FSI Member Spotlight Episode #16: How lenders can leverage continuous employment data to reduce underwriting risk and loan origination friction
In a recent FSI Member Spotlight, Praxent had the opportunity to meet with Shmulik Fishman, CEO & Founder at Argyle, a leading employment data platform.
Argyle provides a trusted way to connect employment records to applications in real-time with user consent and provides continual up-to-date information on employment and how much they are being paid.
In this episode host Tim Hamilton, CEO & Founder of Praxent, talks with Fishman about the challenges and opportunities surrounding employment data verification and the trends that are reshaping the world of lending.
Tim Hamilton: Shmulik thank you so much for joining me today. Could you tell us a bit about what Argyle does and the problem that it solves.
Shmulik Fishman: Argyle is an employment data platform. We enable businesses to access real-time employment information and income information on their users. This enables them in real-time to make better decisions, whether that be for real-time credit decisions, lending, or repayment schedules. There are many different use cases that clients have brought to their users, through our platform.
Tim Hamilton: Now this is all user permission, and I wonder if maybe that takes us through the user journey of using Argyle. What part of the technology do users touch and what part of technology is embedded into your fintech partners?
Shmulik Fishman: Yes, so we’re a fully white-labeled solution where we are deeply embedded into each of our clients’ applications or experiences and it’s as simple as thinking of a button or thinking of a single sentence, such as “how do you make your money?” or “who are you employed by?”
It’s a simple question that you can ask any person inside of an application, and when you do that, think of a Google search but for employment. So you start typing Starbucks, or Susie’s Cupcakes, or Joe’s Trucking, whoever you’re employed by, you find that employer, and you log into that system. So you have ADP credentials, you have Starbucks credentials, you have Walmart credentials, whoever you’re employed by, you ‘credentialing, just as you would normally to access your pay stubs, to access your ships, to access your PTO – the same set of credentials. When you authenticate in from the user experience, it says ‘congratulations your account is connected’.
And from a client experience now data can stream into that lending or that application. Everything from very high-level things like what’s the user’s first name and last name? What’s their birth date? To very granular information, such as, how many hours did they work yesterday? Or how many hours have they worked today? What is their base pay? Do they contribute to 401k? These are all the granular bits of employment data that make the system so powerful.
Tim Hamilton: Before Argyle, how was this data acquired? Was it a painstaking and manual process?
Shmulik Fishman: It still is. Most businesses operate in an employment data desert. The typical way that this data is accessed is through PDFs and through emails and through phone calls, through credit bureaus.ll systems that are one manual, have very high levels of fraud and risk associated with them and are stale. Meaning that the information that’s contained on them is a point-in-time snapshot. If you ask somebody for their pay stub, that is information that was true at some point in time in the past. If you ask a credit bureau ‘is this person employed?’ Most credit bureaus update their database four or five times a year. So you’re looking at information that could have been true at some point in time, previously. And that is a huge difference between how we’re providing employment information and what the marketplace has been used to.
Tim Hamilton: The value proposition, therefore, is not only just about ease of use and speed of, actually, reducing the cycle time for loan applications, for example, getting the employment data that is required in order to perform credit risk decisions and underwriting decisions. But the other piece that you’re really making here, I want to highlight, is the fact that the data accuracy is a lot higher because it’s not just a single point in time, but it’s continuous, it’s real-time and it doesn’t have to just be the beginning of that application, it also unlocks continuous updates. It’s not just a point in time.
Shmulik Fishman: That’s right. It’s a persistent connection, which means that instead of making a single point decision you’re able to have a long-term relationship with your user where you can understand, did they get promoted? Did their title change? Did their base pay change? These are all now questions that you can have answered in real-time and have a journey or have a relationship with the user.
Tim Hamilton: Wow, it really unlocks more of a dynamic relationship between lender and borrower.
Shmulik Fishman: Yes, it also dramatically reduces friction. I think a big thing that the industry talks about is time to close an application or time to make a credit decision. And if you think about that entire journey, both from the borrower’s side as well as from the business side, there’s a lot of process points involved about checking a lot of boxes, about uploading a bunch of different files. People build Manila envelopes, right? They build folders, that is, whether that’s tapping on a computer on your desktop or actually physically happening in your office. That is the process right now, and we are consolidating all of that into a single flow, that is completely digital.
Whereby, you don’t need to ask people for their W2, you don’t need to ask people for their pay stub. It doesn’t need to be a correspondence back and forth over email for information. You don’t need to call the employer. When you start to think about all the different process points that are involved in credit decisions we’ve shrunk that down into all but just one screen.
Tim Hamilton: There are so many use cases that are unlocked as a result of this. Tell us a bit about the founding story. Where did this idea come from?
