Invest Like the Best with Patrick O'Shaughnessy
00:17:46 · David Vélez
[00:17:46] S1: We started using machine learning since we started the organization. Since 2013 and we looked at Brazilian interest rates for consumers paying 506100% a year, we said there's an opportunity here to use better data and analytics and machine learning to price credit risk. And different than a lot of FinTechs, we decided to take balance sheets, to take credit risk and to be capital intensive by default. And so we had already an organisation that was very optimised for making decisions based on models and data. When the CHAD GPT moment came about three, four years ago, we started paying significant attention around how technology was going to facilitate some of their decisions. And we acquired a company called Hyperplane in Palo Alto that was already working on doing foundation model for financial services. They were having such a hard time selling to the big incumbent banks. Sales cycles were two years. They were not really able to move while the technology itself was fairly forward looking. So we bought that company, we integrated the team. We think we were the first financial services firm in the world to build their own foundation model with its own proprietary data to do credit decision specifically. Even published all of our own research. In this area, we developed an early advantage. We're now using our foundation model for fraud decisions for Mexico, for Colombia. As we launched The US and our international markets, we can day one already come in with our own foundation model and understand how to use that data for making basic decisions. This should accelerate our ability to come in.
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?