I going to help the organization make? And you're so driven by that.

[00:21:56] But stepping back and after going through personal life changes and professional everything, the one insight I wish I had known, but also, like I said, understood earlier, is you got to enjoy the process, not just focus on the end result.

[00:22:11] It's so easy to get caught up in achieving specific milestones or reaching particular goals. But the true value, as cliche as it sounds, is in the journey itself, embracing those learnings, the challenges, the growth that comes with

[00:22:26] each step. And that's where maybe I'm starting to show my age at this sort of comment, but that's really what is the new driver and I have a new appreciation for now. They get Ralph Waldo Emerson once said, right? Life is a journey, not a destination.

[00:22:43] I think that's a beautiful moment to end on today. But before I do let you go, if anyone listening wants to find out more information about you or ask your team a question or just find out more information about

[00:22:55] Saitora, is there any way you'd like to point everyone listening just to find out more? Yeah, of course. Saitora.com. So you could definitely go through there, reach out to me directly, whatever it is, happy to connect, happy to explain more. Yeah.

[00:23:11] Well, again, thank you so much for joining me today. Saitora is a leading provider in AI powered solutions for the insurance industry, but it was great to take a peek behind the curtain and understand how AI is being used to improve transparency, efficiency and AI powered insurance

[00:23:27] processes. So much gold in there. But just thank you for taking the time to sit down with me today. Thank you, Neil. Appreciate that. Have a good one. So as we wrap up our discussion with Zahir here today, I think it's clear that

[00:23:38] AI is set to revolutionise the insurance industry, making processes more efficient and transparent. In fact, it's already doing that. And Saitora's innovative solutions are also paving the way for a future where underwriters can focus on high value tasks, ultimately leading to more

[00:23:55] affordable insurance and closing some of those protection gaps that Zahir highlighted. And I think we also talked about some of the challenges and opportunities in equal measure and how AI powered insurance has been invaluable in implementing some of those improvements in those areas.

[00:24:12] But what do you think about AI insurance? Good, bad, indifferent? How do you see these technologies impacting other traditional industries? Maybe you are in one of those traditional industries and want to share your story. Please let's keep this conversation going.

[00:24:27] Tech blog writer at Outlook.com, LinkedIn, Axe, Instagram, all the usual places just look for AXE. But I'm afraid that's it for today's episode. Hopefully you'll join me again tomorrow for another chat about another industry,

[00:24:40] but thank you for listening today and until next time, don't be a stranger.