Everyday AI Podcast – An AI and ChatGPT Podcast - EP 231: NVIDIA Making Money Moves: How they're using AI to change financial services - An Everyday AI chat with Malcolm deMayo and Jordan Wilson

Episode Date: March 19, 2024

Awesome Stuff From Our Partner, NVIDIA -Register for the FREE virtual NVIDIA GTC Conference or buy tickets to the in-person event and fill out this form here: https://www.youreverydayai.com/nvidia-giveaway/AI is being used in about every facet of the financial industry. A company you might not know helping power the entire industry forward? NVIDIA. And Malcolm deMayo is a key player making it all happen. Malcolm is NVIDIA’s Global Vice President of the Financial Services Industry. Tune in as he gives us the behind-the-scenes of the AI-powered money moves that NVIDIA is making.  This is a special in-person interview in partnership with NVIDIA and their NVIDIA GTC Conference.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Malcolm questions on AI and NVIDIAUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTimestamps:00:00 About Malcolm and his role at NVIDIA05:28 Generative AI is a game changer.06:38 Financial firms must embrace technology for success.12:43 Generative AI's role in financial sector risk.14:15 Financial firms adopting NVIDIA's compute platform.19:42 NVIDIA's impact on financial institutions through digitization.22:02 Firms aiming to build our amazing tech.24:06 Server providers build and offer trading data.Topics Covered in This Episode:1. NVIDIA's Platform and Partnerships in Financial Services2.  Application of Generative AI in the Financial Sector3. Challenges and Risks in Adopting Generative AI4. Future Outlook and Developments in Generative AIKeywords:NVIDIA, AI, computer graphics, financial services, AI applications, generative AI, GTC conference, San Jose, California, GPU, microprocessor, strategy, partnerships, Lighthouse customers, ecosystem partners, hyperscalers, deep learning, financial firms, generative AI implementation, chatGPT, AI technology, accelerated compute platform, data analysis, customer experience, AI adoption, AI infrastructure, executive briefing center, upskilling, AI factory, Jensen's keynote, AI developmentSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

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A lot of you probably, when you think of InVidia, you probably think computer graphics, generative AI. But what you probably don't know is that because of all of that technology, Nvidia has its thumb on the heart of everything, including financial services, which is what we're going to be talking about today on everyday AI. Thank you for joining us. welcome. My name is George Moleson. I am the host of Everyday AI, your daily live stream podcast and

free daily newsletter, helping everyday people learn generative AI and how they can leverage it to grow their companies and to grow their career. And if you're joining us on episode like 230 now, you're probably thinking this looks a little weird. This isn't how it normally is. Well, yes, this is actually our first in-person interview for the live stream. We're extremely excited to be broadcasting live at Nvidia's GTC conference out here in San Jose, California. We're going to have a lot more this week, but today we are talking financial services. And before we get into that, I do have to give a shout out because it's not too late. You can still join the GTC conference for free.

We're going to have the link in the comments and in our newsletter, as well as a way to earn DLI credits as well. All right. So now after that big windup, I want to introduce our guest Malcolm DeMayo, the vice president of global financial services at NVIDIA. Malcolm, thank you for joining the show. Joe, and it's great to be here with Everyday AI. Really awesome. And it's awesome that you're here at GTC. So thank you.

I'm excited. It's been a five-year, a five-year kind of delay of us just waiting back for this in-person, right? What's been your vibe so far, kind of walking around here? We're sold out. It's unbelievable. So, you know, it's great. It's going to be in an amazing week. So definitely, audience, tune in.