Shmulik Fishman: This did not start as a credit decisioning platform or an employment data platform. We were very interested in reducing the friction to fill out job applications. At the time, we were on craigslist or you go on Monster.com or, Indeed, and you see that to get a job, to get a normal job at Starbucks, or Chipotle, or something like that, it takes quite a long time to fill it out. I’ve filled out applications now to work at Starbucks and at Walmart, it took me 30 minutes to get my application in to be a barista at Starbucks and I was trying. It takes time.
And our initial hypothesis was, can we autofill these fields? First name, last name, birthday, address, phone number, previous employer. These are all fields that are very common, not just for filling out a job application, but it turns out they’re also very common to get a credit card, to get a loan, to rent an apartment. It’s the same set of fields and so, that’s where we started, we started with trying to fill out job applications.
And the clients that came to us were predominantly ones that were in the lending or a payment vertical, financial institutions. Same data set, but a very different use case than what we originally were intending.
Tim Hamilton: It sounds like there’s really an opportunity to create a unified data format. Even though the fields are different from one employer to another. There are slight differences and nuances. But imagine the kind of efficiency gains and potential data insights that we could unlock by establishing a data format that sort of streamlines and consolidates all those distinctions.
Shmulik Fishman: I think that’s very much on point. An analogy I think that’s important is: publicly traded companies have to abide by GAAP financial standards. We don’t do that because it’s fun, we do that because it allows us to analyze every business using the same set of standards. It’s very helpful. Transparency is helpful, standards are helpful for everybody that is a participant in that market. Employment data has no standards; there is actually no federal standard for a pay stub. It’s the reason why every pay stub looks different. There isn’t actually a mandate from the Federal Government of the United States to issue a pay stub. The only reason why companies do it is it’s a benefit to the employees, there is no mandate to do so.
This is not good. We should expect better from employers and we should expect more from the marketplace. We think that our data schema is the best of its kind and it’s great to see so many different businesses use it. We spend a great deal of time making sure that, regardless of how ADP, or Walmart, or Uber, or YouTube, or Rover stores their data, that it is normalized into a set of rows that are consistent throughout all the different columns (columns being Walmart, Target, things like that) that exists. And the more people that adopt this type of employment data, or use our platform, the more powerful these standards become.
Tim Hamilton: So there are really three stakeholders in this transaction. There’s the employer, there’s the employee or borrower, and then there’s the lender. Do I have that right?
Shmulik Fishman: Yeah, that’s right.
Tim Hamilton: Let’s drill into the lender. Let’s talk a bit about what’s happening within the world of lending and how Argyle fits into that. What are the trends that are on your radar?
Shmulik Fishman: Sure. Lenders, for quite some time now, have been focused on sort of the ‘L’ part of somebody’s personal P&L. So if you think of each borrower as a P&L statement, what we focused a lot on is getting access to data that’s in a bank account, getting access to people’s spending habits; how much do you save? These are the questions that we focused a lot on over the last 10 years, and again, people have been focusing a lot on not eliminating the PDF but digitizing it.
There seems to be a moment now – a shift in the marketplace – where people are wanting to actually not use a PDF at all and focus less on how much somebody spends but more on how much somebody earns. And I think that that is the big shift that’s happening in the market that we want to start looking at some of these ability to pay – not their cash on hand.
And if you can start to look at you know money earned but not yet deposited in a bank account: “I’ve worked for 30 hours this week and that money is not yet in my bank, but I have earned it, it will be deposited into this routing and account number, I can verify it with Argyle”. That is a complete shift in the way to think about making credit decisions.
Tim Hamilton: So, as the industry shifts from looking at cash balance to earning ability, what are some of the gaps in the lending experience or the distortions or disruptions in that customer journey that we’ve lived with historically, but that now might actually start to get closed with technology, like Argyle.
Shmulik Fishman: The part that we work a lot on with clients is that once you start actually having data that is normalized that you can look at whether it’s on a screen or some formula that runs; a calculator, that is actually quite different than looking at a PDF. And there’s a real educational experience to be had in starting to examine on time performance, starting to examine title changes, starting to examine contributions to healthcare spending. And that educational moment, I think is really great for our clients and for the market to be able to rely on real granular information so you can make your own type of credit decision. I frequently tell clients that the only difference I can see between a 600 and a 550 is that a 600 is a higher number than a 550 other than that I don’t know what the difference is.
But if you can actually start to look at the actual data set of somebody, the actual earnings capabilities of somebody, their consistency of work, I think that’s where you get to make the better decisions or good credit decisions.
Tim Hamilton: So much more nuance and insight that could be harvested from this data set that could have ever been inferred from a single number like a 50 or a 550 that makes a lot of sense now. Shmulik, Tell us a bit about the technology and what it takes to implement Argyle’s tech stack.
Shmulik Fishman: Yeah, I think we’ve been touching on it a little bit, but there’s really two parts to it. The first is implementing that ‘who pays you’ screen that we were discussing before. That takes all but 30 minutes. It’s very similar to implementing a stripe SDK, to do credit card check out. That, and we also provide that experience through a no code option as well, where you can log into our console, you can send an email that’s white labeled with your logo on it, you can send a text message, you can customize it so it has that user’s name, so you can go all the way from no code at all to a highly integrated experience. All very lightweight and easy to do.
