‘As income data becomes more important, you can start to creatively rethink the paradigm of credit’: Pinwheel’s Kurt Lin

If data is the underpinning of modern finance, payroll data is expanding the pie. Early data firms like Plaid, MX, and Finicity made it easier for lenders and other fintech apps to access banking data. But now, firms like Pinwheel and Argyle are opening up payroll data. Traditional W2 work is evolving in the gig economy and being able to access payroll data wherever it resides creates new opportunities to serve customers. 

On today’s podcast, we have Kurt Lin, co-founder and CEO of Pinwheel. Lin’s vision is that over time, lenders will use the firm’s payroll API to track their borrowers’ financial health longitudinally. Fintech firms can assess borrowers’ current income, whether they’re showing up for shifts, whether they’re getting paid consistently. We’re at the beginning stage of payroll connectivity and Lin shares some interesting insights into current use cases, based off of income verification, direct deposit switching and payroll-linked lending. Lastly, we talk about where Pinwheel and payroll connectivity are headed in the future.

Kurt Lin is my guest today on the Tearsheet Podcast.

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The following excerpts were edited for clarity.

Intro

My name is Kurt Lin. I am the co founder and CEO of Pinwheel, the leading payroll connectivity API. We are building the income layer for the financial system. And we work with some of the largest players in the fintech world to help enable things like direct deposit switching, income verification, as well as a suite of other use cases, to build what we believe is the future of the financial system.

The impetus for starting Pinwheel is that for myself, as well as my team and my co founders, we're all second generation immigrants. Especially coming from a family with a very traditional Asian upbringing, the idea of carrying debt is anathema, frankly, to core parts of our culture. Growing up, my dad and mom never really applied for credit cards or anything, because they felt if you have the money, why would you do that? I should also add that my dad was this traditional Asian dad, who was very stoic and never really showed any emotions. It took the guy 50 to 60 years before he was even able to say I love you. That kind of gives you an understanding of who he was.

I remember growing up, when my dad finally went to go get his first mortgage. I remember coming with him as a little kid, and we went to bank after bank after bank, and were unable to get a mortgage, because he didn't have a credit score. It really stuck with me, because it was the first time in my life where I'd actually seen the impact that it had on him and seeing him crack. I could see how visibly frustrated and emotional he got, not being able to buy a home, to be able to fulfill this American dream that he had come here for. At the time, I didn't really understand what was going on. As I started to get older, and I thought back to that moment, it really started to click for me that the financial system doesn't work for a lot of people: either folks who are lower income and never been able to actually engage with it in a responsible way, or folks who are new to this country -- we just don't understand how it actually works.

A big reason why we built Pinwheel, why we continue to be so focused on fulfilling our vision, is because we see what we're doing has such a clear, tangible way to actually bridge that gap. There are so many problems that can be solved both by accessing and then increasing the visibility into this much wider world of data. To be able to say, hey, someone may have a lower FICO score, but because of all this new data that we have, we can actually show that this person is a responsible borrower. They should be able to access a much lower interest rate, as well as able to tap into someone's direct deposit and their paycheck in order to further de-risk things with the lender and increase financial inclusion for those who need it most.

Income is changing

First off, one of the things that we realized very early on in our journey is that income is this really broad idea that is ever changing. It used to be that every day you clock in and clock out, and you generate your income via this hourly rate. And then even with salaried workers, the kind of foundational unit of work has always been this hourly rate.

What we've seen now is, as the world has changed, especially with the future of work coming in, the definition of income is really broader. There are a couple things that are going on. Number one, there are still a number of people -- in fact, the majority of Americans are still working on these hourly systems -- but the big difference being that the payroll systems with which all the data is actually being collected and the systems that are actually using their platforms to distribute funds have finally gone into the cloud. That's versus these kind of like older school systems, a lot of which were on prem. And so we're finally seeing the infrastructure available to connect to this wide world of income providers -- that's one.

Two is you start to see this proliferation of all of these different gig workers: everyone from an Uber driver to DoorDash. Or those are these New Age platforms where income isn't being consolidated within these older school payroll systems, like an ADP or a Workday, for example. And then thirdly, we're really starting to see this pretty interesting shift away from these traditional modes of employment, where people are generating a substantial amount of income from everything like Etsy or Fiverr, really challenging that older world of what it means to have a job.

So we're seeing an increasing number of folks generating most of their money from these newer models. We also connect to those systems as well, in order to make sure that we have the widest breadth of coverage, and really can say, with the most amount of certainty, that this is the full income picture for said consumer and said member of the financial system.

The coverage challenge

The interesting thing is, as with any aggregation play, there is always some element of a power law distribution. There is a higher density of folks at the upper end of the spectrum. But the total hypothetical amount of platforms that can connect is endless, mostly because there are new problems that continue to arise as time goes on. And also because the landscape of employment is shifting pretty rapidly. And so it's our job to make sure that we're always staying as updated as possible and continuing to do everything we can to eventually cover every single possible income source throughout the system.

