[00:00:00] Rich: Good afternoon. This is Rich Altman with the GDS LendingLink, and we're broadcasting live from FinTech Meetup in Las Vegas. I'm pleased to be joined this afternoon with Jeff Smith of Equifax, who is in the Consumer Finance Group, and he's the Sales Director for the Workforce Solutions Division, specialized in providing lenders with actionable income employment data to help consumers achieve financial wellbeing. How's it going, Jeff? 

Jeff: Good, Rich. Thank you. 

Rich: Are you enjoying the conference so far? 

Jeff: It's been great. 

Rich: How many speed dating things have you done? 

Jeff:I've lost track. I don't even know. It's exhausting. I'll tell you, it's really exhausting. 

Rich: So, why don't we start off a little bit by giving you an opportunity to talk about the work number and the type of solutions that you bring to the market.

[00:00:40] Jeff: Sure. No, I appreciate that. So like you said, again, Jeff Smith, my work is part of the workforce solutions group within Equifax, the workforce solutions group focuses on a database called the work number. It's a business that Equifax purchased and going back to 2007 and it has grown quite a bit. So that business, so that database is essentially a database of 168 million active income and employment records. So we essentially partner with companies of all sizes. Payroll providers, we're doing I 9 verification work for them, and then we are also taking off of their plate the need to verify their employees income and employment data. So then we house that, and then provide that to the verifier. 

[00:01:28] Rich: Now, if memory serves me correctly, and I might be dating myself a little bit. when the work number, which was the talks originally started, my recollection is that it really started as a service to HR departments really not be bombarded by income verification. So they would sign up with talks where employees would be issued a pin. So if I was applying for a car loan, I could actually. Give my pin and then pull down my data. Am I remembering that correctly? 

[00:01:57] Jeff: You are. So this is the interesting thing about it is it's actually a database that was initiated by our customers saying you are already doing this work for us. You already have access to our records. Can you also take this responsibility off of our plate? And so that's, that's interesting. And then the name work number itself, kind of like what you're alluding to was an actual phone number you call into the work and the work number, you'd call the work number and verify someone's income and employment. So obviously that has come a long way. No longer a phone number that you're calling into, but all digitally accessed. 

[00:02:31] Rich: Okay, good. So did it start more in the mortgage space? 

[00:02:35] Jeff: So definitely, the mortgage business is a large piece of the revenue that the work number brings in. My job in particular has been to expand that into non mortgage lending use cases. So think of consumer finance, personal loans, auto lending, credit cards, both that origination and credit line increases. And then even on the back end, with some things like recovery and collections. 

[00:03:02] Rich: So going back to the initial business model of talks, When do you have a team that goes out and signs up employers? and is it still the same business for both of them, that the value of them signing up as an employer is to help streamline and not be bombarded by employment verification requests?

[00:03:22] Jeff: Yeah, 100%. So there's definitely some additional things that they provide. not an area that I'm an expert by any means, but yeah, it's helping them, kind of outsource that responsibility while also doing some I 9 verification work, some unemployment benefits, some tax credit work. So there's a lot of other things that they're getting access to. We have a team that's their sole job is to go out and bring, employers onto the work number. 

[00:03:48] Rich: When lenders want to take advantage of your data, how do they actually get access to the information? 

[00:03:54] Jeff: Yeah, great question. So, really there's three or four kinds of prime or primary ways that you can access the work number. we love GDS Link, just to kind of throw that out there. Part of the reason why we love GDS Link and our third party connectors is because we have built a relationship with you guys that we can plug the work number directly into. And so, your customers have direct access to the work number through your system. You're not having to, like, technically integrate and program and write code. you can also directly access the data through our REST API. You can also go directly through the website and, and, and log in that way. So the, the fourth kind of option that I kind of, hesitant saying, but there is kind of a batch function as well. So if you have a large list of individuals that you want to run through the system, you can do it that way. 

[00:04:45] Rich: There's no doubt that the income and employment space has gotten very crowded. you brought up connectivity through GDS, me and my coworker, Camille, we even talked the other day about, God, we really need to put together a grid. That shows all the different players in the market sharing. And of course you have those that are friction introducing and those that are not. I think the first question is, how does the work number differentiate itself from maybe other non-friction solutions? And then the second question would be, kind of talking about the relationship of friction solutions, because you don't have a hundred percent coverage.