The second part of the integration is what we were just touching upon which is; what do I want to do with this data? How would I like to start making better credit decisions? I can still extract the pay stubs and the W2s, we provide you with, so if you want to extract those, we can provide you. The second part of the implementation is, let’s talk about how you make credit decisions today and let’s give you some tools about looking at shifts, on-time performance, commissions. Looking at that data set and saying, can I make a better credit decision if I start to use some of these data elements or tools, raw elements, that I haven’t previously been able to do
And that part of the implementation does take some time, that’s a conversation. Because now it’s not quite as much technical, as a business conversation with the entire company about how you make credit decisions. The data is there, now you need to start thinking about a relationship, about how you want to use that data set in a new way.
Tim Hamilton: It seems like with the expanded scope of the data set this really becomes an opportunity to distinguish or differentiate yourself as a lender from the approaches of others. You’re not just looking at basically the same set, you really have an opportunity to distinguish yourself and so as a lender it seems like you can go with a kind of an off-the-shelf low-code or no-code solution, plug it into an auto lending experience or buy-now pay-later solution. But if you wanted to, you mentioned at the top of our discussion that it is totally white labeled, you can put Argyle in the background and really put your brand at the forefront and create a completely whitelabeled experience.
Shmulik Fishman: There’s many aggregators out there in different verticals. One of the big ethoses we have at Argyle is that we don’t want to get in between the relationship that you have with your borrower; it is your client, not ours, and so we don’t want to invoke another brand or another relationship when it’s not necessary, because I think that creates confusion for users and it doesn’t make for a good conversation between us and our clients. And that’s the reason why everywhere throughout the experience it’s the company’s logo or it’s no logo at all, but it’s surely not ours.
Another way to formulate that is, we like to say, if no one knows we exist we’ve done our job correctly. We are a data transmission service, that’s where we start and end.
Tim Hamilton: Staying with the implementation discussion for just one more minute. One thing that we have noticed about Argyle is that you have put a ton of effort into creating beautiful technical documentation, really, really well organized. Tell us a bit about that and the decision to invest so much effort into your technical documentation.
Shmulik Fishman: By trade, I am a Product Manager. I am deeply fascinated, external of Argyle, I’m deeply fascinated about how user experiences affect behavior. And small details about colors and fonts and button sizes do have effects on you, on me, and that’s part of the reason why we’ve put so much effort into that.
As to the documentation itself, what Argyll sells at the end of the day is data. The only thing that we are transmitting. We’re not making artwork, we’re not making a phone; we’re making an excel spreadsheet. The only thing that we can give to our clients is data, and I think because of that it behooves us to make sure that it’s very organized, very easy to read, very fleshed out in terms of what you can do with it, and the way that we’ve done many iterations of our documentation. But, if somebody asks a question more than once, it means that there should be a documentation article about it. And that guiding philosophy has produced what would be internally called the ‘Museum of Selected Documents’.
But we very much think that the documentation is a form of art and that it should hold up to that.
Tim Hamilton: It’s a really design thinking informed, or user-centered design approach or philosophy, to documentation. How refreshing, thank you for sharing that. I’m really curious to understand – we’ve talked a lot about lending and use cases within lending – are there other use cases or industries that are on the radar for you? How else could this data unlock use cases across other industries?
Shmulik Fishman: So we’re always surprised with the new ideas that clients come to us with. I think each week we hear a new use case for the same data set. You can think about tax contributions, auto management of a 401k plan, changing payouts from one bank to another, all using the same data set. I think we’ve just really scratched the surface of what you can do with the infrastructure of employment data. So there are many industries that come to mind. Lending is definitely one of them, financial services is definitely one of them.
But there’s a bunch of things that have nothing to do with finance at all, but still can very much leverage employment data. I’m actually quite fascinated by a subset of our clients that are doing work optimizations where they asked their users to connect all the different places that they work – with the average American networks more than one job – there are some users that connect 6, 7, 8, different places that they make money from and there’s a really interesting new market for optimizing work where on Monday, you should drive for Uber, on Tuesday you go to work at Starbucks – that’s different if you’re in California, or if you’re in Texas. Again, this has nothing to do with financial services, it has to do with how to optimize your earnings and you’re using the employment data set to do so.
I think there’s something really fascinating about that and I bet you there are 10 more of those sorts of ideas out there.
Tim Hamilton: That is fascinating. I cannot wait to follow those and see how that evolves in time. In closing, Shmulik, in your mind, what is the number one thing for people to know about Argyle?
Shmulik Fishman: That you don’t need to rely on a credit bureau or pay stub any longer, that does not need to be the best of it. You can do something more than that and it’s great to be leading that charge.
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