Credit scores

I think it's important to take a step back and think about how lenders assess risk. There are really two questions you have to ask yourself. What is the consumer's willingness to pay? And what is the consumer's ability to pay? FICO scores are supposed to be a proxy for that first question. Here's a high level directional signal of whether or not this person has the willingness to pay. All you have to do there is historicals. That's why credit scores are the way they are. They do serve a function.

But what's always been missing in this big kind of picture puzzle is the second piece. Like, how do you actually get the right information to know if someone even has the ability to pay? Do they even have the cash flow and the income to support what they're trying to acquire as far as like a financial product goes? We are really focused on that piece. I think it's the combination of those two, as well as the multitude of other alternative datasets, that allow lenders and other operators in the financial system to be able to make much smarter decisions, as well as make decisions that are going to be most beneficial to the consumer.

I think we certainly see a world where that income data becomes more and more important, and you can start to do some really creative things around rethinking the paradigm of credit itself. A good example of this is when you think about credit right now, it's very kind of transactional and a snapshot point in time. What I mean by that is when you actually apply for everything from like a credit card to an auto loan or what have you, at the point that you actually apply for it, the lenders are checking how much you make, where you work, etc. But when they actually give you the loan, it's kind of a black box, right? It's kind of is like, well, I really hope this person doesn't default.

What we hope lending will turn into as time goes on is much more of a longitudinal perspective where we can say, hey, you know, here is your loan now, but let us continue to provide visibility into someone's income status and income health -- to be able to say, hey, you know, God forbid, they are either terminated or furloughed, or had a reduction in the hours that they've worked, can we, as the lender, dynamically work with this person to either provide some sort of relief, some sort of loan modification, or what have you, to basically prevent that consumer from going into a deeper cycle of debt, and at the same time, for the lender to also make sure that they protect their assets and do everything they can to maximize their margin their returns, as well?

The funny thing is, it's actually a win-win, because when a consumer defaults, it's actually a huge lose-lose. The consumer, obviously, goes further into debt. And as the lender, now you have to repackage this and sell it for pennies on the dollar to somebody else, like a collector, to actually recoup whatever value they can. And so it actually benefits folks on either side of the table to make sure that, as you get real time information, to use that to make sure that both people are able to hold up their side of the bargain.

Use cases

If you take a step back and think about what we do, we provide connectivity into payroll accounts. And I should say, again, we consider payroll to be a proxy term for a much larger and broader swath of platforms that are anything related to income -- everything from a payroll provider like an ADP to a gig platform to these New Age platforms like Etsy or eBay, for example. We enable two fundamental things: the ability to actually access data, everything from who you are, to how much you make, to where you work, and also the ability to update settings (more specifically, direct deposit settings). This is obviously a really big thing for a lot of the folks in consumer fintech. Our thesis is that when you can combine the two together, the data and the access to direct deposits, it's a really powerful combined function.

So when you talk about these use cases, there are first order things that we can do, like work with our customers to enable really seamless switching of direct deposit, especially at the point of account opening or account onboarding -- to make sure that they are moving their income to their new account and be able to get the most out of that experience. Another first order problem around using that data is to just broaden the ability for our lending customers to actually verify the income of folks that otherwise they would have no ability to get real verified data from.

The more interesting thing for us is, as you start to extrapolate that and move down the line from a roadmap perspective, we can do really interesting things like enabling earned wage access, for example. If you think about what you actually need to enable that, one is access to direct deposit, which we have. The second is access to data to be able to know if someone is actively employed right now. To take it one step further, do you actually have the time and attendance data to be able to say, hey, you know, Zack clocked in and clocked out today for his eight hours at Chipotle? We can, with high confidence, be able to forward a paycheck for the day, knowing that we have a high signal that he's actually working his shifts.

Income data clients

We work with a wide variety of folks, certainly some of the largest names in fintech, like Square's Cash App. And we also work with some of the more up and coming fast growing startups, as well. There are certainly a number of early adopters in the fintech space of all sizes that we are excited to partner with and co-build alongside. I would also say that we're starting to see the growth of this market hit more of the traditional financial sector, as well. Whereas a lot of the early growth that we saw was with fintechs, we're starting to see more and more folks in the traditional finance world wake up to the benefits of what we can do with our platform. So as of late, we've actually gotten meaningful traction with some of the larger older players who see this as the kind of springboard into the next generation of fintech companies.

What gets us most excited is whenever you start something, especially in a startup, where you're basically trying to create a new market, there's always a certain kind of cold start problem -- where you see what the potential could be here, but you're not really sure you know who those people are going to be. Because there's just so many things that you can actually do. I think it's exciting to see ourselves now at the point where we're really at the beginning of this hockey stick, this inflection point, where the value prop is just so blindingly clear to so many of our customers. They're now acting as evangelists for us, saying, hey, not only does this work, this works really well. And it's going to get the attention of the folks who take the longest amount of time and the slowest to actually adopt anything new.