[00:05:22] Jeff: I mean, you hit the nail on the head. Like the work number does not have a hundred percent coverage. We have the highest coverage of any of our competitors in this space. And so our goal is to get to a hundred percent, but until we do, there's got to be a way that everybody kind of plays together, right? clients are coming to me saying, I need something that is verified, that is automated, that is as frictionless as possible. and so that's our goal with the work number work. We're trying to help people say yes to more customers, more borrowers to do it in the appropriate way, as far as risk and whatnot goes. And so. Yeah, we, we are, we are connecting with as many third parties like GDS Link to do that. but as it relates to those that maybe add more,, friction, bang credentials and that kind of thing, frankly, it's, it's a way to like complete that puzzle to get to a hundred percent. So typically our customers are doing it in a variety of different ways, but they will use a variety of sources and. Now, we love when the work number is one of 'em. 

[00:06:25] Rich: In fact, I think we're speaking to Argyle a little later. 

Jeff: Sure. I've heard of those guys. 

Rich:You've heard of those guys. so I'm sure you've had an opportunity to do some data studies, and You probably have come across some interesting situations. picking an industry, let's talk about auto lending. Have you seen any interesting use cases that kind of validate the probability that the income stated on an application really may not be the exact income? 

[00:06:53] Jeff: Right. Yeah, that's a good question. So probably tonight you're surprised the work number is not free. And so everyone that evaluates the work number has to ask the question, is there ROI? If I invest in a solution like the work number, Well, I see the benefit of that. And so we have a lot of data coming in. Like I said before, we have all of our trade link tradeline data coming in from the credit side of the business. And so what we see from our data and analytics team is that there is typically over a 40 percent lift from successfully closed loans. when you're comparing auto lenders that are not using the work number and those that are. So that's pretty substantial. A lot of that benefit is tied to helping customers more accurately. Identify income and employment that's provided from their borrowers. Mm-Hmm, . So one, one other interesting stat is the fact that over 50%, from our view, from what we see stated income coming in, comparing it to the work number over 50% is, is either understated or overstated by 20%. So 50% is substantially, different stated income to the actual income that we're getting in. So that's a big deal. Obviously, a lot of that is overstated income, but there is some that is, that is understated as well. 

[00:08:07] Rich: Why do you think somebody would understate their income? 

[00:08:08] Jeff: I mean, I think there's a variety of different reasons. I think if you have a highly commissioned paid job, that could be one reason why you're always kind of, underestimating. But yeah, there could be a variety.

[00:08:21] Rich: You don’t worry that you're reporting the RF. Yeah, exactly. This guy said he makes, no, he actually makes that. Yep. Well that's interesting. So in the waterfall, where do you typically see yourself? So we think about pulling data barriers and solutions like Bidelica from GDS give our lenders the ability to create that waterfall of when to pull data. So maybe they're going to go to Equifax first, maybe they'll get a thin file. So they want to go out to LexisNexis. So on the income side, is it typically more verifying? That application somewhere down the waterfall. So maybe it's an additional improval, but now I want to do some income verification. 

[00:08:56] Jeff: Yeah. I mean, there's such a variety to how it's being used and it's not always disclosed to us as far as, kind of where we're using the waterfall. We have some auto lenders that stip every deal and therefore, it's used quite,quite often. And then some that don't. but yeah, it's, it's typically, at the point of, decisioning at funding, there is some use at the beginning, where there's some ID verification, just the fact that we've had so much income and employment reported over the years, that means we have a lot of data. And so there's a lot of opportunity to verify identity up front. Does this person, does their name match with the SSN that they've provided? So there are some kind of early funnel use cases. But usually it's at that, kind of decisioning and kind of funding use case. 

[00:09:45] Rich: Any analysis on a correlation between vantage score and stated income? Have you guys ever looked at that? I would think people with higher scores maybe are more accurate.

[00:10:01] Jeff: No, that's a good question. I don't know if I've seen any statistics there. What I have seen is there is a pretty good breakdown of just understanding vantage scores in the overall work number database, meaning like what percent are in that subprime category, who is prime, super prime, who's, who's a thin file. So there's a pretty wide spread of all those categories, but I will say there is a lot of value in that. No file, thin file range, which I think roughly makes, 10, 12 percent of the work number database. Now, is the work number considered FCRA compliant? It is FCRA compliant.

[00:10:38] Rich: So, if the lender is using your data in their decisioning, and really kind of, it would be more around the ability to repay. And they deny that a consumer is a consumer able to get a copy of their work number report. 

