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Reimagining actuarial science with AI · Practical AI

• 0:04
  Jerod: Welcome to the Practical AI podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, X, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm.
• 0:36
  Now, onto the show.
• 0:48
  Chris: Welcome to another episode of the Practical AI podcast. My name is Chris Benson, your co host. And with me today, we have Igor Nikotin, who is the CEO and co founder of NICE Technologies. Welcome to the show, Igor.
• 1:02
  Igor: Thank you, Chris.
• 1:04
  Chris: So one of the things that we love doing on the show is to get into different industries and explore how different industries are implementing and using AI in different ways. And you are an actuary, or at least the CEO of an actuarial modeling company. And that, we may have certain folks in the audience who are not intimately familiar with your industry, and I was wondering if we could start with, kinda tell us a little bit about what your industry is, as well as kind of how you got into it, and then kind of into why the technology around it specifically?
• 1:39
  Igor: Sure. So Actuary, not to be confused with ACTR, is a risk management professional. We work at insurance companies, and we are the people who figure out how much insurance costs, what are the terms of insurance, and various aspects of managing insurance companies. So if you go to any insurance company, people there would tell you that it is really run by actuaries in in that company.
• 2:06
  Chris: You're the folks with the power in it there. You're you're the ones that are actually making the wheels turn.
• 2:11
  Igor: Well, us and sales. Because people don't wake up in the morning thinking, Oh, you know what, today I'm gonna buy myself some life insurance.
• 2:21
  Chris: That's good. Given the fact that it's risk management of this, could you talk a little bit about what is involved? If your job is to wake up, you're an actuary, and you're going to work at an insurance company, what does that look like? What is it that you're doing for the insurance company to do, like how do you implement risk management? What are the traditional tools, aside from some of the technologies that we're about to get into with you?
• 2:48
  What does that world look like?
• 2:50
  Igor: It usually looks like a lot of Excel spreadsheets. And for various items, there are more sophisticated models. So you may see proprietary software being used for certain things like projecting benefits. You may see some software specialized on the projecting of your investments and on your And in general, your day looks like doing a lot of financial modeling to answer questions or to satisfy various regulations or to develop new products. But in the end of the day, most of what actuaries do are some form of modeling or regulation research or compliance.
• 3:36
  Chris: Gotcha, so that is the wheels behind the scene in that industry that makes it turn. So I guess that when an insurance company, for instance, is going to price its products, know, the insurance that a consumer might buy, I assume that all the aspects that go into that are part of what the actuary figures out in terms of like, they can't price it without knowing all the different types of risks and how those play into the model. And so I'm curious, how did you find yourself? Did you start yourself as an actuary and then go into technology? Or did you come from the other side as a technologist into the space?
• 4:15
  How did you find yourself in this field?
• 4:17
  Igor: It was actually quite an interesting story. So I was programming since I was the age of 13 because I told my parents that I must have computer to learn programming. That was a ruse. I just wanted to play video games like all the other cool kids. But I think they get the last laugh because here I am a software engineer.
• 4:38
  So it was a hobby of mine. Then I moved to US and I kind of lost my circle of friends who were into programming. And so thinking what I want to do, my parents were telling me, well, maybe you should be a lawyer. So I looked up, okay, to be a lawyer, what major do I need? And it was English.
• 5:01
  I did one semester of English. I hated it with lots of passion because somehow there is no wrong answer, but all of my answers were somehow wrong. And so I did a bit more soul searching and I decided that I want to become a high school teacher for mathematics. At the end of my college, I taught for one semester in American high school and I decided that I need to switch a profession. So next thing I know, I'm sitting in a coffee shop Googling what to do with a mathematics degree.
• 5:34
  And that's when I discovered actuaries. And I said, oh, okay. Interesting. So there are some exams to take. I took my first exam.
• 5:43
  It was probability. And I was like, oh, probability. I'm great at probability. So I went in. I got two out of 10 on that exam.
• 5:53
  That is not a good grade. And I was like, Oh, you know, that is actually challenging enough of a profession for me. I like it. So how do people pass this thing? It is absolutely brutal.
