Jon Fry, CEO of Lendflow, on how AI is finally delivering on the promise of embedded lending

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In this episode, I sit down with Jon Fry, CEO and co-founder of Lendflow, to explore how AI is transforming the embedded lending landscape. Jon shares his unconventional journey from building websites in college to becoming a key player in embedded lending, including the challenge of launching LendFlow in early 2020, just as COVID shut down the lending market.

The conversation dives deep into how LendFlow’s AI-powered platform is making lending more efficient and accessible, why the small business lending space hasn’t advanced as quickly as many expected, and what survival through multiple market cycles has taught the industry’s most resilient players. Jon paints a compelling vision of the next five to ten years, where AI will create truly magical lending experiences and finally deliver on the promise of embedded finance for small businesses.

In this podcast, you will learn:

• How Jon went from building websites in college to starting Lendflow.
• What it was like launching a lending software business in early 2020.
• What Lendflow does exactly.
• Who is their core customer.
• What has changed in embedded lending in the last five years.
• How brands can customize their loan offerings with Lendflow’s platform.
• Why Lendflow encourages brands to connect directly with lenders.
• What a neutral embedded lending network is.
• How Lendflow uses AI and what problems it is solving.
• Why we are not quite ready for a fully agentic workflow yet.
• What a fully agentic workflow could look like in the future.
• Why Jon thinks lending software hasn’t developed that quickly in the past decade.
• What he thinks the next five to ten years holds for lending.

Read a transcription of our conversation below.

Fintech One on One Podcast No. 556: Jon Fry

Jon Fry:

So I think fully agentic, I think is a good way off for a good reason, but you try to make different parts of the process more agentic and then overall the entire workflow becomes more and more agentic over time. That’s the goal, but in credit, it is important to have humans in the loop as well, right? It’s just that you want them doing more high value activities. You want them doing as little, let’s say busy work as possible. You want them when the file comes to them and it has a credit memo, you had an agent just write an entire credit memo for you. have everything prepared and perfect. Everything’s packaged because everything’s gone through a process and been refined by AI before it even gets to you.

Peter Renton:

This is the Fintech One-on-One podcast, the show for Fintech enthusiasts looking to better understand the leaders shaping Fintech and banking today. My name is Peter Renton and since 2013, I’ve been conducting in-depth interviews with Fintech founders and banking executives. In this episode, we are going back to the lending space for a deep dive on embedded lending with Jon Fry, CEO and founder of LendFlow. Jon shares his journey from building websites in college to his interest in small business lending and founding LendFlow in 2019, highlighting the challenges of starting a lending business just before COVID hit. Jon explains in detail how LendFlow’s AI-powered platform works, providing many examples of how AI is enhancing distribution and operational efficiency in lending. And make sure you stay tuned right to the end where Jonn paints a picture of where this is all heading over the next decade. Now let’s get on with the show.

Welcome to the podcast, Jon.

JF: Great to be here, Peter. Thanks for having me on. Excited for the conversation today.

PR: Likewise, likewise. Great to have you. So let’s kick it off by giving listeners a little bit of background. I know you’ve been doing Lendflow for several years, but why don’t you hit on some of the high points of your career to date.

JF: Sure, sure. I really started when I was in college, building all different types of websites, driving traffic, building audiences, monetizing those with different products and services. And that’s when I really started to get into hone in on financial services. When I graduated, determined instead of going and accepting one of the job offers, just to try to continue to build and grow that, the whole time just building different concepts, applications, all different kinds of websites, think of at least a few hundred and continue to hone my skills in that area. So built up quite a lot, especially on the data front and that became over time incredibly valuable, especially when I started to find pockets of new customers where it had a pretty quick big impact. And that became like financial services, like I said, then it was more lending and then it became SMB lending is the area where I really honed in and built up some great relationships and customers, you know, starting about, you know, 12, 13, 14 years or so ago and became a really valuable service provider for them and continue to, you know, learn more about the space and what’s needed and started to work with them in more more capacity, right?