[00:10:53] Jeff: Yeah, I believe so. So yeah, there's all the normal protocol with that, happens and it is FCRA permissible purpose, all of that. 

[00:11:01] Rich: So, clearly your product has a lot of benefit, in the origination. Have you seen any use cases where maybe in collections, people are revalidating income? Maybe they're looking at doing a settlement and they want to say, Hey, I don't want to. Leave, leave too much on the table. 

[00:11:18] Jeff: Yeah, definitely. So, kind of the origination at applications, kind of the primary use case we see, but it's definitely kind of started to expand into account management. And like you said, collections and the recovery side of the house. Unfortunately, like I think we're seeing quite a bit of an uptick in delinquency within auto lending. so we have a lot of auto lenders reaching out to us asking for this data. And essentially that is used for skip tracing. Identify just basically does that individual have employment. 

[00:11:47] Rich: So you guys are housing this data. So can it be used in any type of marketing like direct mail campaigns as far as netting down applications? 

[00:11:58] Jeff: So going back to the FCRA component, permissible purpose, it is not a marketing tool. Officially not a marketing, not a fraud tool. Those would be kind of the two big ones. 

[00:12:10] Rich: We flooded a relationship with Paralytics. I was working with them. They're like a zip plus four predictors. So they use tax data and you have to have the nine digits zip coming down to the household in the likelihood of whether what was stated was accurate or not. So once again, all these services are very complimentary. Right. There's no one solution out there. That's for sure. Full coverage. When you guys first started to, I think. You were very much going after a lot of the big box employers like Home Depot and McDonald's because you got so much volume from there. I mean, I assume there's just a number of types of small enterprises that will always be a gap. 

[00:12:50] Jeff: Yeah, if you go back, 2016, 17, 18, like I think we had maybe less than 100, 000 companies contributing to the database. We have just hit roughly a 3 million contributor mark. Right. And so a lot of that growth has been through our focus on these medium and small size companies. Which is through payroll providers, so as we've kind of grown those relationships, a lot of them are exclusive to the work number. We really saw that growth in the record count, which is how we've gotten to that 168 million, roughly active record number.

[00:13:32] Rich: 27 million companies in the U S right now. A lot of those things can employ less than 50 people. Yeah, right. They're never going to be part of, and they may not even be part of a payroll system.Right. This is my, obviously right in the check at the end of the month.

[00:13:46] Jeff: Our biggest focus in that area, I think it's a good point. Just like the gig worker and what are we doing there? We're really trying to capture a lot of that market. it's amazing how many individuals are working multiple current active jobs. And so when you're pulling a work number report, you may get to act, active report because. They have two jobs that are contributing to the work number. 

[00:14:08] Rich: Are you being used by any companies in the earned wage access space? 

[00:14:12] Jeff: That I am not familiar with. Yeah, it's a good question, though.

[00:14:16] Rich: So, a little background, I saw that you worked at, was it Thomson 

[00:14:20] Jeff: Reuters? Thomson Reuters. Yeah, 17 years. Wow. That's a long time. And they were, acquired by Blackstone at one point, sold to the London Stock Exchange. So my background is more so in, like, Institutional finance, capital markets type of data. 

[00:14:35] Rich: Good. So what were some of the, for listeners, like, what are some of the tons of router type products that might be used in this space here?

[00:14:42] Jeff: So, yeah, in this space, there are some, there are some like KYC, AML products, world check. It is one of those, to identify an individual on the OFAC list. Are they connected with anyone on OFAC? but a lot of that is more so in the institutional investing side. So yeah, a lot of these clients, a lot of the banks that I'm talking to now, I was talking to their capital markets groups, two years ago.

[00:15:10] Rich: we're sitting here in Vegas. Do you like to participate in any of the casino games? 

[00:15:15] Jeff: I'll be real honest with you. I don't know a whole lot about gambling. This is my second time in Vegas. Last time I was here, I think I won 150. And so I just called it, called it quits there. I'm 150 ahead. To the rest of your life. I'm sure I'll get back out there and lose that 150 and even more. 150 doesn't go very far, does it? No, it doesn't. Not at some of these tables. 

[00:15:37] Rich: For sure. Look, I really appreciate you joining us today. Once again, this is Rich Alterman and we've been talking to Jeff Smith, who's the sales director for Workforce Solutions with Equifax. Thanks again for joining us. 

 Jeff: Thank you, Rich.