Trading Tomorrow - Navigating Trends in Capital Markets

Venturing into Virtual Reality and Finance with Lyron Bentovim

• Numerix • Season 1 • Episode 4

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What if you could see data differently? Not just as numbers on a 2-D screen but as images that tell a story. This concept is Lyron Bentovim's dream. As the President and CEO of The Glimpse Group, Lyron oversees a myriad of virtual reality technology, one of which, D6 VR, focuses on finance and big data.

VR is a fast-developing industry; revenue is estimated to explode to $22 billion by 2025. In this episode, our host, Jim Jockle of Numerix, is joined by Lyron as he shares his first-hand experience and insights from fostering businesses within the VR and AR industry. Listen to hear Lyron's intriguing story behind the birth of D6 VR and what he believes is the future of this technology. 

Speaker 1:

Welcome to Trading Tomorrow, navigating trends in capital markets. I'm your host, jim Jockel. In my decade plus of working with Numeric's global leader in capital markets risk management technology, I have launched our Thought Leadership Division, a place where insights, innovation and expertise converge, just like this podcast. Through my journey in the financial realm, I've had the privilege of witnessing firsthand how the capital markets landscape has transformed the complex dance of market trends, and innovative technology has redefined how the finance industry operates. With game changing innovations just around the corner, we now stand at a crossroads, one where it is more crucial than ever to understand the interplay between these realms. That's what we do here. We talk about current and future processes and technologies you need to be aware of moving forward. Today, we're covering a fascinating topic virtual reality and the world of finance.

Speaker 1:

The virtual reality industry is growing at an astounding rate. According to statistics, a global market size is projected to increase just under 50%, from $12 billion in 2022 to over $22 billion by 2025. It is a technology that's being pulled into every industry, including capital markets, and data visualization is clearly top of mind. According to the annual Broadridge Digital Transformation Survey, leading firms are boosting spending on data analysis and visualization by 33%. Joining me for today for this discussion is Lerone Bentofam. He is the president and chief executive officer of the Glimpse Group.

Speaker 1:

It is a platform company that was designed to cultivate companies in the virtual reality and augmented reality industry. They own and operate several subsidiary companies focused on providing immersive technology, software and services to businesses with real world applications ranging from health care to training to education. One of them is called D6VR. It focuses on finance and big data. Lerone himself is a serial entrepreneur with decades of experience in technology and finance. Glad you could be here, looking forward to it. So let's just start off. What made you want to get involved in VR in the first place?

Speaker 2:

It's kind of a funny story. I grew up in what I call the previous tech cycle, the digital cycle. So in the early 80s, pcs, internet, mobile, basically from the early 80s all the way to probably the mid teens, kind of moved us from an analog world to a digital world. And usually tech cycles are multiple technologies that come together and make a big impact in society Kind of across the board, from consumers to businesses to organizations, everyone. And I grew up in the other one and I kept chasing it.

Speaker 2:

So I've had my startup in the dotcom days I kind of worked in the industry but I was always kind of stepped behind where I wanted to be career-wise. And back in early 2016, it dawned on me that we're in the beginning of a new tech cycle where immersive technologies, virtual reality and augmented reality, together with AI and blockchain, are going to move us from a digital world, which is where we are right now, to an immersive world. And again, this is a 30, 35 year cycle. It's going to take a while, kind of, and we're probably year eight, year nine out of that 30, 35 years. But back then in 2016, I saw an opportunity to be early this time. Take my kind of experience I've been doing and kind of innovation, entrepreneurship, technology for many years and build a company that will become a major participant in this tech cycle.

Speaker 1:

And in fact, yeah, glebs Group is the first ARVR company on NASDAQ solely. Is that correct?

Speaker 2:

Yes, so yes, that is correct, we're the first pure play kind of NASDAQ enterprise focused software services company.

Speaker 1:

So I have so many questions as a first mover kind of in this phase and I do want to get into it, but I do want to focus on the D6VR. So this is a pure play, financial services play dedicated to capital markets data visualization, created by a former Morgan Stanley senior economist. What were your first thoughts when you learned about his company and what I mean? You want to bring him into that Glebs Group fold.

Speaker 2:

So Andy came into Glimps as almost like an entrepreneur in residence. So he was passionate about technology. My co-founder of Glimps, dj, still runs NYVR, which is the largest meetup for immersive technologies in New York second largest in the world and Andy showed up. He reached out and wanted to meet with us and talk with us and I loved him smart guy and passionate about technology and he says why don't you come on in and we'll figure it out? We're very early startup.