• 6:04
  Turns out you get a manual that is about thousand pages sick with about 2,000 practice problems. You solve through all of that and then you show up to examine, it's easy at that point. So I did that. So I applied to a bunch of entry level actuarial jobs, but that was in January. And some of you may remember that there was a financial crisis right at about that time
• 6:27
  Chris: when some Yes. People got laid
• 6:30
  Igor: So I discovered that getting a job as an actuary was very, very difficult as an entry level actuary. And then I said, Okay, what else do I know how to do programming? So I applied to a bunch of insurance companies with my resume for both actuary and programming jobs. And I got invited by Prudential Financial actually for an interview for a programming position. On my resume, it said, I have introduction to C plus plus as the only programming course.
• 7:00
  And then I also brought a portfolio of things I've done. And they said, Okay, you have done a whole lot of stuff. And so they hired me as a programmer. Pretty quickly, realized that as an actuary focusing on modeling, I can continue to do programming but get paid twice as much. And so I joined their actual leadership development program and rotated through a bunch of different departments, learning how insurance works, won an innovation competition along the way there with the idea of kind of future of modeling, what does it look like, and what should a firm do to prepare for that?
• 7:39
  So that's how I ended up being an actuary.
• 7:42
  Chris: Gotcha. So I'm curious, while the current age of AI had kicked off again by that time, it was really before anybody was doing it. So I'm kinda curious as you were looking at models of the day that you're actually working with in real life at the job, just to draw a distinction, you know, as we as we talk of modeling over this past decade plus. What were you typically working with? What kinds of models, what were the algorithmic things that you cared about in your day job when you were doing that, as you moved into software development, but within that actuarial modeling context?
• 8:19
  Igor: So coming from a software background, I was actually very surprised by a lot of things that I've seen. Because quite often models, instead of being this kind of software that is very rigorously tested and optimized, you discover that there is a lot of quality controls that is commonplace in the software industry that just doesn't exist. Part of it is because how do you version control an Excel workbook? And that is very challenging. But coming from background of using Git and DevOps, you're like, Well, there is a way.
• 8:58
  We just need to get away from Excel somehow. But that runs into a different problem of there was no programming training for actuaries. There was no required programming training for actuaries prior to 2018. In 2018, there was actually programming added to the actuarial exams. And since then, the profession became overall much more knowledgeable on programming and programming tools in general.
• 9:27
  Because the situation was kind of interesting that we all do modeling, but none of us had some sort of like very hands on modeling course that is very kind of operations based as opposed to understanding the theory behind insurance and all of the probability calculations and things.
• 9:47
  Chris: I'm curious, is the field in 2018 updated itself, as you said, and you started seeing programming as a requirement, how did that lead into, now that it's kind of recognized as part of the field, and over the years since 2018, not too long here, seven years, how has that affected the field itself in terms of getting up to the kinds of things that you're doing at this point where you're having AI capabilities and kind of modern algorithms directly implemented for actuarial fields? What did that look like, that evolution? Because that's not a lot of time between 2018 and 2025 as we record this.
• 10:35
  Igor: It is actually very different. And it was actually driven not so much by technology, but by regulation. Regulations became much more sophisticated and that kind of forced everybody to use better technology. Because if you need to run stochastic runs with thousands of scenarios, you cannot possibly do that in Excel. It's going to run for weeks.
• 10:59
  And so that forced the industry to upgrade their systems and to adopt quite a few of the practices that are much more typical in software development. So seeing things like DevOps and unit tests, it became much more common nowadays. It's still not on every insurance company. And it really depends kind of what insurance company how insurance company operates. Right?
• 11:27
  Because some of them, they focus just on sales and they kind of outsource all of the product development to other companies. And the others are much more focused on kind of the mechanics of producing the insurance and then letting other companies care about sales. As far as AI, there is still very little adoption of it. Chief problem is the fact that AI is not reliable in a sense that it doesn't necessarily give you the truth. And when you're complying with regulations, you do have to have the truth.
• 12:00
  So there is quite a lot of experiments of what is it useful for in summarizing the data. There is some experiments in model development assistance. And yeah, I would say and in providing a second opinion to the data analysis that is being done. So I would say those are the three areas where we see AI actually having some real world value that we observe.
• 12:53
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• 14:21
  Chris: So as you got to this point in your career where where technology is changing, you know, and it's evolving, How did you get from that point into where you're at now with NICE technologies and and the capabilities that you provide? What was the the remaining part of the journey into as you were kind of getting closer to the modern point?
• 14:43
  Igor: It really was born out of necessity. So our platform is kind of two pieces. There's calculation engine and data, and those are kind of two major parts. The data management part was really born at 2AM as I was working on pricing reinsurance deals. And in that job, you deal with a lot of reruns and with a lot of tool chains that are rather manual.
