Name: Brainstorm AI 2024: The Greatest Financial Instrument of All
Uploaded: 2024-12-10
Duration: 16 min 51 s
Description: Pegah Ebrahimi, Co-founder and Managing Partner, FPV Ventures, Rajat Taneja, President, Technology, Visa Moderator: Terri Burns, Founder and General Partner, Type Capital

Pegah Ebrahimi, Co-founder and Managing Partner, FPV Ventures, Rajat Taneja, President, Technology, Visa Moderator: Terri Burns, Founder and General Partner, Type Capital

Brainstorm AI 2024: The Greatest Financial Instrument of All

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OK, Pega Raja really excited to have you both here today, Raja, maybe we'll start with you. Visa, of course is at the forefront of leveraging A I for risk prediction. Can you tell us a little bit more about this? Tell us how Visa approaches the challenges of scaling A I solutions to detect and mitigate A I risk globally. Absolutely. Uh Terry, you know, Visa is the OG of SAS companies and it was one of the original SAS platforms and lean in to A I back in 91. So core to Visa managing about a billion transactions a day, connecting 4.6 billion credentials to over 100 and 50 million merchants is the ability to do it in a trustworthy manner in a secure manner, managing the payment, security and cybersecurity. So our fraud model have been at the core of what we do with A I for over 30 years. And we went from good old fashioned A I to deep learning to neural networks to all the predictive A I pieces to now leaning in to generative A I helping us with models with the problem of Cold Start for something like a real time payment network to figure out if there is any problem going on with account that are being enumerated. Uh to see if bad guys are trying to see if cards are legitimate before they use it for nefarious activities real time uh management of this. And it's all about investing in the data platform, spent about $3.5 billion in the last decade to create the data platform for the future. And it's all coming together. Now, I want talk more about how you and your team have really embraced this culture. But but first, Pega, you have had a fascinating journey from CIO to investor. Can you tell us a little bit more about trends that you're seeing in A I and specifically, what do you think could have the biggest impact on enterprise transformation or even cio decision making? I look, I was I was a cio during the first mobile when mobile wave was coming. And it's like it's super interesting because you see, you know, everyone's like A I is so different than before. And yes, there's a lot of things that are different in the pace that it's growing. But in a lot of ways, history doesn't repeat itself, but it rhymes and it reminds me so much of when mobile first came and everyone was, there's all these mobile companies that existed, right? And for the most part, nobody says a mobile company anymore, every single company that came was on cloud is now born in the cloud and using mobile and, and it's part of being company. And I would say that's the same, you see it happening with A I at the beginning, it was like A I company A I company. And I think now there's definitely going to be foundational infrastructure A I company, but for a lot of the real great companies that are going to form, it's going to be how are companies starting and taking advantage of the fact that these inherent advantages exist because of A I and to build something that they couldn't have built before, right? Similar to when Uber, nobody thinks of Uber as a mobile company, but it wouldn't have existed without mobile if you don't have your phone walking around, um you couldn't really do that business model. So that's the part that's really exciting. It's how do you take things that people just assumed would have to be services, businesses, things that people assume would have to um you know, depend on so many people to get it done or it couldn't have been done because of the scale it needed. Those are the parts that are really exciting. That will be the next wave of A I that I think are, are gonna be exciting start ups. Let's talk fintech. This is a question for both of you. Do you believe that fraud prevention presents the most significant application of A I in fintech today. And if not, what is something that is commonly overlooked that you each are really excited about? Look, I think fraud is table stakes managing it, managing it well, staying ahead of the bad guys uh because they're very proficient, the same tools are available to them. If cybercrime was its own country in 22 it would be a $7 trillion economy growing at 32%. So it's very profitable. It's very big and so fraud and payment security and cybersecurity are existential. We have so many attacks on our network millions that you have to stay ahead of. And A I is a very important tool there. But I think there is the other side of A I. How do you use A I to re invent and rethink commerce? If you think about it, the last big major change in commerce was with ecommerce and then mobile and continue to break back. Years of shopping in the storefront or online. Now comes the opportunity to move from the hunt and peck era of shopping where you are still searching for things, you're analyzing it, you're trying to figure out what to buy, then going to the buying mechanisms to what we are calling the self driving commerce era where A I takes away some of the mundane aspects of discovery and of helping you with warranty management. Price matches registration of products and it brings the joys of commerce back to you in ways that you're discovering things that you would like to see whether it's products or travel or, or art and then helping you really enjoy and appreciate the pleasures of shopping. I think that's where a lot of the innovation is going to happen in addition to the basic foundation of core infrastructure, fraud and security. Yeah. And I always loved it like as a, as, you know, a cio or it was always nobody s companies