Invest Like the Best with Patrick O'Shaughnessy
00:19:14 · Patrick O'Shaughnessy
[00:19:14] S0: The data thing is such a good example of the scale advantage. And you're an investor obviously before starting a bank, and so I'm sure you've thought about this endlessly. It sounds like scale is maybe the most important thing that will make new banks so competitively advantaged for the next ten years or something. Is that right? Like, does that feel like the right one to pick?
00:19:32 · David Vélez
[00:19:32] S1: I think it's case important, but you need it also with the right culture and the right systems. So one of the things we heard a lot in the early days was, well, how are you going to compete with the banks? The banks have scale. They have a hundred year history with a 100,000,000 customers. How can you replicate that data? And going back to the analogy of Blockbuster, my answer was always, well, Blockbuster had a huge amount of data. Imagine the amount of people coming in into the Blockbuster and seeing if they were looking at that VHS or the DVD or not, or renting. Did that data help them build a better search algorithm? No, because they were not capturing the data. They did not have the right systems to capture data. They didn't have the right culture, data culture to be making decisions based on data or based on models. And someone like Netflix was able to come in with no data and build a better algorithm and ultimately use that. Scale and data is important, but equally important is your systems and your culture. You ultimately be in a culture that is using a lot of these data to make the right decisions. And this, I think, differentiates neo banks or more digitally native organisations like us versus the more traditional incumbent banks that are still operating with 15 different systems as a result of a huge amount of acquisitions that they did over the years. And there's still, if you look at a lot of the AI applications, how we use AI to transform COBOL? How do we get away from the mainframe? You're talking about ten, fifteen year histories of going to the cloud. They're still in that process of going and putting systems on the cloud.
00:20:54 · Patrick O'Shaughnessy
[00:20:54] S0: How do you run that tactically in the culture to make sure you don't fall in the same trap that like all the big players before you have fallen into, which is that you're sub optimally using your advantages and you don't have a good algorithm. Like what is the tactics of you making sure the team doesn't mess that up and actually does the opposite?
00:21:12 · David Vélez
[00:21:12] S1: I think the biggest difference is that historically traditional banks saw their basis of competition as a lot of banking branches. That's literally how consumers used to pick a bank. Is I'm going to pick a bank that has a banking branch very close. And so that's why there was a huge amount of effort and energy into real estate and physical infrastructure. And obviously a lot of focus on building brands and building trust with consumers. In financial services, especially you need to protect that trust with consumer, that's very critical. So when we began the company, we did not have to spend energy or focus on an offline world, it's more of an online world. And we saw technology and data as a way to build comparative advantage. And so since the beginning, we saw ourselves as a technology company, not a bank. Technology company that happens to be in financial services. And therefore the culture itself has a culture that uses data and analytics consistently for every single decision. What our own foundation model has enabled us is the cost of putting a feature into a model has gone down significantly. Pre AI, you had to spend six months adding a new feature. If you wanted to test a new feature in a model, had to spend several weeks work testing this feature, putting in the model, testing it around. With our own foundation model, you can test a number of different approaches much faster. Then the cost of creating a model, you have an email that has purple letters or yellow letters. The cost of doing a specific experiment has gone down significantly. And so every decision in the organization can now be automatized and be done by model versus a human. That's what AI is also enabling. So it kind of let us do what we were already doing at a much more significant scale. But the culture itself already existed since we saw this as a way to differentiate ourselves since the very beginning.
00:22:56 · Patrick O'Shaughnessy
[00:22:56] S0: How do you think the global financial system will change the most or needs to change the most in the next five years, totally away from your business? Like what are the exogenous changes that you're most carefully watching that you think matter?
00:23:08 · David Vélez
[00:23:08] S1: One of the most transformational technologies in the world in franchise is gonna be the first one is the end of cash. And this is happening really everywhere. There's a lot of big cash heavy economies like Mexico, for example, or Indonesia, or a lot of African countries. Brazil used to be like that, where that cash will go away and will digitalise mostly through real time payment systems. I think the implications of this, some of them are obvious. You don't need branches or you need less. Some of them are less obvious, which is what happens to the economy, for example. The efficiency of banks when they don't need banking branches. The speed at which money moves in the economy. The amount of data that gets created in an economy that then can be used, for example, to provide access to credit. So that's a big catalyst. The second one is open finance and open banking across the world. This is basically the concept that you as a consumer own all the data that you have across all institutions, and you're able to grab that data and share it with anybody else. So one of the biggest sources of inertia in financial services, but also let's call it telecom, for example, or utilities, is that you you have a history with a specific company and you cannot transfer that history with somebody else that comes new. And incumbents have benefited a lot from that inertia of data. The moment that you can connect all your history from all your services and pass it to somebody else, you're reducing that inertia and you're enabling more competition for your data. And you can monetize that data as a consumer. You can grab your historical information with a bank and offer it to somebody else that is willing to pay for that if you decide to do so. Right now, social media does that without you deciding to do that, increasing you're going to be able to do that as a decision. And the reason open banking is important is because then it will decrease that inertia globally and will increase significant competition in banking. Banking today globally really is still very much on a oligopoly structure. You have five, six banks in The US that own 6070% of all the assets. Same in Europe, same in Asia, same in Latin America. So that will mean a less concentrated banking industry, much more fragmented, much more competition. Obviously, that translates into less fees and less cost.