Speaker 2:

This was our first six months out there and Andy came and initially was involved in everything and kind of we started really narrowing down into financial services and, again, with my background, I used to run a hedge fund for seven years. So my experience, his experience, we started talking about what could we do and then we brought on one of the senior technologies that was actually doing something else at Glimps and he's got a lot of years of experience. He actually was involved in virtual reality back when he was trying to become a real thing in the 90s before, but technology was not there. So we pulled him in and he took all the stuff that we were talking about and kind of made it into kind of mock-up but technical mock-ups to see how it felt, and the more we talked about it, the more we felt that there is something here. And that's kind of how that story evolved.

Speaker 1:

So perhaps you can give us a little bit. Explain the technology. How does it work? What was the use case? What was it particularly designed for?

Speaker 2:

So the main I'll start with the main kind of use case and kind of the thesis behind it and something that I thought about as well and that has managed kind of portfolio kind of for many years, is what if you can see data in a different way? Obviously, we are used to seeing data on 2D screens. We see it on our Bloombergs and wherever we're using, and data in 2D screens doesn't really talk to you. If you focus on each number, you can understand what that number means. The price is going up by 2%. You get it, you understand. But if you look at a big picture, yes, if it's all red and green, if the picture is mostly green, you can kind of tell. But that goes towards my story of kind of how do you visualize data differently? I'll give you another analogy. You drive down the same kind of road to work every day or go on a train and you see things outside and you'll be shocked when you notice differences in what you're seeing a building that someone has changed the color of, or something, even though it's immaterial to your life. We notice. We're visual creatures as human beings. So the thesis was how can we take data and visualize it in a way that gives you an edge when you look at mass amount of data.

Speaker 2:

The dream I had back then which is still a dream kind of fun, when they might become reality is imagine you're a portfolio manager and you're sitting on the beach In the headset. You're in a virtual beach, enjoying kind of watching the water, but instead of just random waves, the waves actually mean something. So the data that you're seeing is visualized into the waves and if you see the data every day and look at that, you will get observations about the market, because suddenly you will see that there's a lot more waves on that far right side of the ocean view that you have. Then you had all the previous weeks. And here comes AI into play.

Speaker 2:

You can actually kind of nowadays with AI say AI, what's that kind of? Why is there a lot of ways on the far left side of the map? And the AI will explain to you that this is whatever kind of real estate and it's been going down in high volatility. And now you've got an observation that, looking at all the data on the stocks, you might not get. So that's the thesis. Now it evolved into an ability to see data. Talk to people about data taking all the advantages that virtual reality has in terms of being immersed in it, and you can either walk into data or bring someone else and have a discussion with the data around you.

Speaker 1:

So I guess you know, I think thinking about capital markets and you know the concepts of big data are still being evolved, if you will. You know, data lake technologies only really started to emerge. The role of a chief data officer, you know, really started popping up, I want to say in kind of 2014, 2015, around that, obviously, cloud computing has been a facilitator in the development of terabytes of data in financial services. So what is that leap in terms of getting to the beautiful ocean of data and bathing in alpha as compared to, you know, the state of organizing that data and preparing it for this kind of visualization?

Speaker 2:

So that's a really good question and obviously the quality of what you get out of it is ties to both the quality of the data and your ability to process the data in a way, that kind of convert the data into a visual language. And that's where we've been starting. And you start. You have to start small. You can't go all in ocean, pun intended. You have to kind of basically take, start, taking kind of fixed data sets and visualize them so kind of. One of the things that we as people can see is multiple dimensions. That are usually harder to proceed. So let's kind of take that from that big dream of the ocean of data and bring it down to a seven dimensions or six dimensions, as the six was called because they had six dimensions at that point in time of data. So kind of. Usually you can see two dimensions pretty easily on any chart, kind of you can do three and kind of get that. But as you start getting into color and size and other things that attach to the data and variations, that gives you a lot of points for your mind to focus on. So let's say we're converting data into a forest, so you start with the kind of X and Y of the forest, right. So those are the two data points that are easy for us to do. Then you look at the height of the tree as the third data point. But now what type of tree it is. So you can have different color trees, which are easy to see, or I can have different type of trees. Does the tree have fruit or not, kind of. How much fruit does it have? Kind of? Does the tree have animals on the right? Whatever, you can start coming up with a lot of data ways of converting data into this visual effect.

Speaker 2:

Now, as I'm looking at that forest, I will make an observation. Why are all the pink trees in the middle of the forest? It means something right. Or why are all the pink trees large and with no fruit? And you can either query that or, nowadays in AI, just ask the question and you'll get the answer. It means something You've just made an observation on the data that if I give you an Excel with that data, you would not be able to do. If I give you Excel with seven data points on multiple stocks or multiple investments and I give you the Excel as an Excel sheet, there's no way you can look at it and kind of make any observations, unless you have a thesis and you start taking the points. I put that in front of you in the data and you look at it and you immediately kind of see data. So that's the value. And the question is, how do you convert that into value for a portfolio manager that's generating a basically alpha.