Just became from like customer acquisition to helping them with maybe other channels, to helping them with optimizing the onboarding flow, to helping to automate underwriting and doing things of that sort. So really started to dive into the tech too. And this is when, also when a lot of the new APIs were starting to come out, know, even eSign was a big one for a conversion rate way back then. It was before it was like Scan, hard to believe, at a time before it was as easy as eSign. And then APIs like Plaid and it started to come out. so, was a big part of that and implementing a lot of those early solutions and then used a lot of that knowledge to build out some automated underwriting systems that are own LOS in-house and used a lot of those angles and insights and relationships and conversations with both merchants, businesses and lenders to determine, you know, kind of the holistic platform that brought really brought everything together to solve a lot of problems that we saw in the space and the opportunity that we saw to come in and have a big impact in the space. So that was the genus and Lendflow how we got started. And we really kicked things off 2019 and launched early 2020 with our first customers, which turned out to be maybe not the most ideal time, but it never is. Right. So.

PR: Well, yeah, you couldn’t have picked the worst time in some ways for small business, because small business lending basically shut down for over a year with the PPP. So tell us a little bit about that first year. mean, how did you do it?

JF: So, I mean, in startups, the good thing is that we had, everything before was all bootstrap, right? So was all just from ground up, figuring things out and having to make it meet by the revenue that you’re able to generate. And so it helps you develop the muscle for that pretty quickly. And you have to be pretty scrappy and find ways to find a way through all different types of curve balls and all different types of situations. So we’ve seen a lot of those before.

And that was pretty needed for handling this one because not only all merchants shut down, well, the lenders shut down as well because, they’re not going to serve businesses who aren’t able to operate. That doesn’t make sense. And so it was really pretty much like overnight. I think it was basically the day after maybe they, I think they stopped like an NBA game or there was some events that I remember like the next day was the day. And everyone was pretty much just pulling back and stopping everything. So for us, it was, well, how do we handle this? We also had other businesses as well that were highly impacted by this. Back then, it wasn’t just Lendflow that was just like the new business that we were starting to build from the ground up. And so we started to focus on, well, how can we help with the PPP?

We played a role there. were active and engaged with, I think, people from different government agencies and different companies that were trying to solve this problem. So we started to build in this area, too, to see how we could be helpful and did some of that. We also put a bigger focus on asset-backed lending and products that were loan products that were backed by assets. And then we just really put our heads down and focused on building for when we would get through this.

So it had kind of expanded our product set and our lender set more quickly in areas than it probably would have otherwise. We learned a lot about the PPP and everyone who was working and building on that as well. We didn’t go too deeply into that because I think we determined probably compared to some of the other FinTech players that were really taking this head on, we were just too small and early, to play a big role there. And, yeah, I mean, we just kind of scrapped until lending picks back up. And I think it was maybe like September, November, where we started to get traction back up, which is great. But it was certainly a difficult time and a confusing time to navigate. But it did really take some of those prior lessons to know that like, we’ll weather this storm just like all the others.

PR: Yeah. Well, you did, obviously you’re here now with a thriving business. Maybe just explain to the listeners who haven’t heard of Lendflow. What do you guys do?

JF: So we have embedded lending infrastructure that’s powered by AI, right? And the focus of our infrastructure to solve two primary problems in this space, right? That’s first, distribution, right? It’s being able to efficiently take a loan product and put it in front of your ideal customers to do that much more efficiently than had been done in the past. And the second is operational efficiency. How can we make the loan process more efficient and drive out waste in the process to create better experiences for the borrowers and to make the businesses who are operating these loan products and services more efficient to provide a better product, ultimate end product to the customer as well. And to drive some of that cost, of course, out of the product. So that’s a high level. We have three core product lines to do this. The first is Lendflow Connect, which is connecting into different lenders and brands, as well as all the tools to embed loan products into brands and the infrastructure to build out your own custom networks or marketplaces for offering different credit products. The second is Intelligence, which is the decisioning infrastructure and automation to do things like automate your decisioning, automate your approvals, automate pricing of loans, and really to get the loan offer and the approval to the customer as quickly as possible. And the last is Lendflow Automate, which is workflow automation and AI agentic, LLM automation as well to really ensure that you can drive as many humans in loop out of the process as well and eliminate as much inefficiency and waste from the process.

