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In this episode, I sit down with Tomás Campos, CEO and Co-Founder of Spinwheel, a fintech infrastructure company that’s revolutionizing how lenders and financial platforms access consumer credit data. With over 20 years in fintech, Tomás explains how Spinwheel can authenticate users and connect to all their liability accounts—credit cards, loans, mortgages, and more—using just a phone number and date of birth, achieving 95-99% coverage across different debt types.
The conversation explores how this technology is solving critical pain points in debt consolidation lending and how their AI-powered infrastructure orchestrates multiple data sources behind the scenes. Tomás also discusses their Optimize product, which saves users an average of $150 per month by creating personalized debt management plans, and shares his vision for using technology to transform consumer debt from an anchor into a lever for achieving financial goals.
In this podcast you will learn:
- What Tomás saw that led to the founding of Spinwheel.
- Why there is so much friction in connecting liability accounts.
- The different use cases for Spinwheel.
- What is so special about the phone number and date of birth data fields.
- Why passwords are a very bad way to verify a user.
- The coverage of the entire U.S. population Spinwheel has achieved.
- The data sources that they are still working on connecting with.
- The type of data they are extracting from these data sources.
- Where Spinwheel fits into the flow of a loan application process.
- How they using AI agents and AI in general.
- How Spinwheel Optimize is saving end users $150 per month.
- The long term vision for Spinwheel.
Read a transcription of our conversation below.
FINTECH ONE-ON-ONE PODCAST NO. 545 – TOMÁS CAMPOS
Tomás Campos: Two big numbers that are out there. The first is coverage on the actual end user, meaning if you give us your phone number and date of birth, can we actually authenticate you as the individual? And our coverage there is between 95% and 98%. And then on the liability side, our coverage there is also between 95% to 99%, depending on the type of liability. So credit cards we’re at 99.5%, personal loans we’re at 95%, student loans we’re at 99%, auto loans we’re around 90%. So, you can see that it sort of depends a little bit on the type of loan, but fundamentally, our goal here is to try and get to 100% universal coverage on every type of liability you have.
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. Today on the show, I am delighted to welcome Tomás Campos. He is the CEO and Co-Founder of Spinwheel. Now, Spinwheel is a fintech infrastructure company focused on consumer credit data. They have built what I would say is a very sophisticated system, that allows any lender, bank, marketplace, or PFM platform to connect quickly and easily to all of a consumer’s liability accounts, be it credit cards, personal loans, auto loans, mortgages, student loans, and more. They do this with just two data points, phone number and date of birth. Curious to learn more? I certainly was. Now let’s get on with the show.
PR: Welcome to the podcast, Tomás.
TC: Thanks for having me, Peter.
PR: My pleasure. So let’s kick it off by giving the listeners a little bit of background about yourself. Just tell us some of the highlights of your career to date.
TC: I’ve been in fintech for more than 20 years now. So, an old timer, I guess, in the space. You know, really cut my teeth on building infrastructure for one of the world’s largest prepaid payment networks. And that was sort of the, you know, where I really found the impact that you could have in fintech by building out core infrastructure. So, you know, did that for about 10 years, and then I spent the last 10 years building companies. So I’ve been a founder a couple of times now, heavily focused on how to build infrastructure for fintech.
PR: Okay. So then let’s talk about Spinwheel now. So, how did this idea come about? What happened that led to the founding of Spinwheel?
TC: Yeah, so it really came from the core thesis that when my co-founder and I saw the broader landscape of fintech infrastructure, there had been some fantastic value creation investments across the board. So think of payments, think of open banking infrastructure, think of banking as a service. So across the board you saw even, you know, in sort of investing, you saw these infrastructure capabilities that came up and really opened up how value can be created in the space. But we saw over and over again that, for us, the biggest piece of the puzzle was still missing, and that was on the liability side, the consumer credit side. And we kept coming back to this because when you look at the average household balance sheet in the U.S., you know, the average household balance sheet in the United States has more in liabilities, more in debt than they have in assets and investments combined. And then when you think about this also from an account standpoint, so the number of accounts that the average American has. The average American has more debt accounts, more liability accounts than they do bank accounts or investment accounts. And so we looked at this and said, why hasn’t there been this investment or this emergence of the same sort of substrate, this infrastructure, to power those markets that we saw across the board? And so that was the general thesis that needed to be created to unlock the value that we’d seen in other fintech infrastructure areas. But what really was sort of the spark that caused myself and my co-founder to jump into this was when some of my own family members started really struggling with their own financial obligations, their own debts, and came to me and asked for, what was the best app that they could use or what was the, or the solutions in the market that they could leverage to help them, you know, get better or improve their situations. And when you looked across the board, there really weren’t any; there was a bunch of failed solutions out there.