had a hard time selling to financials because it time if you think about it, all the things in financials were on prem and it was just really difficult and I would say what's really exciting about this time around and how it took financial companies in a lot of ways, so much longer to get advantage of the digital transformation trends because they had to really take their time, make sure their data was secure, do it in a much longer time. Horizon right now, financial companies, I'm seeing move way faster in A I than some of the other industries, right? Because it almost was, they had to get set up, they had to care about data, they had care about some of these regulations and some of those parts make it easier in certain use cases for them to adopt faster. And I was always, when I went into tech, I was at Cisco and it was always interesting how everyone thinks of financial companies as old stodgy companies. No. No offense to us. Right. And in so many ways financial companies depended on such razor thin margins that in so many areas they had to be super efficient. Yes, they weren't the early adopters of technology, but efficiency is so core to finance organizations. And I think you see it now some of the financial visa other, they're moving so much faster on thinking where are the, where are the areas where we could actually take advantage of some of these um technologies for margin expansion for what services do we have on the fraud side, on customer service on financial advisors? I think that's incredibly interesting. You look at, you know, there's going to be 80 trillion of wealth transfer happening from baby boomers in the next few decades. And so many of the, you know, only one person and has access to financial advisors because it's just not a scalable by person business, but so many people that are used to technology and all those things I think will get advantages of that that they couldn't have before. And I also think there's just, there's, there's gonna be a myriad of other stuff that comes from building efficiencies in the orgs to all to, to actually consumers getting the benefits of it. Yeah, I think it's a really interesting point what uh in recent history might have been seen as kind of old school is now really at the cutting edge. And I think that both of you uh alluded to that in, in your responses in a couple of minutes, I'm gonna turn it over to the audience. So if anyone has questions, start getting those ready. But first, um I wanna talk a little bit about A I transparency. So there's this ongoing debate with A I models, particularly open source versus closed source. I know this is something I think about a lot as an early stage investor. What do you each think is the right approach for early stage companies? Well, I mean, I'll, I'll talk high level and then we can talk about what specifically they're doing. I think there's no one formula. I think it's so early that everyone's experimenting with a lot of different ones. But I do some of the things that are saying parallel look, I remember when the cloud migration was going and people were like, oh, do I choose Aws or Azure or do I don't want to get stuck on this cloud? Now? It's even fundamentally even I would say more exon because people are forget cloud lock and needing a few years and a bunch of engineering resources to migrate. If you have like model lock, you got seven years in and the prices change or the the dynamics change. And now you have all this investment that you did on really bringing your model up. So forget model lock. I think that um cloud lock, model lock is even becoming more key and So because of that, I think Cio S have saw that game played out. So they're just going at it more eyes wide open this time and thinking, I don't want to be stuck if something changes, if open gets closed, which is happening to some companies, right? And if things like that happen, and I think that's, that's the learning from that cycle have made Cio S just want a lot of different opportunities and knowing how to, how to be more in control and not stuck on something. And you see it at least that you guys are doing it. I think so well. Said Pega, you know, I think there are pros and cons to open and closed and you describe it really well with the M lock. I think the core is for there to be radical transparency. A key pillar of the model is the data. You have the algorithms and you have the GPUS, but it's really about the data and these are general purpose models that are built and trained on massive amounts of data. And we know the data out there has got noise in it. There is a data that is error prone, there is data that is just blatantly wrong. And so as more, whether it's an open model or a closed model as they are building it to explain with transparency, what an explainability, how they use the model, how the algorithm created fairness, consent, privacy, copyright infringement issues is super important. And for companies can I interrupt you though? Can you have true radical transparency with a closed model? It depends, well, it's difficult, right? Because it depends on what they are saying and the community is not able to go in and therefore you need to think about the regulations terry that come in and help create a framework for understanding of what happened in the model so that the practitioners can use correctly. If models are going to be transformed in medical and health care and diagnostic tools or finding new drugs, you need to know that right. And therefore, I think the role that regulators will play in creating that framework uh use based um interoperable because models don't know boundaries of countries. So you have to have global regulations that allow us to have guardrails and think through what went into it and how it can be tuned uh and how it can involve. So they are helpful and they are able to do what they are supposed to do. Yeah, I think that's fair. I think my personal bias is towards open source, but I think you're