Invest Like the Best with Patrick O'Shaughnessy
00:13:04 · David Vélez, Patrick O'Shaughnessy
But that might be 5% of the cases today versus if you have a savings account, a credit card or a loan, you just don't need that anymore. So I think the rationale would be effectively, yeah, we're trying to serve these customers, but it's ignoring that for the majority of people, especially the mass market, increasingly you just don't need it. And you don't go to that physical infrastructure anymore. Yeah. [00:13:26] S0: One of the questions that I think lots of investors and founders are wrestling with is what will be the best competitive advantages or moats in this new AI era when the cost to build new stuff digitally is much, much lower? If you had to imagine it like you're some new founder, I strip everything away from you and your only goal is to kill new bank, is to like build a new company to attack new bank, let's say in Brazil or in Latin America. What do you think you would do? Like what would be the vector of attack on your own business? [00:13:52] S1: Which in a way was what we did to the incumbent banks [00:13:54] S0: ten years ago, right? We were
00:13:56 · David Vélez
[00:13:56] S1: the disruptor going after incumbent banks. So I think the early stage startup today that is AI native, biggest advantage will be the speed, the ability to move extremely quickly and launch a number of different products. Especially if you're born unregulated, then you're to be able to move fast. By now we are fully regulated. We have banking licenses in a number of different countries. So we have to go at a certain pace. We have to be checking with regulators. Now, think specifically in financial services, specifically banking, that will only get you up to a certain point because eventually you're going to need to be regulated as well. And so I think in some areas, there's a lot of conversation about how AI disrupt software and that you can ask Charge EPT or Codex to rebuild an entire software strategy, but you still cannot really get Charge EPT to get your banking license. And you still cannot really get them to have regulators give you licensing in six months instead of two or three years. Specifically on credit or in certain products that we operate in, the amount of data that we have is pretty significant. So we have about 140,000,000 customers. For each customer, we have something like 20,000 to 30,000 data points across thirteen different years. So this is a huge massive amount of information that allowed us to build our own foundation model. We build our own foundation model for financial services, for credit. And this gives us a significant advantage in making credit decisions, for example, or collection decisions. The early stage startup entrepreneur world, they would have the speed, they would have a much harder time getting to that level of data. So data becomes a significant moat now for more of the incumbents. Companies like us, we're sort of in the sweet spot of benefiting the most from AI, because in one way we already have the scale and the data and the brand and the trust and the licenses that will be hard for the early native startup to move. But also we still have the technology angle and technology infrastructure to allow us to benefit from AI and what AI is creating and move even faster than we were moving before.
00:15:50 · Patrick O'Shaughnessy, David Vélez
[00:15:50] S0: Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant up to date proof of trust without all the manual work, so you can close deals faster and with less friction. Customers report a 526% return on investment, and more than 16,000 companies use Vanta, including Ramp, Harvey, and Snowflake. Get started with one zero off at vanta.com/invest. Ridgeline is the first end to end system of record with embedded AI for investment management firms running portfolio accounting, reconciliation, reporting, trading, and compliance on one unified platform. Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software. I've been hearing from a lot of investment managers about AI, and they fall roughly into two camps: some unsure where to even start and others convinced that they can build their own order management system over a weekend. The reality is that running an investment firm will always require governance, controls, and a single source of truth for your data, and no amount of AI enthusiasm changes that requirement. Ridgeline is built on exactly that foundation, which is why I believe that the firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. If you're serious about your firm's AI strategy, Ridgeline [00:17:13] S1: should
00:17:13 · David Vélez, Patrick O'Shaughnessy
[00:17:13] S1: should [00:17:13] S0: be part of that conversation, and you can request a demo at ridgeline.ai. Can you build the pretty detailed timeline of when you first had the thought like, wow, AI is a thing that we need to take really seriously. And then as the leader of the business, how you have thought about rolling it out and being more aggressive with it over time. And I'm interested in the details, like you said, you have your own foundation model, like how's that built? Is it open source that you've tuned based on your own data? Like who do you work with? How did you think about it? I'm really interested in your personal timeline, like how far back it goes and what the major hash marks have been.
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?