PR: Right.
TC: And then actually sort of underscored why we thought it was so important to build this, because when we looked at why these apps or these solutions never delivered on their promise, or failed, it’s because the infrastructure was missing. It was too hard to build value or create the value needed for the end consumer or for the business.
PR: Right, right. Well, yeah, as someone who’s been in the lending space for a long time now, I mean, focusing on it for the last 15 years, I know many of the companies that you’re probably referring to that, you know, basically started, even created a little bit of traction, but never kind of got to where they needed to go. And it’s been a perennial disappointment to me that some of these companies never made it because I feel like the personal financial management space should be focused on liabilities and it just hasn’t really, I mean, you can certainly connect your credit cards and your auto loan and your mortgage if you have one, but it doesn’t really do much other than…
TC: Yeah, to your point Peter, just think about what you just said. You just said your credit cards, your auto loan, your mortgage. For the average American, that’s connecting at least seven accounts.
PR: Right.
TC: So that means if you had to do it previously, that’s seven usernames and passwords you have to put in, and that’s seven, you know, have to do probably every four to five months. And so just right there is a great example of the friction that caused so many of these solutions to never take hold, or to really keep the consumer engaged on the liabilities because it’s just too hard to do and to keep up. And then to your point, if you did, if you were one of the few that actually did do that and kept up with it, then what were you gonna do? It would just sort of help you see what was in front of you. But if you wanted to take any action, you had to go somewhere else to do it.
PR: Yeah. Which is still the case, really, today for most of these. I have a PFM app that I use. I look at it every single day, and I’m conscientious. I connect things when they disconnect, and they disconnect regularly. And I still think it’s kind of crazy here in 2025 that we can’t seem to get the connections right. But anyway, I wanted to sort of dive into a little bit about the Spinwheel story, because I think you started with a student loan focus. And now today, you’re even moving beyond just the credit side. So tell us a little about how Spinwheel has evolved since you started.
TC: Yeah, that’s absolutely right. We did start with a focus on student debt, and the goal is to always provide the solution across all consumer debts. But when we looked at the landscape, student loans are very unique in a couple of ways. One, it’s the most complicated consumer debt that’s out there. You have multiple sub-loans nested under one servicer that contributes to an overall interest rate. Each of those sub-loans has its own terms, its own repayment plans, some are subsidized, some are not. And it was also the debt that most people get into at the earliest in their lives. And so we said, well, hey, that’s a great starting point to tackle this infrastructure. If we can get that right, then we can kind of tackle everything else. So that’s where we started. In March of 2020, we had the pandemic, and rightfully so, they paused all student loan payments. And at the time, the expectation was that maybe this pause would last for maybe a year or so. Three, almost four years later, and the pause was still in place. It made it really, really challenging for anybody who was doing business in the student loan space. And so that really helped us accelerate the coverage across all different consumer liabilities, which is where we’re at today.
PR: Okay, who is using Spinwheel, and what are some of the different use cases?
TC: Our clients kind of fall into three major buckets. The first bucket would be if you are a client that’s, let’s say, oftentimes a marketplace or a resource for consumers to, let’s say, learn about the consumer credit options available to them. So think about, let’s say you go to like a NerdWallet, and you try to learn about maybe what’s the best credit card for you, or maybe does it make sense for you to consolidate your debt with a personal loan, or all these sorts of different things you may decide you want to do. That client is very important for us because we can help them personalize the experience for that end user. We can help them use the data that the consumer actually has, and then match them with the right product, with the best product that they might be able to get for whatever the intent they have is. So that’s sort of the first use case. So think about it as sort of the discovery and the matching between the consumer and the consumer financial products that are out there.