PR: Okay. So then are you selling primarily to banks, fintechs? And obviously you you said brands for the first product. Who were the sort of typical customers for each of those three?

JF: Yeah, I say lenders, alternative lenders is number one on one side. And then also brands is another big one. So, brands would include software platforms or payment platforms or apps or just anyone who has an audience of both SMBs or consumers. And then the other side, alternative lenders, which also includes banks as well, but we mostly work with alternative lenders as kind of our sweet spot.

PR: Right. Okay, you know, embedded lending’s been around for a while. I a decade ago it was just getting started. We were called it lending as a service. that, you know, it was pretty groundbreaking at the time, but now it’s obviously, it’s having a moment, It feels like in the last year or so where everyone’s talking about embedded lending. I’m curious, cause you’ve been, sounds like you’ve been around this space for a while, particularly on the tech side. So how do you see what’s happening with embedded lending today versus maybe five years ago as we’re just in the throes of the pandemic and maybe coming out of that. What really has been the changes that you’ve seen?

JF: Sure, so I think there’s likely, there’s been a lot of lessons learned, I’ll say, over the years, especially going through different market cycles very quickly. And it was also a very interesting, unique cycle where, well, there was free money being given out. So although it was like an upmarket, everyone was just getting free money and that’s really hard to compete with. And everyone will just take that and then that kind of becomes the expectation. Well, why isn’t…the money free. So that had its own challenges. But also, there’s a lot of implementations. There was a lot of push and a lot of companies that came out with the embedded lending concept around when we did in 2019, 2020, 2021. And there was a lot of lessons learned. a few of those are, of course, a lot of new technology as well. But a few of those, it’s difficult to cover an entire audience, right, with one product. And there’s a lot of different types of businesses that have different credit needs that match to their industry and the way that they run their business. And so trying to do that under one lender and one brand effectively is quite challenging. If you try to do it in-house, well, that’s especially challenging because it’s a very difficult business and you also have your core business. And so now you have to run both.

There’s been some who’ve cut back from that for this reason, right? They’re like, well, maybe we don’t want to be a financial services company plus a tech company. We don’t want to try to juggle both because we have all these challenges. And you’ve seen a lot pull back. You’ve also seen some like, well, we need to add a larger suite of products or maybe different products because maybe this one is great for some scenarios, but our customers are talking to us and tell us, well, we want another product. Maybe it’s a line of credit or maybe they have a specific cash flow gap that isn’t perfectly addressed by the product that is available. And for a lender, it’s quite difficult to serve an entire audience from a big platform, both from a product perspective, but also from a credit perspective, right? The credit spectrum and then the product suite, right? How can you bring all of those to best serve that audience? It’s incredibly challenging. So I think you’re seeing more and more companies realize that, It’s kind of happens to be where we said too that having a number of multiple lenders to have better coverage of those three areas can provide a much better experience for your customers. They can provide like the right product at the right time, more likely for them. And so you’re not trying to, you know, fit a round peg into a square hole. You can select the different lenders that you’d like to serve your audience. And then based on the context and the input that the customer gives you or the touch point that they come from, you can make sure they get to the product that’s actually going to be the best fit for them. Right? And so they don’t have to now go, well, this isn’t right for me. Now I need to go back out into the wilderness and try to figure this out on my own. And that’s quite difficult. There’s so much information asymmetry in lending. It’s really difficult for them to do that. And that’s what embedded, a lot of what embedded lending meant to solve. And yeah, there’s a lot of lessons learned there and you can see the adjustments in the market of how the programs are changing over time because of this dynamic.