Yeah. So, I mean, can you tell us a little bit about what you do in your role in global financial services? Sure. Thank you. So most people think of Nvidia, like you said in your intro, as a GPU or a microprocessor. And we are so much more than that. We've been involved in financial services for over 15 years. My role as the global head of financial services at Nvidia is to make sure we have the right strategy, that we're building the right partnerships that we're engaging with Lighthouse. customers to what we call build our bowl of soup, which is solving really hard problems with our accelerated compute platform. We sell nothing direct. We work through our ecosystem and we believe

that everyone should win. Starting with solving the customer's problem, the partners should be winning and we should. You know, yeah, even going back to that, how does in the end in vdia actually play out in all of these partnerships, right? Can you explain that for our audience? Because is it just, you know, a platform that, you know, banks and financial institutions log into that helps them, you know, better use their data or, you know, what does it actually look like in the video's partnership in the long run? So take a step back, if you go back to, say, 2012, we helped JPMorgan Chase to accelerate the risk management trading platform, options trading platform. They saw an 80% reduction in total

cost of ownership and a 40x speed up in their trading. Fast forward to today, AI exploded on the scene in 2022 with chat GPT. And it's really hard for these companies for financial firms to leverage AI. There's a lot of work they have to do. And they need help. So it requires ecosystem partners. It requires their current ISV partners, their software vendors, to take our platform and vet it in so that they can deliver value to the customer without having to rip and replace their technology. And we deliver our platform through the hyperscalers. So all of the clouds are our biggest customers.

They make our platform available through their customers, all of the large server OEMs, original equipment manufacturers, like Gillette Packard, Dell, Lenova, Super Micro. I don't want to leave it out, but all of them. Got to get them off. Yeah. And they all deliver our platform. And they have expertise to help customers leverage, to help the financial leverage the capabilities in our platform.

You mentioned DLI, our Deep Learning Institute, and it is a gem. It's sort of a hidden gem. DLI is free. It's free for anyone to come in and use, to learn how to use our platform, to build generative AI applications. You know, Malcolm, you said something there that's interesting. You know, you talked about kind of this explosion of chat GPT, you know, onto the scene, you know, about two years ago now.

How did you see that from your vantage point playing out specifically for financial institutions? Do you think it was maybe kind of watching from afar and saying, okay, this isn't really for the financial sector? Or do you think that some, you know, banks and financial institutions were quick with that? I think that today, 2024, most people, and there's still some who are skeptical, you know, in the business there were a couple of technology waves like blockchain, all right, that had a huge hype cycle and still it's going to be a valuable technology, but it didn't deliver on the hype. And so there's a few doubting Thomases out there, but for the most part, everyone

understands today that generative AI is a game changer. It is a tsunami wave size game changer for financial services for every industry, but in particular financial services. And when ChatGPT was announced, when Generative I exploded on the scene in 2022, executives, not just the data scientists who learn how to use the technology and use the technology to improve the efficiency, operational efficiency of these firms, to improve productivity, and to create new revenue streams. in partnership with the practitioners, the banking practitioners, the trading practitioners,

the network payments firm practitioners. They started to see us. The executive started to come to see us because anyone can use chat to APT and see how powerful this technology is. And so they wanted to understand how can we use this in our business. So, 2003, Jordan, was the year of experimentation for financial services. How can we leverage this to generate better experiences for our customers? If you read any 10K today from a financial firm, one common theme is we do improve customer experience.

And this technology is enabling that in call centers with digital assistance. For wealth managers, you have co-pilots that are capable of being very smart, in real time helpers. And then the fact, Financial firms also want to be careful not to kill the next generation of bankers because a lot of young bankers coming into the business are in an apprenticeship. So using the technology requires that experimentation and understanding how are we going

to use it in the firm. This year, 2024, is the year of AI going into production. And we're going to see some really exciting things. You know, something that's interesting there is, you know, even mentioning these partnerships that have been going back a very long time. And with artificial intelligence, it's not new to the financial sector, right? You know, machine learning, deep learning has been pivotal for the industry for many decades. But with generative AI, it seems like kind of like what you were even saying, all of a sudden,

executives are paying attention. And it's not necessarily, you know, people in IT or your chief technology. officer, et cetera, how should those in the financial sector be looking at generative AI, specifically even when we're talking about large many models? Yeah. Well, so very, they should approach it, I think, with enthusiasm. You want to embrace this technology. The reality is generative AI is very good.