Speaker 1:

So, in terms of the developers themselves or the individual, what is the skill set required? Right? I think back to the introduction of Tableau and Power BI and things of that nature. All of a sudden, we're all learning different languages. We're learning DAX and trying to figure out Python in the whole bit. So what does that person look like? The user? No, the creator, the one who's going to transform that data.

Speaker 2:

So the people that build it basically are developers. They're developing in one of the gaming languages, so kind of D6 was built with Unity. So that's kind of basically on one option. The other is Unreal, which is done by Epic. So those are the two big platforms you can build in. You can build equivalent to have pros and cons and why you use one or the other. Our teams use both in a variety of ways.

Speaker 2:

So they build that data, but for the actual user, the person putting the headset on, you don't need anything. That's the beauty of it. You basically select that. You upload the data sets which you should already have from whatever sources you're using and we'll talk about live data versus kind of pre-canned, kind of Excel data or kind of data file that you can upload, and we can work with all the different functions as they come in. And you come in and you choose what data you want to see and you kind of basically use the user interface. You put it on the thing and you get the data in front of you and you can then start editing kind of the things until you see something that tells you something, either a picture you want to show to someone, or analysis, or kind of. You have an observation and you can look at that and kind of make the decisions. So that's pretty easy when that comes that way.

Speaker 1:

So it's an organization's data. They're structured. If they're getting streaming data, some sort of database, it's a connect to them.

Speaker 2:

And we can work with organizations to connect the product to their data sets. So either upload it or connect to it if it's stored on their servers, and some of those require kind of basically adjustments. Or we can connect to live data feeds from kind of a variety of the solutions that are out there and then you bring in live data and kind of do some delay because it needs to be processed, but you can still see kind of live stock action as it's counting.

Speaker 1:

So streaming data off the internet, yeah.

Speaker 2:

Again kind of technology will evolve. Right now there is a delay in processing in cloud and all that stuff. It's seconds, not days.

Speaker 1:

John. So just out of curiosity, right. So the gentleman, the economist from Morgan Stanley I forget Andy, as he was kind of exploring with the prototypes what were some of his experiences in visualizing the data and seeing it differently, and what revelations, if any, did he walk away with?

Speaker 2:

So what he wanted to do, the way he looked at it is, he came in saying I'm going to start with what I know best.

Speaker 2:

So he was basically an economist in Morgan Stanley, so he was presenting macroeconomy to senior leadership and kind of customers of Morgan Stanley. So he says, look, I used to have those 16 charts of different economic data and I would flip through that book and I will show them this and that. And then I wanted to show them how this relate to that. I couldn't do that because it's all in the same chart. So you see this trend going this way. Now kind of, let's flip to this page and this trend is going that way and go back to this page. So that was the challenge he had and he wanted initially to build the prototype to be able to do his job that he did at Morgan Stanley and say, okay, I want to put all these data. So I'm going to put them up on the wall and then I can connect the data sets and show how they kind of over arch with each other in the big data room.

Speaker 1:

And that's kind of how we evolved product Well so you mentioned AI in terms of integrating the technology. Obviously, chat GPT is taking the world by storm, even though AI has probably been around for 40 years. How do you see the two technologies coming together?

Speaker 2:

So where AI comes in is the ability to have knowledge that you can converse with. So when we introduced AI initially to the product before the chatGPT revolution over the last nine months or so, we basically kind of used AI to try and find trends in the data and highlight a variety of trends and give you more insight. Because we now have the data in a multi-dimensional facet, we can show you insight when chatGPT AI agents will come into play and we're introducing now those into our VR elements so you can actually go in and talk to an AI agent. So it's not just text.

Speaker 2:

With chatGPT you actually have a person that looks like whatever you wanted to look and you can give them access to either the full world or very specific data set, depends on what you want to do.

Speaker 2:

So the thought of having that AI agent with you in the room where you can interact with them and discuss the data with them and they can give you past experience that you might not have.

Speaker 2:

So you look at the data and you find some kind of increasing situation where volatility is going in this way and kind of, but the prices are going that way and you have an observation and you can actually ask the agent in there and they either have access to the full world of knowledge out there, if you want to open it up, or to your specific foreign database of situations. Well, let's talk about a situation where we had this before what happened, and the agent can actually go and retrieve that information and you can interact with them and ask them questions about it and you can do what-if scenarios. So those are the capabilities where you're immersed with the data. The AI is aware of data that you're seeing and together you can actually discuss it Again, same way that you can do it with live people. But if you want to kind of run it first by AI and look smart when you kind of talk to clients or talk to your bosses, that's a great opportunity.