PR: Right. Right. So when you’re working with like a SaaS platform, vertical SaaS is hot. I everyone’s talking about it because it’s such a great market because I’ve argued this and others in the industry have as well, that every single vertical SaaS player of any, if you’ve got relatively small scale, should be processing payments and you should be providing loans. There’s still a lot that haven’t got there yet, as I’m sure you’re aware.

So I’m curious, so you’re working with a SaaS platform and they’re going to have a range of different companies. You talked about the different credit perspective, the credit box, right? Could you provide a solution for a SaaS platform that will say, here is a great business, 20 years in business, could probably get an SBA loan versus a company that’s two years in business, really just getting started, probably will not get a SBA loan, but might be able to get a term loan or some sort of MCA product. Are you going out there and providing the SaaS platform with like a suite of products that they can select for? How does it work?

JF: Yep, that’s right. And there’s some new nuance to this. A little bit more I’ll go through, but from the brand’s perspective, yes, you can select the products and the lenders that you’d like and also how you’d like those products to be delivered. For example, how you’d like to be ranked, if there’s more than one product. But if you get five results, do you want to route to one lender at a time or do you want to route to five at once? Do you want to, if the offers are returned, do you want to rank them by price or term length or by offer amount? Do you want sliders so they can adjust to their offers? All of these different things are considerations. And of course we have these templates ready to go out of the box for this. But if you wanted to make any adjustments or changes, you can self-serve and go into the product and do that, or you can just ask us to do it and we’ll make the changes and they’ll immediately reflect within your implementation. And so it gives you really the control and flexibility to say, hey, I want to offer these products, or I want to start here, you have all the transparency to everything that’s going on, all the offers, rates, terms, all the communication layer between the customer. Your customers are always in sync and have the information, the same that you have, the same that the lender on the other side has. So you’re always in the loop and you can always audit that. And then you can over time very quickly iterate and adjust your program based on your preferences and say, hey, you know, if it’s, want the top three products ranked by this. If the term length or rates doesn’t match my preferences, just hide the offer. Don’t even show it. Or if it’s rank four, I only want to show the top three. So just hide that one. So you can get really creative with your programs, or you can just use what’s ready to go out of the box. We have templates for that as well. So, it gives them a lot of flexibility.

The other thing that I think nuance is important there that I referenced earlier is. We’ve really opened up the platform so we can manage this for you, but you also can just manage it yourself and work directly with the lender counterparty, right? You don’t need to use our managed service. You can just use our Rails. So we’re seeing more and more of that where we’re just the infrastructure. And this is really the role that we prefer to play, right? Is where we’re just the Rails and infrastructure and you can partner directly. We already have all of the lenders all integrated. You can go in, get the API keys from each one, input them, and then you can run it directly however you want it with those lenders if you’d like, and we can support you in that. We can manage it for you or you can self-manage it with the lenders. So it’s a little bit newer. It’s been maybe 18 months, but we’ve seen a lot of this lately where there’s more self-serve in this way.

PR: Makes sense. then are you working with lenders like on both sides? Cause you talk about, you said lenders launching a second product or a different or a new product line work with you, but then you’ve got lenders that are on the other side with the, with the SAS platform providing financing directly. Some of the lenders you work with, it’s like a, it’s almost like a two sided offering.

JF: Well, lenders are actually the ones that we work with because of what I just said more often. They’re really most of the time who are going to because they want to go and become a better lender. They want the capabilities of taking their loan products and going out and to win their own brand partnerships. So we really sell into the lenders to give them lot of the capabilities that they need to be competitive and to have a lot of the experience and the SLAs that those brands are going to require. And to have that fully embedded experience, the instant offers, the great magical experience that they want for their brand when they offer these loans to these customers. The best products and the best possible experience. And so we bring that all to the lender and we also bring them the ability for them to build their own private marketplace that sits side by side to their product. And so they can effectively cover the entire audience if that’s what the customer wants. We really work most closely with lenders and then they go out and use our infrastructure to go and win these different partnerships and we’re just there, you know, their support in doing that, the technology rails and support for making that happen. And, you know, we support them in a bunch of different ways to do that. Yeah, that’s the most common way.