The second would be the lenders or the issuers that offer those products. So the use case there is being able to clearly understand the needs of the end consumer, leveraging the data, the actual and live data that the consumer has, to price the loan, the interest rates, and the terms appropriately and better, oftentimes better, for the end consumer. And then ultimately being able to disperse the funds from the loan, from the new loan, to any existing loans that are out there. So think about things like transferring the balance on a credit card, or if you’re consolidating existing credit card debt with a personal loan, we facilitate the ability for those payments to be directly dispersed to each of those loans without the end consumer having to go and upload statements or log into their accounts or anything like that. It’s all done with just a phone number and date of birth. And then the third use case would be what you just mentioned. So think about those personal financial management use cases where the end consumer is just trying to stay on top of the liabilities and the debts they have today. This might be things like understanding the due date or understanding what your minimum payment due is, or what the interest rate is on that, and being able to make the right decisions as to maybe which ones you pay off, which ones you pay a little bit more, or which ones you maybe just pay the minimum. But being able to do that seamlessly without having to go through the friction we just talked about earlier, of connecting all your accounts one by one and being able to make those payments all in one place. So those are the three major use cases that we see today. But they’re all connected because they help the consumer across their entire journey with their consumer credit.
PR: Right. So you just mentioned it, but I do want to dive in a little bit. I know you’re not going to give away your secret sauce, but the reality is you’ve said that you can find all of the liability counts with a phone number and date of birth, not even an email address. Cause phone number is obviously a unique field. Date of birth is not. I’d just love to kind of get a sense of the infrastructure that you’ve built; you decided that those two data points were all you needed? What can you tell us about those two data fields that are so special?
TC: It’s important to note that if you just sort of take a step back today, and you think about the way you, or I, or anybody typically authenticate ourselves and verify our identity to third parties, using passwords is probably the most prevalent one that we’re familiar with. But that’s not actually a great way to authenticate Peter or Tomás. All that communicates is that somebody knows the password.
PR: Right.
TC: And is using a password to Peter’s accounts or Tomás’s accounts. So that’s not really a great way to prove that it’s Tomás or Peter. Then you can look at other solutions out there, like out-of-wallet questions, which are oftentimes called knowledge-based answers, right? So this is where I asked you what street you grew up on, or did you own this car in the past, or what high school did you attend? Things like that. And those oftentimes are used also to verify an individual. But those are also pretty bad as well, because especially today, most of that stuff can be socially engineered or discovered through social media. When we looked at this, we took a step back and said, if we could fundamentally demonstrate that we can authenticate the individual and verify their identity, that is much better than the other solutions we just talked about, which in those two solutions, I just talked about KBAs and username and passwords, that’s a common way that today we interface with our accounts. So that phone number and date of birth essentially allows us to authenticate and verify you because we go out to multiple data sources in the backend, and all those data sources have to point back to the right individual. We are then able to do things like look at attributes around the device, so your phone itself. We can prove that you actually have possession of your device by sending a text message to you on that device, a one-time passcode, right? So all these technologies behind the scenes are being used to verify that you are who you are and that your identity, that you essentially have authenticated yourself. Once we do that, then we’re able to use that authenticated identity, authenticated Peter or Tomás, to then go back and work with all of our data partners to retrieve your data as you. So that’s like a very, very fundamental thing that we’re doing here that really has removed so much of the friction.
PR: Right. So, you’re not going into the person’s checking account per se and just looking at all the transactions there. It sounds like you’re going into other data sources, because the checking account has liabilities, you know, incoming and outgoing, but you’ve been focused on the liabilities for the most part. So, how are you getting a clear picture? Like the data sources you’re using, what kind of coverage are we talking about here? And I am not a typical user, I have 16 credit cards. All of them are connected to my PFM. And I also have a spreadsheet with like 40 different records in it of different assets that I have. So that’s not typical, but I’m curious about those two pieces of information, your data sources, what kind of coverage are we talking about here?
TC: Yeah, so I usually like to think about it as there are two big numbers that are out there. The first is coverage on the actual end user, meaning, if you give us your phone or date of birth, can we actually authenticate you as the individual? Our coverage there is between 95% and 98%. And then on the liability side, our coverage there is also between 95% to 99% depending on the type of liability. So, credit cards we’re at 99.5%. You know, personal loans we’re around 95%, student loans we’re at 99%, and auto loans we’re around 90%. So, you can see that it sort of depends a little bit on the type of loan, but fundamentally, our goal here is to try and get to 100% universal coverage on every type of liability that you have.
PR: And so the liabilities that you don’t have, you’re just not able to authenticate correctly? Or there are all kinds of different types of liabilities, some that have very, very offline type coverage? What prevents you from the 100% today?