And AI in general, pregenerative AI was amazing at classifying. information and we just do a ton of that in banking, a ton of classification, document classification, etc. Very good at making predictions, used to make predictions. But generative AI takes it, kicks it up and notch and allows you to really for the first time analyze the vast mountain of unstructured data, and that's data that, the document data, written data, that's video, that's image, as images. So for the first time, they have the opportunity to harvest all of this knowledge that exists in all the data. And the thing you need to keep in mind is that generative AI is, while it's very good at analyzing and finding insights in all of this giant haystack

of data, what it's not, it can't replicate the way humans make decisions. And we probably, as humans make thousands of decisions a day and don't even realize it. And we just generally go through this decision tree of thinking, the logic, the way our brains are wired, generally that multi-step thinking isn't there yet. So it's a great tool to help people understand. So think about a wealth manager. Instead of having to collect data from 15 different systems on their institutional client, Jenny, I can do that more, making summarize it.

It can do the extraction. but you still need that wealth manager to understand the customer's goals, to understand the risk tolerances, and to build a really good strategy for them and plan and help them execute it. So in your role at Nvidia, you've been able to see this Gen AI implementation in the financial sector from many different angles. I'm curious, you know, so far not saying we're at a, you know, halfway point of GenAI implementation or anything like that, but, you know, how would you grade the,

financial sector right now in terms of keeping up or actually implementing generative AI. Maybe can you talk about just from your vantage point some of the kind of challenges and triumphs that you've seen as well. Sure. Well, so just to put a punctuation mark on the question, we're at the very beginning. We're not in the middle. And we're at the end of what you would consider general purpose compute. was compute and we're at the very beginning of a new era of compute called accelerated compute.

And what's driving that is generative AI. And so today or yesterday, financial firms have thousands of applications, hundreds or thousands of applications they've written tomorrow in the future. That's all going to be done by AI and as an assistant to a developer. And you'll have instead of thousands of applications that are hard-coded and have and require developers to go in and rewrite and modify AI is going to do all of that so now you have the opportunity to do higher-level things and so you know i from a grading perspective i would say that financial services industry gets a very high grade very very good at adopting technology this is what they've done for decades they have to be careful because they're highly regulated.

The data that they use is highly sensitive and they need to protect it. They are the custodians of their customer data and they do an amazing job at it. The uses of technology by bad guys is escalating and they fight them every day on behalf of their clients. So I would give them a very high market. Let's talk a little bit more about that because it's very easy to see the wins for genera. of AI. You gave the example of someone having to look into multiple different areas for data and now generative AI can do that for them. What are some of the challenges, specifically in the financial sector that people need to be aware of, you know, because sometimes people just want

to just go out and, you know, leverage generative AI and figure it all out later. So what are some of those risks or, you know, things that people should be concerned about? Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Firefly app, the All In One Creative AI Studio. Powered by Adobe's creative agent, Firefly AI Assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the Assistant. The Assistant orchestrates multi-step workflows, drawing on 60-plus pro-grade tools across Adobe Creative Cloud apps,

including Photoshop, Illustrator, Premiere, Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks, like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adopi.com. Well, that's kind of a two-prong question.

For financial firms, the challenges are this isn't easy to do. And the technology is changing so fast that it's hard to keep up. And so working with an accelerated compute platform like NVIDIAs takes that burden off their shoulders. We're keeping up. We make sure that every time a new model comes out, it's optimized to run on the platform. That we have financial firms coming to us and asking the question, how do I know if my model's good? How do I know if this thing's accurate?

How do I measure that? And the metrics for models today are things like, can it take the MCAT, can it take the LSEC, can it take the GMAT, can it take the blah blah, blah, blah test? And that was a technical phrase, by the way. Yeah, I failed that test. And so, you know, they may want to show that the model is very, very accurate and can take the CPA exam. They may want to show that the model can take, can not take the certified financial advisor test. And so we've built all of those evaluators into our platform.

So you're going to hear all about this week at Nvidia. That was a sneak preview. From a risk perspective, you know, the biggest challenge, and so it's very hard to work with mountains of data. It's very hard to work with a model that's frozen in time. And so we've built a platform that helps them do that. So, for example, the frozen in time. If you use an API service and you ask you a question, a current question, you'll get an answer back something like I've only been trained through September 23, right? So we use a technique called Retriever augmented generation.