PR: So is that what you’re referring to? I was looking at your website and you have this term that I hadn’t heard before, a neutral embedded lending network. Maybe explain exactly what that is.

JF: Yeah, it’s a good time to bring that in. It just means that it’s not opinionated by us. Like we have that as a template where we have a lot of data and we’ve built out different templates for things. But like I said, you can come in and route and build the routing however you like, right? It’s not necessarily defined by us and it’s not a black box where a lot of marketplaces, that’s kind of what it became like, hey, someone applies and you don’t really know what’s happening and where it goes. And then maybe you get a report later, but you don’t know your customer’s journey experience or all the offers or things that they’re getting. And that can be problematic for brands because they care very much like these SaaS companies about their core product and the reputational risk that comes with offering additional financial services is really top of mind for them, right? They want to provide the best in class experience. And if it’s going into a black box, that’s very difficult to know if you’re doing that. And so we provide that level of transparency and the ability to set up all of the routing and infrastructure to your preferences and to the lender’s preferences who you’re working with. So you can set it up however you like. And to your point, if you wanted to build your own in-house product, you can do that. We’ve had customers do that too and to make that easy. But it’s really, you know, we try to just make it flexible and modular to build it how you want it. And that’s what it means by neutral.

PR: Right. Gotcha. Okay. That makes sense. All right. So let’s dive into AI. you talked about when I first asked you to describe Lendflow, but maybe you can share some specific examples of how you’re using AI and what problems that it’s solving that was not possible when you started the company.

JF: Sure, AI has just been a real, just accelerant for the second part of the platform, a problem that we’re trying to solve under operational efficiency. It’s also helped with distribution as well, certainly, but it’s accelerated pretty substantially the ability to drive waste out of the end-to-end lending process, lending machine that these lenders have built. There’s different touch points through the process even to today, you know, we’ve talked about how far lending common has really come a long way, but there’s still a lot of humans in the loop. There are still a lot of manual processes and typically the further up market you go, the more problematic this is and it can still take two to eight weeks and a lot of different departments, you know, from these large scaled lenders. And it’s going through a lot of departments. There’s a lot of human hands on it. And that leads to a lot worse experience, right? Which is harder to get distribution, right? And also leads to higher cost of capital for the end customer and just a more poor experience from the customer and can break trust as well. Or maybe they drop out because they go somewhere else because it’s just too frustrating and taking too long.

Examples of this are first, like initiating the relationship with the customer and how you do that. AI can play a role in better targeting and making sure that that’s happening using the data and the context from the platform or other proprietary data or other third party data that you have to do that in a more efficient and effective way. It’s when they start the application, when they drop out and being able to quickly, as soon as you recognize that you have all the context around it of where they dropped out, reaching out with voice AI, getting them back in, also text and email, having the full suite of the communication layer, reach out to the customer, depending on where they drop. It’s also, once they upload documents and information, it’s enriching the file with additional information, so you have more context more quickly. It’s when they have documents transcribing all the information, much of that is still done by hand. There’s a tax return or a…you know, some type of document or driver, they need a driver’s license or whatever it is. need to validate it, take the information, compare it, or they need to take all the information, sit and transcribe it from a tax return or from the bank statements. There’s been some tech for quite a while with some great companies, especially on the bank statement side, but there’s all different types of documents and still many, many scenarios where there are humans still transcribing that. And it’s hugely…even categorizing what type of doc it is, downloading it, categorizing it, uploading it in another system, transcribing all the information, then writing a set of credit policy rules calculations to make sure that it still meets, it’s within the guidelines of the policy. So, you know, there’s setting conversations with people for Discovery Call to ask questions, to learn more about context, about what the use of funds is for. There is presenting the offer when it’s prepared and ready and explaining all the details about it. There’s the final closing funding call to validate all of the information before the wire goes out. There’s renewals. There’s engaging when they drop out, declines in servicing. There may be their risk at default because of a risk signal from their transactional data or some other data signal or if they miss a payment or if their account goes into the negative. There’s all these different touch points through the end-to-end process where you can provide a ton of value. We specialize in just going really deep in lending, understanding all of those touch points deeply, and having a platform of just like the other components, flexible and modular to build custom AI agents to address each of those.