• 15:14
  So new piece of information comes in for a meeting that you have tomorrow at 9AM. This piece of information comes in at 5PM the day before. And then the tool chain to incorporate this piece of information into the final quote can take four or five hours of different combinations of Excel models, Python models, different proprietary models. One reason for that is that reinsurance pricing is very bespoke. And so it's difficult to automate a process that is bespoke.
• 15:51
  And because of that, you end up actually sitting in the office until you're done incorporating this and checking this. And then you send your boss a message at like 2AM saying, hey, it's done. Can you review it by 9AM? And they would actually be there knowing that they do need to review it because there's a lot of money, a lot of eyes on the quote and a lot of responsibility. So that's when I came up with the idea of, all right, how there must be some sort of engineering solution to this.
• 16:25
  And the solution is really what if you make your tool chain remember the steps you took in your original pricing. Right? Because if you have that and it can recreate those steps without making mistakes that humans tend to make when they work at 2AM for the fifth night in a row. That's where that idea was born. The other part of it was I've done modeling in several different systems.
• 16:55
  And I realized that for certain products, some systems are far easier and quicker and cheaper to use than others. The challenge there is what if the cheapest system and the best system to use for a given product is an in house system? And that's where I took where I realized that we can take a page from the video game development industry. So in video game development industry in the '90s, they had the same problems. They had a bunch of proprietary software.
• 17:24
  As complexity of games took off with compute power, you said, Oh, three d graphics are great. Your development costs also skyrocketed, but you can't sell video games more expensively because your customer refuses to pay more than $50 So how do you solve it? Well, Unreal Engine and Unity, right? They basically provide a core functionality that everybody builds off of to build the actual video games. And that makes the process much cheaper, and hence, you can actually have games that cost $50 as opposed to $5,000 So we said, okay, can we take that similar concept and apply it to insurance?
• 18:01
  So we would need a software that provides access to the underlying code, but at the same time, we need to make sure that it is commercially viable and well supported and industrial grade. And that's how we came up with idea for our calc engine, which is source available business model. Meaning we provide source code to our clients, but not to general public.
• 18:25
  Chris: Gotcha. So it's interesting that I I love you're kind of there's a little bit of kind of a classic entrepreneurial story there, and that you're a, kind of scratching your own itch. Like, you're recognizing my job would be easier if I were to recreate what I was doing before and have that immediately available. And then that's interesting inspiration. Would not I don't think I would have expected you to the analogy between the actuary in field and also gaming is pretty cool.
• 18:59
  So it sounds like you're building an app kind of in the same spirit as you mentioned, Unreal Engine and Unity, which are the two biggies in that industry. So in the end, could you talk a little bit about what your calc engine became and how it was received in the industry? Like what you know, I know, a sense, Daniel, who is my cohost, who is not able to make it here today, is also the CEO of a small company that's growing very fast. And I love hearing kind of how he's gotten through the steps of how his company has developed from kind of the the challenges at the very beginning, and and he gets traction and stuff. Could you talk a little bit about what your what your pathway has been like as you've been developing the the calc engine and the other features at NICE Industries?
• 19:45
  What has that pathway looked like for you?
• 19:48
  Igor: So we we always leaned on feedback. Even before we started a company, we we did went to several executives in the industry and said, would this be interesting? You know? We went to heads of pricing, heads of modeling, and said, you know, what if this thing existed? Would you care?
• 20:08
  And the answer was we would love to see a demo. Pretty much every single person we spoke with said, we would love to see a demo. We said, okay, great. But to build a demo, we need like a year of work because this is pretty complicated thing to to build if you want a demo that looks somewhat realistic. So with that knowledge, we said, okay, well, let's let's see if anybody else believes in this.
• 20:34
  We reached out to several people that we wanted to cofound a company with, and only two of them did not join. One of them did not join because she had a baby five days before we called her. So that was understandable.
• 20:48
  Chris: That's a pretty good reason for anything, quite honestly, in the world.
• 20:51
  Igor: But the others joined, and that was a very defining moment of saying, Oh, okay, I'm not the only person who believes in this. And then we went to investors and we said, Well, would you be willing to invest in this kind of venture? And we discovered that the answer was also yes. So we said, okay, well, apparently there's quite a few people who are interested. So then we developed a demo.
