How I Invest with David Weisburd
E149: From $0 to $1B in 3 Years: Fin Capital's Meteoric Rise in Fintech Investing
Tue, 25 Mar 2025
In this episode of How I Invest, Logan Allin, Managing Partner and Founder of Fin Capital, shares how his firm uses AI-powered predictive models to evaluate startups. We explore how AI can assess founder DNA, predict billion-dollar outcomes, and transform the venture capital landscape. Logan also highlights the surprising traits that lead to startup success, the advantages of repeat founders, and what the future holds for fintech. This conversation offers valuable insights for investors, entrepreneurs, and anyone fascinated by the intersection of AI and venture capital.
Chapter 1: What inspired Fin Capital's investment strategy?
There's a professor at Stanford named Ilyas Zhubelev, who was one of my professors while I was at GSB, and he's spent a lot of time on this. He produced a book called The Venture Mindset, and that was coming out of a lot of his research. And he started his research by looking at what do unicorns have in common? And he started doing that research when I was there in 2012, 2013.
And I kind of laughed. I was like, well, that's a fun academic exercise, but that's not how you make money. How you make money is figuring out what they had in common at the seed stage. which was really two things. One was effectively who the founders were and their backgrounds. And then two was the business model.
And for us, we believe, and certainly the Flights of Quality has demonstrated this, that software investing is where you derive capital returns within venture and then growth equity in late stage. The data is pretty demonstrable within fintech specifically that repeat founders dramatically outperform. There is an 80 plus percent correlation between over a billion dollar outcome
When we last caught up in New York, you mentioned that your AI can predict startup success to 90% certainty at the seed and series stage. How is that possible?
We are still obviously testing and refining the model. We have a pretty incredible Gen AI driven predictive model for effectively evaluating founder DNA. Gen AI has changed not only what we invest in, but how we invest. And we have a director of AI at the firm. And he and I and the rest of the team have been refining this model. We invest strictly in repeat founders and seasoned entrepreneurs.
So how do you evaluate them and score them ABC from the attributes that... We've assigned, which are about 35 weighted criteria. And from there, we want to predict, is this the type of founder that's going to generate a 5 to 10x net outcome at the earliest stages? To be inside stack ranks, the top 250 fintechs every year. There's a couple of other Fortune 50, etc.
They all have these ranking lists or these lists of the top fintechs. 50, top 250. So we've done backtesting against those. We've also done backtesting against our own portfolio. And we've found a number of interesting signals from that. With the Gen AI teaching the model, it's refining our percentages, refining our weightings. That started with
kind of multiple basis points, like three to 5% changes in weightings. Now it's down to, you know, small basis point tweaks. So we really feel like we're at a point now where we just keep feeding it more and more data and then on the margins, obviously continue to refine it. So we've looked at in training data sets, YC data, we've looked at a pitch book data.
We've looked at companies that have exited for over a billion dollars, companies that have exited over $500 million and And we've also looked at in terms of how much money have these companies been able to raise as a proxy of success. And we've used all those training data sets to refine our model.
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Chapter 2: How does Fin Capital use AI to evaluate startups?
And so we wanted to create automation and scraping and other things around that to take a lot of the guesswork and lift off of our portfolio companies and then be able to utilize that to create signal on both asymmetric growth and companies that are breaking out, as well as companies that might be struggling relative to cash burn or other dynamics so that we can get out ahead of that.
Asking companies for that data on a biannual or annual basis does you no good. You have to be looking at it every week.
You've really been thoughtful about your LP base. Talk to me about your LP base. Why is it strategic? How did you come about building it?
We're about 80% institutional, 20% family office. We have our share of foundations, pensions, endowments, but we've really leaned in to this notion of how do we derive signal from the market
um in terms of our thematic thesis building how do we de-risk diligence and and work with companies and validate their technology works and it's going to be adopted and then third how do we drive meaningful commercial adoption so i thought about it initially on those three elements and those are still the three elements that we think about with our strategic lps and those include large banks all around the world um
It includes large asset managers, large wealth managers, large insurers, and then corporates. Corporates are largely public fintechs for us. And the signal we get from them in terms of, hey, here are my innovation goals for the next three, five years. Here are my massive pain points across my front, middle and back office. Here are the RFPs that I have coming up.
And by the way, here's access to our technology team and our business line leaders if you need to pick their brain on specific themes or specific companies or get them to engage in some kind of proof of concept. So our mode of going through diligence is Here, we're going to introduce you to all these prospective customers. CEOs love it because these are shots on goal for customers.
We love it because it's a massive signal on the diligence side for how they're going to go to market, how they're going to engage. And we'll often join those meetings. And so this is another way we win term sheet battles. And it gives us incredible signal in terms of their pitch, their demo, what the reactions are, what the questions are, what the friction looks like.
And if that goes incredibly well, that's a really nice thing to lean into in terms of having more conviction and leading that round. So the strategic LP base is fundamental.
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