Invest Like the Best with Patrick O'Shaughnessy
00:11:00 · David Vélez
[00:11:00] S1: The first obvious rearrangement is those 600,000,000,000 that banks are keeping in the balance sheet to pay for banking branches, get rearranged, get forwarded to all the banking consumers. Because suddenly they realize, what am I doing with my money leaving it at zero? Especially when you think about compounding, what that means for savings and compounding interest. This is a lot of money being sent ultimately to pay banking branches. You look at really any country around the world, any structure you look at, Mexican banking balance sheets, for example, or Brazilians, we're exactly the same thing. Big incumbent banks, about 70% of their deposits, they pay zero. And 30%, they pay some yield generally to the highest income population or the corporates. One of the things that you see in banking is in a perfectly competitive environment, the best product should win. If you're charging no fees, if you have the best experience, Banking is a commodity product ultimately. So you should be able to get 100% of the market share. That doesn't happen because there is a lot of inertia in the system. And with the combination, the multiplication of intelligence with open banking, for example, Pled. Can connect 120,000,000 accounts and you put intelligence on top of that. You're effectively allowing each customer to know how to optimise their financial life and where are the fees coming. So I'll give you an example. We're doing this in Brazil today where about 50,000,000 of our customers every day, we're telling them, Hey, your bank account, your credit card in that bank is due tomorrow. You don't have any money there. And they're going to charge you a 15% interest rate per day. Let us give you some money so you can pay that credit card. Or refinance, you have this loan at 10% interest. Let us refinance that at 5% interest. It's ultimately a drive towards optimizing and removing a lot of those profit pools on behalf of the consumer. In some products, like small businesses do want to sit down with a banker, with a human and have that conversation. If you have millions of dollars to invest, you still probably want to see a human on the other side of the table and have that conversation. For certain consumer segments and for certain products, if you want to get a mortgage or certain sophisticated products, there is value in having a physical space.
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.