• 21:17
  We showed it to the original group of people that we were working with. They said, okay, great. When is it going to be commercially available? We said, well, probably for another year or so because there's a lot of work to go from prototype to a production grade system. And then we launched publicly in this February with a production grade system.
• 21:41
  And right now we're in talks with a lot of different insurance companies in various stages of negotiations.
• 21:49
  Chris: So it sounds like right now you're kind of the hot new thing in that field. Because if you just came out a few months ago, you've got this great new thing that's gonna revolutionize the industry with a capability it didn't have before. I'm just curious, as an industry that hasn't had this kind of capability in the past, what's the reception like on that? I see in a lot of industries that we'll talk to people who, you know, there's a bit of a struggle to shift mindset into recognizing it, and then there are other industries where people are just waiting for the leap forward and stuff. Is it, or some combination of the two, what is it like changing an industry, you know, in that way at such a fundamental level as you're trying to get traction on that and bring in for you as a as a business owner, bringing new customers on board, but kind of giving them a new way of of doing that?
• 22:41
  Igor: It's a lot of explaining how we're different. Because on one hand, people quickly catch on that our business model is completely different from everybody else in the market. On the other hand, the most common question I get is, do I require to know Python to use your system? The answer is no. But that is like the number one question the most common question that I get.
• 23:05
  And so it is a bit of a process, especially at big companies where there are many stakeholders, a lot of different vetting processes, and a lot of coordinating between a lot of parties. So you need to have pricing on board and valuation on board and modeling on board and IT on board. And it just takes a lot of time to coordinate everything between those. Just like life insurance policies, insurance companies usually don't wake up in the morning and say, Oh, you know what? We're going to swap our modeling platform.
• 23:38
  That's a big undertaking, and it is not taken lightly. And having been in those shoes, I realize that there's a lot of work and you would only do it for very, very compelling business reasons.
• 23:53
  Chris: It sounds I I like, when if you look at like, speaking about one of your customers, that they're really it's really to to get the benefits and to leap forward, they they have their workflows, which is and as you kinda pointed out, the different components of their business, the different departments, you will, have to all kinda get on board. It reminds me a little bit as you say that, and I don't know that this is a good analogy, so I'll it out of kinda when you put in like like an SAP implementation in a in a company, and it hits many different functions of it. It seems to have where the workflows themselves at those the at the customers will change. It sounds like different software, but within your industry, like you're, there's a bunch of plugin points where people have to buy in and workflows change so that they can get the benefits of surging forward. Is that an accurate way of representing that?
• 24:48
  Igor: Oh, yeah, it is somewhat similar. I would agree that it does change the way people work for some people more than others. So if you look at it from a model developer perspective, you used to use proprietary software where you had a bunch of dropdowns where you could edit some formulas. Fun fact, those formulas look suspiciously similar to code. And now you use something like Visual Studio, for example.
• 25:17
  Now, looking at it from perspective of an actuary, you would say, okay, that's a big difference. Now I have to understand Python. But looking at it from kind of an actuary who also has experience in programming, I see it as, oh, I finally have Git for version control. I finally have DevOps. I finally can automatically merge code from multiple developers.
• 25:43
  I finally have amazing capabilities and breakpoints and a lot of these tools that I did not have before that allow me to understand things quicker. And in today's world, that also means that you can have an AI code assistance that can explain how things work, that can make variants of different calculations, that can create unit tests for you, and do all of those things actually quite well.
• 26:14
  Chris: So that is pretty fascinating to me in terms of the description. As you're talking about tools like Git and Visual Studio and stuff like that, and those are things that I obviously know coming at it as a developer, Could you talk a little bit about recognizing that not every actuary is a developer and everything, could you do a deep dive into kind of what your workflow looks like? Like from the customer's perspective, what does that turn their reality into? And can you draw out some of those points about why this is better than it was before by doing that? Because we've talked a bit about the investment in learning new stuff, the investment in getting the different departments of the insurance company working together.
• 27:01
  Take me through it, and if you could draw out some of the benefits of the technology and specifically the AI bits that you have into it, I'd love to understand what that looks like. What does a modern actuarial workflow look like for a company that's now out on the leading edge of that?
• 27:19
  Igor: Sure, so a simple example would be, let's say you have an Excel model that does something. Right? So imagine a dozen or so tabs full of Excel formulas, and that does some sort of calculation. Right? So the number one question you would get is, well, how does it work?