The value chain

We actually think quite a bit about this. We talk about data: there are limitless things that you can do to optimize and improve on it. But I like to think about it in kind of three phases. The first phase is just ingestion. And even though it sounds really simple, in theory, the idea that you can basically ingest data in a way that actually ensures the integrity of the data is actually quite hard. Especially because you're both trying to expand coverage as quickly as possible to this wide, vast world of income sources, but also be able to build the foundational data structures to make this data actually recognizable.

The second phase is what I would call, normalization, which is, now that we have high quality source data, can we actually clean it up? And in some ways, can we package it in a way that is usable by our customers? For anyone who hasn't spent a lot of time in the data aggregation world, this stuff sounds easy, but it's actually incredibly difficult to do. Because the reality is, a lot of our customers want the data organized in a way that is immediately usable from the get go. They don't want a bunch of raw information, so they have to sit there trying to figure out what to do with it. I think we've certainly made a ton of progress through the first phase. And I think now we're very much in this second phase, where we're starting to see a lot of different ways to organize the data to be the most impactful and useful for our customers.

And then the third piece, which I think will be a long pull and the future of this platform for years to come, is what I would call the translation layer. Basically, once you have all this data normalized and cleaned up, what can you now build on top of it to provide surplus value to your customers? It's everything from potentially building our own version of a score, but instead of it being based off of historical data, now you're basing it off of someone's income and employment status. The whole idea of kind of answering the question of ability to pay in a much more programmatic fashion to also building things like a 52 week income volatility index. Especially if someone is a gig worker, being able to provide an overview of their seasonal dips and changes so that people have a much clearer sense of the true risk profile, the true income profile, of any consumer. We're in phase two, and we are starting to actually work on a couple of things in phase three. We're really excited about rolling these things out going into next year, and where else we can go from there.

Security

I would start with a couple things. One is, before I go into the company building piece, which I absolutely adore and could spend hours and hours talking about, I should mention: when you're handling such sensitive consumer information, it is really, really important that you have things buttoned up, especially if you're dealing with larger enterprise customers. Security is just something that is table stakes and non negotiable. And for us, that's been something that we're really focused on, especially with all the data that we have, and especially the use cases around lending.

We made a very concerted decision to be in full compliance with the Fair Credit Reporting Act and have established ourselves as a CRA entity, a consumer reporting agency, which means that, unlike some of the other data aggregators in the space, we took a hard line stance to say, hey, you know, we want to be responsible for our data. So that if any adverse action stems from the use of our data for a consumer, we ourselves are directly liable and responsible for that. Whereas I think some of the other folks will kind of provide more of an argument of oh, we're just the pass through, a gateway, and thus are not responsible for the outcomes related to the data that is provided.

We think if you want to actually be a cornerstone and pillar for this system, you need to be responsible for the output of your product. It seems like a no brainer. We made that choice very early on, and I think that's really paid off dividends for us in the market.

The other thing I'll say is, very early on, we brought on a CISO and got our SOC 2 type of compliance and all the right certifications, because again, with something like this, you only get one chance, right? If you really care about the consumer's well being, which we do, then securing that data in a way that leads the market is really critical for us. That's one piece around how we think about the future of the business.

Finding talent

This market is so competitive from a talent standpoint. But candidly, I think our team has done an incredible job really building the cultural foundation for massive growth and success down the line. I think it's because we've been very intentional from the get go about how we think about our culture. To give you an anecdote: my two co founders, Curtis and Anish, and I interviewed over 60 to 70 engineers before we made our first three hires. It was a painful amount of time doing so but we thought it was well worth it. Because given our prior experiences, we knew that the company is nothing without the people that are there, and the early people have an excess impact on that culture.

Even though we met with a lot of exceptional engineers, technically, we really wanted to reach people who are a great cultural fit. And what I mean by that specifically is, there are key tenets that we really focus on that we think are predictors of success. I think there are four really big pieces. One is what we call grit, which is basically are you willing to run through walls and accomplish your goals? Two is resourcefulness. I think some people might call this scrappiness. But basically, are you able to see problems and figure out the solution versus sitting there seeing a problem and then complaining that the problem was there, but you aren't able to actually solve for it on your own? Three is natural curiosity, which I personally have found to be the biggest differentiator between good and great operators. It's like, how willing and deep are you willing to go on any problem? The folks who go deeper are the ones who inevitably understand how the product and the system really work, and are able to find those secrets and hidden gems that really allow you to differentiate yourselves across a long timeline.

And then, last but not least, there's this really interesting idea that the Collison brothers at Stripe really pioneered, which is this idea of craftsmanship, or I guess, craftspersonship. And it's this idea that not only do you take great pride in your work, but there's this certain care that you take, where you truly feel like the product or the output of your work is a reflection of yourself. It's everything from being detail oriented to going that little extra step. Because you know that what you're putting out, you care so deeply about it. I think, above all else, the thing that I'm most proud of is everyone on on this team cares so deeply about what we do, how we do it and how well we do it. We see that what we do can actually lead to a tangibly better financial system and tangibly better financial outcomes for those who really need it.