exactly right. There are very clear use cases where for data protection or sensitive situation, closed source, but with still some transparency makes sense. So right now, we have an ensemble, we have a number of different models at visa and we have abstraction layers on top of it much for the ways that pick up was saying but not, not getting a model lock in. Yeah, for sure. All right. Let me turn it over to the audience. Let's see if there are any questions. Ok. It looks like there is a hand raised here. We'll give it one second for our Mike runner. Please do say your name. Hi, I'm Maria from Freestyle Capital. I'm curious how you guys think about agentic payments evolving. Um I know that Visa has been thinking about it. I'm sure everyone has been. But do you think that it will evolve on existing infrastructure or do you think that a new infrastructure will need to be created for it to work at scale? Look, I think payments as a part of commerce is incredible, right? So you need high security, you need it to be seamless, you need it to adhere to all the regulations and compliance of every country. And as you move into an agentic model where a agent is conducting commerce on behalf of a consumer, it has to have all those attributes and explainability and management of compliance, but it will require new infrastructure because you're running models, you're in the models are doing inferences, the models are conducting transactions. So it will require digital investment in infrastructure. For sure. You sort of mentioned earlier. Uh Raja, you also mentioned this pe a regulation and so I wanna kind of double click on on this idea. How can companies balance regulatory compliance with the rapid rate of, of A I development. How do you both think about that? So, I mean, I think one of the look and we're talking about financials, which is interesting because it's been regulated for so long, which is why it's been so hard to sell to it. And you need to have a lot of data management and all these aspects. I think what's gonna be interesting is that for, for regulation is usually a little bit delayed. I mean, it's from how fast companies wanna go. And I do think that um on for most folks that are trying to enable A I it's hard for them. If you're a large company that has a high brand risk, it's hard for you to be at the forefront and then get pulled back. So what's happening is I'm seeing a lot of companies go in the areas when they know they're not going to be necessarily hitting some of those pieces. Like can you pick low hanging fruit that you can drive a ton of efficiency versus trying to go for the moon and trying to go for something that has a really high risk. And I think that's what you're seeing. Some of the pullback companies where at the beginning, there's this crazy excitement about, I wanna have po CS of every single use case on using A I and taking it back and saying, OK, what are the areas where we can do it? And we can have some risk exposure. It's not gonna be, you know, existential for the company and where are those areas that we wanna go? And I think that's what you're gonna see the next, you know, few years until the regulation catches up because I think people kind of are like, I don don't know if it's worth it to take that leap and, and have some existential risk, but there's so, I think you and not all use cases are the same. Like if you're touching personal data versus if you're having an FA Q, that's way more, you know, way more robust. That's, that's totally different areas. I think as a risk, you look, I think you have to think about the spirit of any regulation, right? If a safe, secure, trustworthy models, use of data explainability, fairness. So we have a set of guiding principles that are called data use policies in our company and we adhere to them very closely. So as regulations evolve, whether it is the EU Act of 24 whether it is the uh executive order in the US, uh the year before that or its new toolkits coming from Singapore or from the African Union. I think we just have to stay with the spirit of what you're doing and collaborate with regulators and academia to create the right framework. But if you are open and fair and transparent and explain things, I think you will be always safer with your models. Um In our last minute and a half here, you both manage teams, you have lots of stakeholders and clients and start ups that you work with. How do you all spot true value and opportunity from hype? As we know, there's a lot of hype here. What are both of your really quickly frameworks for how you think about that? Uh I think, I think the number one thing is don't you, you can always use the hype. But to me, um just the best companies don't talk about A I, they don't say, oh, we're an A I company. They say here's the big problem we're solving and here's the unique insight on how we're actually solving it. And I think that's at the end of the day, that's where the huge companies will form. We're solving real problems. You can obviously use the advantages of A I, you, you should because you're a start up and you cannot a lot. But I think those are the best ones and I think there's a lot of opportunities in the services or look at large companies. They have massive services that they have already figured out how to extend you outwards. And I think there's going to be a huge number of companies formed there. Um I think, you know exactly what you said, move away from powerpoint and go to code and let the code talk to you and utilize it and take it for a test drive and if you're helping it write code software development, use it to write software development and see how it compares or contrasts. At Visa. We give access to every single employee access to the models. Back in 23 we had been using it uh years before that and had patents and papers and so we knew that there was great potential and, but the real rubber meets the road when you use it. Absolutely. Tag Ja. Thank you so much for your time.