[00:00:00] Rich: Hi, this is Rich Altsman and I'm your host of the Lending Link and we're live today at FinTech Meetup in Las Vegas. I'm happy to have my first guest this morning, Mike Mondelli, CEO of Verdata. Mike and I've known each other for quite a while now, and maybe we could start Mike by going back in history a little bit and talking about L2C and how that evolved, and a little background on that. And the use of alternative data and how that kind of led into their data.
[00:00:28] Mike: Sure. Happy to. When we first started L2C, the challenge that we saw was subprime consumers and neophyte consumers had very difficult to interpret credit scores or didn't have a credit score at all based on a FICO score. So, what we set out to do was find data sets that were leading indicators for the traditional Bureau data. For instance, a credit report is essentially kind of a balance sheet. What we tried to find for newer credit consumers and subprime consumers were information on their income statement, because we felt like their income statement would be a good proxy for their eventual balance sheet. So, we aggregated data like payday loan information, bank account information, property ownership information, subscription information, that type of thing, and built credit scores off that from 2000 to 2014 and worked with a number of financial service institutions to prove to them that we could help them prove significantly higher percentage of their subprime or improve their pricing on us. Significantly higher percentage of their subprime new to better consumers. Sold that business to TransUnion in 2014, and it's still the basis today for their credit vision link solution. We're proud of the work we did there. And for six years, I was at trans union before I left to start Verdata.
One of the things I really learned there was that I got to lead TransUnion's global data strategy team, and I got to see how different regions around the world use credit data, both for consumers and businesses. What I recognized was there was very little investment in the last 20 years that had been made on the commercial side. Almost everything was on the consumer side, so we started to look at the S and B space in particular, and there were a few players in the marketplace, some of the legacy players, but there really hadn't been a lot of improvement in that space, either from an identity perspective or a credit perspective. And the data was very disparate and just really the processes that we experienced that our clients were going through were very manual. So, we thought we could bring data together to really help solve some of those. Those challenges.
[00:02:34] Rich: Now you mentioned the use of banking data at L2C and certainly we're all familiar now with the open banking and pulling data down for companies like Plaid. So back in the days at L2C, was it similar type of data or more non credentialed bank data?
[00:02:48] Mike: It was more non credential. I mean, the Plaid data is fantastic. The challenge is the breakage on requiring the credential. And what is the drop off that you lose? We're looking at that. Now, for data for SMBs, because SMBs are much more willing to share their credentials than say a consumer was. They're generally a little bit savvier than just your average consumer, but we're still looking at it at the right place in the credit life cycle, not necessarily the point of transaction because the point of transaction, everybody wants things to be fast. Potentials are not fast. So now companies like Kodat and Validus, which help with QuickBook data.
[00:03:32] Rich: So do you see that as a compliment as well to your type of service?
[00:03:36] Mike: Yeah, absolutely. What we really see the opportunity to do. Is there's a tremendous amount of data being built in silos, whether it's automated bill payment data, whether it's accounting data, like you just highlighted. None of that data is being put on any kind of commercial credit report today. And why not? I think it's just because nobody is focused on that. The legacy companies are really not going to go after that generally. And we see that as a really big opportunity to create a robust data set of verified businesses with verified business relationships and verified bill flows and things like that to eliminate things like Fraud risk, e compliance risks.
[00:04:17] Rich: They started a company in, I think it was what? March, 2021.
[00:04:19] Mike: I joined it in March. We founded it late 20.
[00:04:21] Rich: So right during COVID. Any interesting stats on that journey of starting this company during COVID and how that affected the initial growth or opportunity?
[00:04:35] Mike: I would say it's a two-sided coin. On the one hand, we've had the opportunity to really attract people from all over the country that you might not be able to find that specific skillset in Atlanta where we're based. On the other hand, I think I'm a big believer that in office work is valuable. Particularly when you're doing hard things and you're doing complicated things. And you've got people working on projects that really need to communicate with each other. I feel like sometimes the remote work gets a little siloed and it requires extreme amount of communication, make it work successfully. So we're still remote, we're moving to more of a hybrid model going forward.