TC: It really is just about how quickly we can tap into those data sources. So, let’s talk about a couple of them. So, let’s take tax debt, for example. So if you have maybe a tax lien on a property or you owe taxes, that’s also considered a liability. We, today, have not completed our integration into the IRS to go and pull that back, but that’s something on a roadmap, right? So that’d be an example where that’s not there today, but we will pull it in. Medical debt is also one that’s very, very tricky. One, because the definition of medical debt itself and how that gets reported is still gray, right? But we feel that once we have a very clear understanding of what really constitutes medical debt and in what use cases it should be surfaced, the goal is to be able to tap into those data sources and be able to pull in medical debt as well. But those are just two examples of where we may not be able to pull it in today, but it’s something that’s on the roadmap to pull it in.
PR: And when you’re pulling in, are you pulling in just an account balance? Are you pulling in payment behavior? What kind of data do you pull in?
TC: Usually it’s all the above. So it’s a combination of the existence of the account, the account balance itself, and the attributes around that balance. So, typically it’s going to be a due date and the minimum payment due, and then also typically the payment history. So it would be, have you made the payments, and what is the nature of those payments? What’s the size of those payments on that account?
PR: Gotcha. Okay. So then let’s talk about the lending use case. I think that’s one that is really interesting to me. How is a lender using Spinwheel? Like obviously, they had, before Spinwheel, they had their credit reports, they had cash flow underwriting that they can use. Where does Spinwheel fit into kind of the whole workflow of a lender when a potential borrower is coming in, they are the seeking like a new loan or potentially increasing in a credit line or what have you, where does it fit?
TC: So, two big areas for lenders. The first one is really gonna be more around streamlining the application process and receiving more accurate data. And this is across any type of loan, any type of lender, right? So, if I can reduce the amount of data and the number of fields you have to enter in manually, Peter, that’s a good solution. And as long as it’s verified information, it’s a good solution for the lender, and it’s a good solution for you, right? It removes errors. It removes gaps. It also makes it easier for the consumer to get to the point where they can actually see the loan offers and things like that. So, when you think about the very first use case across all lenders, it is how do we streamline that application process and provide them with accurate information, both about the individual and their existing debts. And so that helps them get a better picture of their DTI, that gets a better picture of the individual. So that’s one of the key use cases. The second key use case in lending is really around debt consolidation. So when you think about debt consolidation, this is typically going to be a loan that you are taking out, a new loan you’re taking out to pay off or pay down existing accounts, right? So that might be a refi, that might be a personal loan to consolidate your credit cards. It might be a HELOC to consolidate, maybe your auto loan and some of your other high-interest rate loans like credit cards. It also includes use cases like credit card balance transfers. So maybe you have an outstanding balance with one credit card and you want to transfer that balance to another because you’re being offered a promotional rate for transferring a balance.
All of those fit into this use case that we call this sort of debt consolidation use case. And in that scenario, where they use us, it’s for one, ensuring that they have visibility of those accounts, those other accounts that you have that you’re going to be consolidating, right? Accuracy there. Two is understanding the real-time balances and payoff amounts for each of those accounts. And the third is being able to disperse the funds, send the payments directly to each of those accounts. So that’s what it is. Now, if you were to contrast that with how that’s being done today, these use cases today are relying on end users uploading statements, looking at their apps, typing in what their balances are, and trying to define what accounts they have. And most of the time, that’s not only error-prone, but it’s not even reliable. So today, most of the time, lenders are just saying, “Peter, tell me what you think your balance is, and I’m gonna send you a check. And I’m gonna trust, Peter, that you take that check and you go send the money to these other accounts to pay it off.” Well, whether you had all the right intentions or not, oftentimes that money never makes it to those other accounts. Maybe it’s because you forgot, or maybe it’s because when you got the loan, you figured you wanted to go to Vegas or whatever it is. And so the problem there is that when, as the lender, I underwrite you for a certain amount, expecting your DTI to stay in a range because you’re gonna pay off these accounts. Well, if you don’t ever pay off the accounts, your DTI is different, the loan’s gonna perform poorly, the interest rate should be different. And so just by that simple fact of being able to, one, know the accounts accurately, two, understand the balances and payoff amounts, and then send the funds directly to those accounts, it streamlines the entire process and makes it easier for these lenders to actually do debt consolidation loans.