And this was created by meta, but we baked that into our platform so that customers can now have a conversation with their most important data sources. Imagine just being able to ask a question in English and get an answer from your most important data source instead of having to sit down, write a structured code, test the code, etc. That's how this is changing. the industry. From a risk perspective, there's the risk of hallucination. And so what we've done is build guardbells. And our guardrails ensure that a model stays on topic. It's not going to talk about religion. It's not going to talk about anything politics. It's not going to talk about anything's

mom taught you not to talk about. You know, we make sure that the model is only connecting to trusted sources. That's another guardrail. And the ability for customers to build their own guardrails using our open source platform is really easy. And they can do it again anywhere in cloud, on-prem, through all of our partners. Could you maybe just walk us through the whole process, whether it's an actual example or theoretical how financial institutions either are or can.

really push forward, save time, improve efficiencies by using NVIDIA's platform. Sure, a couple examples. First of all, the industry, banking at its core, is a batch system that was developed in the 1960s. These are mainframe-based platforms. And running in COBOL, which is sort of your grandfather's programming language. I did learn COBOL, but at any rate, what we're able to do, today is use a use a an AI assistant to essentially analyze that code and write it in something modern like Java as a first step and then maybe a second step to something that's more modern like

python etc so or g plus plus which is very fast so that's one example and if you have code being generated about 40% of the code in github today is generated by AI think about it if you're a financial firm and you have 10,000 software developers and they're now 40% 50% more productive. All right. That means you can get 40% more work done. That's more value to your clients. That's more capability for your clients to use. Another example is in the call center. You call your bank on Saturday morning or on lunch break during the week and you get the infamous touch one for blank, touch two for blank, touch three for blank or wait for the next available assistant, right? Nobody likes that. 30 minutes later, a very very much. A very much

nice person gets on and helps you. With digital co-pilots, you're going to be able to have the option to say, you know, I just have a simple question. If seven out of the ten questions that are asked on a call center are just complaints, help me with something, all right, a digital, a digital avatar, a human, which we build in our platform. So a very lifelike person will come on and say, hi, how can I help you? I need to reset my password. Here's how you do it. Or I don't understand how to transfer money using Zell. Here's how you do it. All right.

And so the opportunity to take those 30-minute wait queues down to, you know, immediate, real time. And so what have you done? You've improved customer experience. And the customer is going to walk away at one good experience. All the surveys today, one good experience means they're going to use more of your products. You know, yeah, we've talked a lot about how NVIDIA's advancement in this sector have changed things for the financial institutions that are wealth management, etc. But we haven't talked too much about on the customer side or on the client side. So do you see that is maybe the future of where we're heading?

And instead of, yeah, like waiting on the waiting on the line forever, is there maybe just I'm speaking with a digital twin avatar? You know, is it going to be that personalized? Even, you know, you talked about rag, right? Like, would my even data be accessible with a digital avatar? Like, would I feel to instantly make changes like that, whether it's my personal banking, wealth management, etc. Is that where we're going? You're going to have AIs helping AIs. You're going to have AIs watching AIs so that, you know,

the very first thing will be, do you actually need access to this information? Do you have the right authority to have access to this information? But yeah, think about today everything is so siloed. It's very hard for customers and bankers to have a holistic conversation. that all that's going to change. We're desilowing. AI is helping firms desilo. So it's going to be exciting. So speaking of exciting, we're in a very exciting environment here at GTC in San Jose. What can you talk about where you're headed in the future? Because I know we're always going to get, you know, some big announcements and some things that I'm sure that this sector is going to be

reacting to in the weeks and months to come. But what is in the near future for Nvidia, especially in your space. People always wonder, what do you guys do? We started there. And Jordan, the first thing to understand is that there's a lot of GPU and hardware accelerators. There's FPGAs, GPUs, A6. These are all hardware accelerators. There's lots of them.