• 27:38
  If I want to understand how this works, I would spend some time digging through all of these formulas and tracing and kinda understanding how they all connect. Now in the in our platform, right, you literally just ask the AI system, hey, can you explain this how this calculation works? Can you point me to where in the code is it? And you get an exact location in the code and the calculation. Right?
• 28:05
  Then you say, okay. Well, I need to modify this model to support a new product. And there is maybe several pieces of that product that would need to be modified. In Excel, it's very difficult to merge models. So if you have three people working in parallel, it's very difficult to put all of their changes together and make sure that you didn't miss anything.
• 28:28
  Versus if you work in Python, you have Git. You just say, hey, can you merge this? And if your platform is well organized, meaning it's object oriented, you would have very little problems with that. And there's good chance it'll just auto merge in seconds and say, okay, there is no conflicts. So it enables development of functionalities as a team as opposed to being forced to be in a single developer because working in parallel is simply too complicated to merge.
• 29:04
  And then you say, okay, so now I have this functionality. It kinda sorta works. I wanna make sure that it's production grade, so I need to test it. In Excel, you have a limited number of tests that you would hopefully come up with. I've seen dozens usually being kind of a decent number.
• 29:24
  With the help of AI, you can create a lot more than that. And with unit test functionality in Visual Studio or DevOps, You can have literally thousands or even millions of unit tests to make sure that your software does exactly what you want and can handle all of the edge cases correctly. And then the next step would be to say, okay, well, it works in Excel, but this is glacially slow. What can I do? And at that point, you're quite often just stuck because there's simply no way of getting it faster.
• 30:01
  Now, in our world, you can ask your AI assistant, Hey, can you check my code for Well, first of all, you have the performance benchmarks and those would get triggered if you've done something that tripled the runtime all of a sudden. But second, you can ask AI assistant to say, hey, can you tell me which part of my code is slow and can you give me some ideas of how to do it quicker? And quite often you will get, at the very least, will identify where you're doing same calculation over and over or doing other things that are suboptimal. Overall, it's just much nicer experience, which requires some knowledge of Python to be effective at it from modeling perspective. But at the same time, with AI systems, that threshold of how much Python you need to know is much lower.
• 30:59
  As well as it is just so much faster to ask AI assistant for like, Hey, can you explain something? Or can you find something in the code base?
• 31:08
  Chris: It sounds like, just as an analogy on another topic that we brought in, common buzz phrase these days is vibe programming, vibe development, where you're working with a LLM on your code, and that it sounds like there's sort of an analogy in that the actuary is going to know some Python to use the system, but the system helps with that. Is that is that a fair assessment to where the system itself is kind of working in that vibe coding manner of saying, I can show you the challenge in the code that you need to address or and maybe I can help you fix it and things like that. What are can you differentiate a little bit about which parts of the workflow the actuary is getting into the Python and what kinds of things are able to be done through a GUI or through an agentic approach, for instance, where you have an agent that may have taken on a task that was historically a very manual task or anything like that. Do you have anything that you can share along those lines just in terms of how workflows evolve?
• 32:13
  Igor: Right, so I'm mostly talking from a modeler perspective. Now, if you are a user like pricing actuary or valuation actuary, so you use models, but you don't alter their functionality. You're just putting inputs in and getting outputs out. For that, you actually don't need to know any Python at all because all of that is done through a GUI. Now, a great thing about a source available approach is that you can bring your own GUI.
• 32:45
  And in fact, you can have multiple GUIs. So instead of a single desktop application that's used by pricers and modelers and tries to do well for both of them, we say, well, why not allow use of third party tools? So a modeler can use Visual Studio or PyCharm or whichever IDE they prefer. Now the pricer can use Excel or web interface or some other media that integrates well with their particular systems and workflows. And you can swap those out or you can even build your own.
• 33:25
  So it helps a lot when your system is source available with the ideas that you can build anything you want in it, and that could include your GUIs.
• 33:37
  Chris: So it sounds like you're really focused on kind of maximizing the actuary's time and minimizing the amount of effort to get compared to what it was prior to them having this kind of capability available to them. How does that, like if you're the actuary using it, like what is the impression of how it changes jobs? I ask that in terms of the requirements of doing the job because that's one of the first things that people are always asking is kind of how does the technology change what it means to be an actuary? Like in as a software developer myself, that's in the AI space, people are always talking about what the quality of software development is with our with the all these cool new tools we have. And often the results there's been a lot of conversations about it, you know, depressing the software development need.