PR: Well, I remember the growth of the fintech lenders very well like 10 years ago, when fintech lending was really getting some traction, and it was very hot, and VC money was flowing, you know, private credit money was flowing. And this was the big issue at the time, because you could tell. Obviously, they would pull credit after the fact. They go, oh, look, they took out a debt consolidation loan, but only 30%. I don’t remember the exact percent. Don’t hold me to it, but it was a smaller percentage than I thought, 30% when it actually paid off the debt, all the rest just kept it, and just moved on. And again, the payment behavior is going to be so different for those two types of borrowers. So, I could see how that does make it a very, very compelling use case for the lenders. So I want to ask a little bit about how you sort of built all this, and you go to your website, which I’m on on my other screen here, and you talk about a lot of things we’ve discussed. We chatted a few months ago when you were talking about how you really are an AI company. You’ve built a lot of the technology with the, you know, the latest AI models, but you don’t mention that much. You certainly don’t lead with it. I haven’t read the entire site, but you don’t lead with that. So, how are you using AI agents, and tell us how you’re using AI in general.
PR: And you’re right, we don’t lead with it because it’s a lot of how we do it. And while some people care about that, most people aren’t interested in the what or the why, not so much the how. But you’re absolutely right. From the very, very beginning of building the company, we understood that AI was gonna be one of the key levers to make this even all possible. So the best way to think about this, Peter, is when we go and connect to your account,I t’s not one integration to one data source. It’s actually seven or eight integrations on the backend to get all those different types of attributes. That might be a combination of bureau data, payment network data, bill payment data, public records data, and it’s not always a linear process. Imagine this, Peter, imagine today if you had to go and get all the information about your accounts. You’d probably have to go log into some apps. You may have to go make some phone calls. You’d probably have to go get some statements. You’d have to go to different sources to try and get the full picture of where it is today. Well, AI enables us to do that. It’s really that orchestration that we have to do across all these different data sources in the right way to be able to pull the information on your behalf. And that orchestration is extremely important, and you can’t really do that without AI. So, really what’s happening behind the scenes is we have an AI agent that gets spun up on your behalf to go and understand where all this is and to go through the processes to do it to pull it back for you in a very, very timely and accurate way. And that allows us to make our APIs a seamless, reliable API to our clients, when behind the scenes, it’s a very messy world, a very messy process, but we can use AI to make this very reliable and accurate.
PR: Given that you’ve been around for a few years now and the whole Gen AI revolution, shall we say, is still less than three years old since ChatGPT 3 came out, how have you kind of leveraged the new capabilities of these AI models that you didn’t have access to when you started the company?
TC: Here’s a great example. So, in a previous company, I had built a platform that did natural language processing and text-to-speech and speech-to-text for conversational commerce. And this was back in 2016, 2017. And that was a lot of work, and the accuracy and reliability wasn’t fantastic. It was good enough. If you look at where we are now, any of those same capabilities that we may have in terms of being able to process documents, process text, extract what you need from text, any of those things, that’s almost trivial now because of AI, right? So previously, I had to build a whole company to do that. And now we can plug into our own models that are running these AI agents to be able to process all of that. And so that’s to me a very, very clear example of how AI is helping just democratize things and lower the cost and increase the efficiency across the board.
PR: Right. It actually reminds me of a keynote we had at one of our LendIt events back in the day. I think it was 2016 or so. We had a keynote from one of the leaders in AI at Google and he gave this fascinating presentation about photo recognition and saying how photos of dogs versus cats and how to tell the difference. And back then, I think, I can’t remember what the number was, but it was reasonably good. It was like 95%, 96% you could tell a cat versus a dog. Whereas I’d say today, it’s got to be very close to a hundred percent.
TC: Oh, yeah.
PR: But at the time, it felt like, my God, this is so revolutionary. It had nothing to do with fintech, but it just showed the power of the technology, even back in like 2016. Anyway, I want to ask about this. One of your specific, it’s a product that you offer, the Optimize product. I found this really interesting because you say that, on average, companies using Spinwheel Optimize are saving their users $150 per month. Now that is not an insignificant amount of money. And I’d love to sort of get a little bit more color on how you’re doing that.