But there's only one accelerated compute platform, and that's ours. And we started building this after AI's Big Bang in 2012. Jensen saw the correlation between AI, and compute and started investing heavily so that when the world was surprised in 2022 with AI's iPhone moment, chatGBT, we were ready. All right. Most most firms are trying to build what we have and what we're going, what you're going to continue to see from us is an amazing, but in the last 10 years, we've developed, we delivered a million X speed up to our customer and we're We're going to do the same thing in the next 10 years, starting today at GTC at 1 o'clock

at Jensen's keynote. Don't miss it. He's going to be announcing so many cool things. The first observation is that we're a full-stack platform and nobody else is. The second observation is that we're available everywhere. As we generate speed-ups, it makes this more affordable and more adoptable. And when we put it in cloud and you can use it as a pay-for-use, it means that more more enterprises have access to it.

Is there a scenario in your mind when you look in the coming, you know, year or two, do you see just development going even, even faster? You know, I even think in my own, you know, personal experience with generative AI, and I feel, you know, in the last like couple of months, it's hard to keep up. So specifically even in the financial sector, you know, like you said, and AI and machine learning, keep learning, has been in a decade. Do you think, and maybe with what's announced today,

right, is it going to hyperdrive in the coming months and maybe a year ahead? Well, the last three waves, sort of the internet, mobile, and cloud took 20 years to mature and to be fully adopted or largely adopted, means the way to say it. And, you know, compared to AI, that's glacier movement. AI is moving at light speed.

The announcement is every day. There's another research paper published. There's another innovation. There's another. And so you need a company like Nvidia who is on top of this. And who are our biggest customers? It's the clouds. It's the server providers who build all of this

and make it available to the everyday person. So yeah, you're going to see. be in the trading business, the half-life of an alpha signal. Alpha signal is there in, is the industry sort of description for above average profits or returns. All right. That, the half-life of a signal now continues to shrink because the market's moving so fast and everybody's competing in a world where they all have access to the same data.

And so how they use that data, how, you know, is the differentiator. curious and how talented their team is and how expert they are at what they do and how good at using the platform we built they are is what's going to allow them to win. What would you say, what's your biggest piece of advice, maybe whether it's people at extremely large firms who are still tackling, you know, Gen AI implementation because it is an ongoing process, right? what's your best piece of advice for people working in large financial firms? Maybe they're going back and forth.

Maybe they're not on, you know, in video's platform yet. But what's your best piece of advice for them to first understand generating AI in large-inch models? But how should they be actually looking or what problems should they be trying to solve? So first thing is embrace this. And don't get left behind. The ship's leaving the dock. Don't stand on the dock. embrace this. Lots of ways to do that. We have a launch pad. It's free. You can experiment on that launch pad for two weeks and try something out. Use our DLI, a Deep Learning Institute to learn. Come visit us. Our executive briefing center is whirring right now. It is just jam-packed. But when you leave, you will have a very good understanding. When customers walk in, they're like, we know this is for real. We just don't understand necessarily. When they leave, they're like, wow. So number one, embrace it. Number two,

Make the investments in your people, upskilling them, and two, in the infrastructure, because the infrastructure in everyone's data centers today is yesterday's infrastructure. They need an accelerated compute platform, and you can start small and build as time goes. But over the next few years, they need to transform their data center from a data center, which is a cost and expense item. It stores emails to an AI factory, which allows them to generate AIs that, improve productivity, improve operational efficiency, and help them grow revenue by doing a better job with delighting their customers, depending on what segment. So those are the three things.

It's one, embrace this, two, upskill your people, and three, invest in the right technology. Well, speaking of investing in the right technology, hopefully you're doing that with us. Today, like we mentioned, Jensen's keynote coming up at 1 p.m. Pacific, I'm not going to mail vice president of Global, Global Financial Services at VDivya. Thank you so much for joining. It's great to be here, Jordan. All right. Hey, and as a reminder, stay tuned with us.

We're going to be here throughout the rest of the whole week with bringing you exclusive guests, insider insights, and also check out the show notes. We do have that giveaway going on. So you can sign up for free to watch the conference virtually, no matter where you are. Maybe you can make it to San Jose. We'll be given away that free GPU as well as the credits. So thank you for tuning in. Make sure to go to your everyday AI.com.

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