Speaker 1:

So one of the questions I have to have and admittedly I do have a VR headset I play golf in one other game that I won't disclose, but where is the hardware right now? Because it does feel like a limiter as it relates to adoption.

Speaker 2:

It is a big limiter right now, kind of especially for this use case. So right now the hardware is constantly evolving and Meta is going to come up with a Quest 3 in a couple of months. Apple just announced a really beautiful machine. That kind of, I think, shows where the future is going, but it's not even ready right now. So hardware is an inhibitor in a variety of ways. First of all, kind of not everybody has that, which makes it hard to do it, kind of.

Speaker 2:

At least now it's more of a oh well, we started out it was all tethered. So we went to Morgan Stanley and we're talking to his senior partner, morgan Stanley that was pretty interesting technology and they told him he needs to run on Computer with a high GPU and all this stuff and he's like, brought the IT guy and the guy said well, those are the four computers we have here. None of them are good for this and we're not getting any other computer. And the guy kind of and you know that the IT guy actually runs this place and he can go up all the way to the CEO and it won't help him much, and it was a real challenge. So now it's just at least a mobile headset that goes on Wi-Fi and less issues bring it into organizations. But at the end of the limitation is you can't be in the headset for very long time.

Speaker 2:

So people like me that are used to can do an hour and hour and a half no problem, kind of. Some of my crazy people in my office can do three hours, but most people initially after half an hour they're like getting fatigued from it, because it's different. You have screens in front of you, it's on your head and that's a limitation if I want to do real analysis and spend a little time in it. So the use case we have right now is you have it on your desk, you put it on when you want to do some stuff and then you take it off and you work with it on the desktop and without the data, and then you set everything up, you put it on, you see it and you put it off.

Speaker 2:

I think we're gonna get to and as the cycle happens and again we talked about 30, 35 year cycles, so we're early in the first half of the cycle is a hardware cycle, just like it was with computers and phones and the Infrastructure that got the internet going. So over time We'll get to the point where we have things that look like these glasses that we have on all day long and you basically turn them off between different modes, from VR to AR, to just seeing the world, and it will not be inevitable right now. Yes, it requires a change of how people do things, and that definitely slows down adoption.

Speaker 1:

So a final question for you in terms of Capital markets financial services. You know in many ways they're not first movers, as as as you are. You know cloud computing. It arguably is only getting to a significant point of adoption and I remember talking about cloud, you know, as the panacea back in you know ten years ago. So you know if an organization is Thinking about or is, or the one enlightened individual in the capital markets group is thinking about it, what, where? Where do they start? Where should they be thinking as it relates to Introducing VR to change and transform their business?

Speaker 2:

So you need to find that use case that you're right now Challenged that you think VR will allow you to do it. So, yes, once you get into VR, it's fun and we've had a lot of kind of capital markets people that's Played around with our solution and we used it and enjoyed it and kind of got into it. But you want to have some use case where I want to get to see this or I want to work with this team, because one of the benefits Right now you can be remote, so if you're out of the office, you can actually meet in the data and have the team wherever they are, whether they're internationally or kind of Just working from home. Go in the data. So find that use case, find a team that's all committed trying it out, because you want to have success when you start so we're now at our final segment.

Speaker 1:

We call it the trend drop, so it's like a desert island question. If you could only track one thing about VR within capital markets over the next few years, what would it be and why?

Speaker 2:

so kind of the data I would like and kind of. I spent some time thinking about that and Kind of finding that data point that I know I don't have access to it I probably will never, but that's the whole point of the question. I would love to know how many headsets are used by people in the financial services world more than an hour a day. That's, that's the metric. I'd love to know because that shows you where it is. I would guess the number right now is very, very low and, as you see, that number rumping out, kind of it means the industry is finally getting this. I think this is going to change the industry, but industry, as you said, is slow to evolve and Everybody's looking to the sideways. If nobody else is using it, there's no reason for them to, and and kind of, once you start seeing that number pick up, you know the industry is is on the move. So that would be the kind of the number I would love to see well.

Speaker 1:

Thank you so much, for your thoughts are Making I think all of us think, and I want to thank you so much for your time today. We really appreciate you joining us. My pleasure Coming up next week machine learning and how it's changing the face of investing it's an episode you don't want to miss. But first, if you enjoyed the podcast, make sure you hit the subscribe button, leave a comment, a like and check out our other episodes. Thanks for joining.