PR: Interesting. I was wondering whether you’re going to get to AI agents because I see it on your website and all that was really great because you gave us probably 10 examples there of different ways you’re doing it. Are you thinking this is, know, like is there an agentic type workflow here? I mean, how do you think about it when it comes to sort of the credit infrastructure you just described?

JF: Yeah, so I think fully agentic, I think is a good way off for a good reason, but you try to make different parts of the process more agentic and then overall the entire workflow becomes more more agentic over time. That’s the goal, but in credit, it’s also really important to have, it is important to have humans in the loop as well, right? It’s just that you want them doing more high value activities. You want them doing as little, let’s say busy work as possible. You want them when the file comes to them and it has a credit memo, you had an agent just write an entire credit memo for you. have everything prepared and perfect. Everything’s packaged because everything’s gone through a process and been refined by AI before it even gets to you.

Right? And each of those steps, there’s less lag and delay because now it’s all programmatic, right? It all happens on a schedule, but there are times where you need your reps sitting behind that. Those agents can book a meeting or transfer a call, or sent them over to you, your credit risk team is going to evaluate the decisions that were made and everything. And that’s a really critical part of the process, especially with agents. It’s so critical because you need all of that information for understanding what has gone wrong. So you can write better evals and create a feedback loop for improving the agents over time, right? Which is actually more important than just having an agent, how you’re evaluating that agent over time and how you’re improving it over time. So it can become more and more accurate and then it can have more and more agency as well. There’s something like a full lending process. You really just think about there’s actually many different agents. It’s how they’re coordinated and how they’re triggered and how they all read from the context of the other agents and how to do that very efficiently. And so for us, that’s a big focus. How can we get the context and put it in a way that’s really easy for them to access? So the agent has actually all the knowledge about the business and all of their past applications and everything about them that it can pull from to make the conversations really powerful, actually more knowledgeable and be able to provide more information than a human could. Because they can instantly see all past applications, hey, you applied last September, or you had a…offer for this or use your use case last year was this, you know, was a cashflow gap because of a new project you were taking on is that this still kind of growth, you know, issue that you’re hitting. And if you deliver and have all of that context in one place, then you that’s where really you can start to make the magic happen with AI because if they’re accessing it efficiently, they can have really great conversations and they can also prepare reports and prep your team for those conversations as well, which we do both of those. That’s really, I think, really the future is stitching together a lot of these and then making sure they work together as a cohesive group and over time allowing them to develop more and more agency.

PR: Right. Well, let’s build that out a little bit then.

JF: Sure.

PR: You know, I feel like, you know, particularly small business lending, I’ve been a little disappointed in how much progress we’ve made over the last decade because I thought we’d be further along than we are now. Now I think there’s a lot more small businesses getting funded than ever before, which I think is great, but I felt like the technology would be further along. And anyway, it feels like we’re on the cusp of a major transformation, particularly when it comes to small business lending with lot of the things you just described there. So, you know, what I’d like to do as we start to close is give your perspective on, I mean, obviously you’ve got your own product roadmap. You’re really seeing how the, the AI models are getting better and better, but take us out five years. And what do you think is going to be different? Embedded lending is I’m bullish on the…on that as a concept, I’m sure you are too, but maybe you could paint a picture of that how embedded landing is using AI and what it will look like five years from now.

JF: Certainly. I would like to preface it, if it’s okay with your kind of first statement about the disappointment in progress. Because I think that’ll tie in nicely to where things are going as well. I agree with you in some sense. I thought things would be farther along too. Back in identifying all these problems back in like 2012, it’s like, okay, there’s a path to fix this. These APIs are coming. We’re on our way. For one reason or another, it actually hasn’t moved quite as quickly, even though there’s been a proliferation of all of these different data APIs that can help support the process. But you also remember there’s companies who come in and really try to be that technology company and the lender to kind of solve both simultaneously. And that’s a really extraordinarily challenging combination.