TC: From very, very early on, what we recognized was that there might be more, but there are typically four main intents or goals that an end user or a consumer may have. Being able to improve my credit score, being able to reduce the interest I’m paying on my loans. It might be to free up more cash flow each month, so maybe decreasing my monthly payments to each of my debts. And the other is, I have a goal for a large purchase, which might be trying to get my debt-to-income ratio into a certain level that’ll support whatever purchase I’m trying to make. Maybe it’s a home, maybe it’s a car, whatever it is. So those are the four goals that when we looked at Spinwheel Optimize, we recognized these drove most of the optimization. And it was unique because, for example, Peter, you may be in a position where you say, hey, you know what, my cash flow is fine if I maybe pay 20 % more per month and that lowers my interest rate by 10%, 15%, whatever it is, that might be what your goal is. But someone on the other side might be like, you know what, I don’t have the cash flow, and so, even if my interest rate’s slightly higher, but I get $50 or $100 more in my pocket every month for groceries and other commitments I have, that might be the goal for them. And so what Optimize did is it took these goals and applied weight to each of these, right? So someone may say, my primary goal is this, and my secondary goals are this. And we took those weights and we said, okay, if we can now understand your exact profile, all the details on your debt, we can not only understand how to support that goal and provide a plan exactly how to do that, but we can also make that plan actionable. So if part of the plan was to pay that 20% more every month, well, great, you could do that. You could automatically make those payments. If your goal was to consolidate the debt into a HELOC, for example, because you can save 15% on interest rates there, well, great, we could actually make the ability to apply and refi with that HELOC actionable through the APIs. And so that’s what Optimize really is. It’s the combination of the logic, those intents, the weights, the ability to spit out that plan, and then for that plan to be fully executable as part of our APIs. And what’s interesting, going back to that sort of AI discussion you said, Peter, is that when we started, this was still very kind of algorithmic, and users still had to be heavily involved in all of the different actions. What we’re seeing now is that, you know, probably within the next 18 to 24 months, that should be fully automated where an agent should do all of that for you, Peter, and it would just, you know, take your goals, you probably provide some guardrails, and it’ll just go execute and handle all of these things for you. You know, and would check in with you every now and then to tell you how it’s doing. But it’s fascinating how that’s gonna play out.
PR: Yeah, yeah, it sure is. So, last question then, I’d love to kind of get your vision for Spinwheel and where you think this is going. Are you staying on the debt side? And if so, do you see Spinwheel as a tool to help Americans kind of get out of debt or have a better financial situation? What are you trying to do here?
TC: The mission for us has always been anchored around how to provide better financial outcomes for the average American in the US. We can all relate to that as individuals in the company. The view here is that having debt and borrowing is not in itself a bad thing. For me, it’s enabled me to grow my wealth because I was able to purchase my house. There’s things like that where we’re borrowing money can actually be a lever for you to increase your wealth or achieve the goals you’re looking for. The challenge is that oftentimes it doesn’t act as that; it becomes something that can hold you back. And so our goal is that if we can provide the technology and the infrastructure that pushes the needle more towards that lever to help you achieve your goals versus the anchor that’s keeping you from achieving those, then we’re doing our job right. We wanna shift the pendulum and shift that lever more towards debt being something that’s a tool for you to achieve your goals. And the way we see to do that is through the companies that you engage with today, where you’re getting your loans, you’re paying your loans, you’re discovering them, you’re managing your money. And so just by that very nature, if we can improve the financial outcomes of which we can improve the businesses that are clients, and through them, they improve the financial outcomes of the individuals, we’re achieving our mission. And so where we’re ultimately going with all of this is we look at this and we say, anything that really can impact consumer credit, consumer data, if we can find a way through technology and infrastructure to improve that, then that’s sort of in the scope and the vision of what we’re trying to do. We’re not trying to be out there and be the next open banking player. We’re not out there trying to be the next banking as a service platform. Our focus is really on how we tackle what we think is one of the largest issues today, which is liabilities and consumer debt. And that, if you boil it down, is a function of data and actions. And so if we can get more data to help with that, we’re gonna go get it. If we can enable the actions that enable that, we’ll go do it. You can think of us as, we’re gonna be adding more data and more actions. We’re constantly doing that for the life of the company.
PR: It really is a noble cause because that can make such a huge difference in so many people’s lives. Anyway, Tomás, it was really fascinating chatting with you today. I appreciate you coming on the show.
TC: Thank you so much.
PR: You know, we were chatting after we stopped recording there, and I was lamenting the fact that we ran out of time and I never got to ask Tomás about fraud. He said, “No problem, fraud is really not that big of an issue.” He made the point, do you think fraudsters are going to want to hack into your account so they can pay off your credit card debt? Not very likely. It doesn’t mean that fraud can be ignored. It just means that when building infrastructure for managing liabilities, there is not the prevalence of attacks that you see with other infrastructure. Anyway, that’s it for today’s show. If you enjoy these episodes, please go ahead and subscribe, tell a friend, or leave a review. And thanks so much for listening.