• 34:29
  But in actuality, I actually disagree. I find the tools to be very helpful, and I'm still very engaged in it. What is the actuary experiencing now, having ramped up on your process versus the process of doing it fairly manually with spreadsheets and everything before? What's the feedback you get on that in terms of what people are how do they perceive this new way of doing it now that they're kinda fully ramped in and up to speed?
• 34:55
  Igor: So from pricing perspective, I think this would be most vivid. So as a pricer, you basically take the request for proposal for a certain insurance contract. You figure out how to plug it into your models. And ultimately, you produce a quote that says, okay, we will do this reinsurance for this much money on these terms. Right?
• 35:20
  If you ask a pricing actuary today, what are they doing during their day? They would say, Oh, I'm pricing. Now, if you ask them, Okay, so what exactly does that mean? Mechanically, what are you doing? They would say, Well, about 50% to 70% of my time, I'm literally moving numbers from one model into another model.
• 35:45
  And then run the next model and then takes the outputs and plug them into the next model. The remaining time I'm spending checking the results and making sure that they make sense and that everything looks good and there is no strange things happening as far as the results that I expect versus what I actually got. And then I'd say about 10 to 15% of time is also spent on hunting down answers to various ambiguities that were presented in this contract. So you would reach out to the party requesting and saying, Hey, there is a bunch of people missing gender. Do you know what gender they are or their birthdays are in the future?
• 36:30
  We're pretty sure that's wrong. So that's the world today. Right? Once you implement a platform like ours, what happens is you set up your pricing process once. And then from there, at any point, if you get any data updates or changes or you want to try something, you change your original inputs, you click a button.
• 36:53
  And because the system remembers the steps you took, it'll actually do the full repricing for you in about ten, fifteen minutes. So it's a good amount of time to go get a coffee. And that frees up your time to do more analytical things or to price more quotes. Management loves the second one. But as a pricer myself, I like the third option.
• 37:22
  It gives you an option to go home at 5PM as opposed to 2AM.
• 37:28
  Chris: That's a good answer, actually, a really good answer. So you've taken it this far, you've really made an impact in terms of the capabilities you're offering to the companies in your industry as you're taking on new customers and stuff. As you're looking forward in time, and and over some period, you know, not not immediately, but, know, over a year or two, maybe longer, depending on what your horizon is. How are you envisioning the industry changing, and do you have any ideas for the future that you would like to see either yourself implement or the industry at large change and develop into? What kind of future you know, when you're when you're kind of done for the day and you're having, you know, a glass of wine or just kind of chilling and your mind's wandering a little bit, what's the future that you're excited to see that you're wanting to help build or be a part of?
• 38:25
  Igor: Yes. So I see the future we wanna change the way actual modeling is done. Instead of current proprietary softwares that are being used, we envision a future that is built on common technologies. So particularly Python and tooling around that. And we think that that future will be much more efficient and it will make our profession even more valuable than it is today.
• 38:52
  Reason for that is if you learn a proprietary software today, you go to a different company or even a different department within the same company and they use different software. And then you have to relearn everything you learn because all the buttons are different, all the menus are different, the logic is different, and many things are different. If you build modeling on a common technology like Python and Visual Studio and DevOps, you learn it once and those skills will be just as applicable to any other company where you go. In fact, they apply beyond just modeling. You can do automation, you can do data science.
• 39:36
  And in fact, they apply even outside of actuarial professions. They're just generally more and more valuable as our society becomes much more technologically advanced.
• 39:47
  Chris: Fantastic. Well, Igor, thank you so much for coming on Practical AI today. I learned a lot and really appreciate you sharing, not only kind of teaching us a bit about the industry you're in, but telling us how NICE Technologies is starting to change the face of the industry in terms of actuaries are able to kinda level up and go home at 05:00 instead of 2AM. Definitely a big bonus there. Thank you very much for coming on the show.
• 40:21
  Igor: All
• 40:21
  Jerod: right. That's our show for this week. If you haven't checked out our website, head to practicalai.fm and be sure to connect with us on LinkedIn, X, or Blue Sky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner Prediction Guard for providing operational support for the show.
• 40:41
  Check them out at predictionguard.com. Also, thanks to Breakmaster Cylinder for the Beats and to you for listening. That's all for now, but you'll hear from us again next week.