The ones who tried to do that, you’ve noticed maybe there’s been an acquisition or the outcome hasn’t been ideal. And it’s because, there’s just this, you’re mixing kind of like an ordinance science. You’re trying to grow as fast as you can, the venture backing encourages that. But also you have credit risk and that’s your growth lever. So you are always tempted to kind of expand into new product areas, credit spectrums, different types of customers, different products, and then that adds even more more complexity because each of those are essentially like different businesses, some similarities, but they become quite different when you break out and that can quickly overwhelm.

The ones who’ve survived, have been the most successful are actually the ones who have moved a little bit slower and been really methodical and focused about the industry and the product that they have and the market that they serve. And that has actually allowed them to survive through up and down cycles. I don’t…like, it’s incredibly impressive is what I’m trying all of those because of all the challenges, know, the market challenges that have come up over the past 15 years for the ones who are standing, it’s been taken like extreme dedication, commitment, focus. And that meant sometimes like, focus on being a financial services company and maybe not deploying as much money into the technology. And the ones who tried to go too heavy on technology actually maybe became a little bit of a Achilles heel because it allowed them to change levers too quickly. And then, you know, it just takes one mistake and one market shock and one wrong segment of your book for things to unravel pretty quickly. And you’ve seen that. So I think that’s part of the why. Like why we haven’t moved as fast. Maybe the ones that were really pushing weren’t able to survive through multiple cycles. And the ones who have, well, it’s because they stay dedicated and focused to become a great financial services company.

I think now you’re seeing technology that’s coming along that’s going to enable them to kind of break through. Now you’re going to have a combination of people really resilient, right? And have built up systems for that. And now the technology is getting so good that it’s actually like really ready to be deployed in a way to be married to that, where you’re going to start to see the breakthroughs in this. And that’s where we sit. That’s like what role we want to play is like, there’s all these amazing lenders and amazing products. can, if we come in and augment what they’re doing and, you know, allow them to be flexible and modular and build their systems, how they’ve learned to build them best, allow them to iterate quickly based on the data, then they’re going to be able to have the breakthroughs without some of the risks that some of those prior companies in the past had to undergo that maybe they didn’t survive through despite like being really, really innovative.

AI is just a massive accelerate on top of all of that. I that was happening anyway, but now you throw AI in the mix and now it’s, I don’t know, 10, 50X. We’ll see how much faster things were able to move, but the next five, 10 years are going to be really interesting because you have all of that and that’s gonna really super power embedded because the products are going to get better. The experiences are going to get better and brand platforms are going to be able to offer a better suite of products and better cover their audiences and better serve their customers without the reputational brand risk that concerned them so much in the past. I think that’ll all of this new innovation will allow that to be solved for them to build trust with their customers, along with a new revenue line, along with making them more sticky, along with just giving their customers a great magical experience in lending that, especially SMB lending, that’s been more difficult to come by in the past.

PR: OK, it’s exciting times for people like you and for, I think, all of the SaaS companies and everyone in the small business lending space. So we’ll have to leave it there, Jon. Really appreciate you coming on. Fascinating to learn more about your company.

JF: Appreciate it. Thanks for the time today.

PR: My pleasure. See you.

I wanted to follow up on what John just said there about creating a magical experience in lending. It reminds me of comments made by former SBA head and current Harvard professor Karen Mills. She talked about small business utopia in her book. And it always seemed to me that we were still a long way from making this reality. But now I can really see us as an industry making a quantum leap over the next 12 to 24 months. Where applying for a loan will become almost effortless and many more small businesses will be approved for credit. I wrote about this recently and I remain convinced that we’ll be making dramatic advances over the next couple of years here.

Anyway, that’s it for today’s show. If you enjoy these episodes, please go ahead and subscribe, a friend or leave a review. And thanks so much for listening.