[00:05:14] Rich: Good, good. Now the Verdata platform, is it a contributory database?
[00:05:20] Mike: It is. That's what really makes this unique. We have a three data field approach, if you will. The first track of data is really around verifying the identity of the business and the principles associated with it. Everything from secretary of state filings, which is your traditional kind of KYB. We think that's really not enough. So then we go to the next step, which is, let's make sure there's no bankruptcy slings and judgments at either the business level or the principal level. Let's match the principles to the business. And then if licensing is required for things like medical providers or contractors, some improvement contractors. Can we verify accuracy of their license and that it's still intact. We monitor that on an ongoing basis for our clients. So that's kind of that KYB, KYC track.
The second track is what you just brought up, which is a contributory piece and really ends up becoming the dependent variable in the models that we built. So, we do get data furnished to us by all of our clients, give to get database. Now we anonymize the data. So, it's a little different than a Bureau data we wouldn't necessarily see. Bank of America as the contributor or chase as a contributor. We try to protect our clients for their good account from being fished by others. But what we really are focused on is having a contributory bias so that we can identify the fraud and the bad actors and clean them up and at least eliminate their ability to go do transact business. The next partner.
And then the final piece is really, we get all of the customer sentiment. So, whether that's consumer complaints via BBB or state agencies and things like that, or whether it's just reviews. We have all the Google reviews, all the Yelp reviews, all that information and we model all that and we trend it over time against our dependent variables.
[00:07:14] Rich: Given that your company was started during COVID, did you have clients that were involved with the PPP program and using your data as part of the underwriting for that?
[00:07:23] Mike: No, we didn't really have a solution in place until end of 21 and we kind of missed the whole PPP.
[00:07:30] Rich: When you kind of think about the future, in the small business lending space. Do you see any real game changers? I mean, obviously what you're doing is critical, but the use of AI, for example, is certainly something we're all talking about. What's your thoughts on what some product development might be for either your company or the SMB lending space in general?
[00:07:50] Mike: I think the biggest challenge for SMB lending, particularly, and I consider it direct and indirect SMB lending. So direct lending is if I'm going to give a business $150,000 direct line of credit or something like that, indirect would be what we'd call, point-of-sale finance or embedded finance where we're lending to a consumer through the business.
Both of them have risk concerns for the lender. What I think are real big changes is. First of all, Rich, I would say they're both very manual process using very disparate streams of data. I think there's a tremendous amount of opportunity for improvement. When you say game changers, we're starting from pretty low bar right now.
So, I think there's a whole lot of opportunity, whether it's AI, yes, eventually, but I think first you got to get the right data sets into a single solution or be able to access it in a single call. Then I think you could apply AI on it. But right now, I still think aggregation of data sets that are out there today, and the inclusion of additional data sets, like what we've talked about, whether it's built payment automation data, whether it's furnished data from point-of-sale finance companies or SMB companies.
Or whether it's accounting data, bringing that data into the fold and then applying machine learning and AI to it. I think it will definitely move the needle dramatically. I also think the hardest part about the commercial side, it's different from consumer's entity resolution. Very different on the commercial side and I see AI playing a huge role there. How do you make a machine, make a decision like a human would if they had the time to look at every application? AI is going to play a big role here. First of all, we've got to get the data.
[00:09:29] Rich: Well, certainly solutions like GDS's Modellica is a great platform to use for small business underwriting and interconnecting with their data.
So, we're sitting here in Vegas. Did you partake in any of the tables last night?
[00:09:41] Mike: Maybe tonight. I got in on the East Coast time yesterday, so I went to bed pretty early, I wanted to be fresh this morning. I might get out tonight.
[00:09:49] Rich: What's your favorite to play?
[00:09:50] Mike: Probably craps. Yeah. But I'll play craps all over the blackjack.
[00:09:55] Rich: Well, you can use your analytics there. So, we appreciate you being our first guest this morning at FinTech Meetup. Certainly wish you the best of luck with Verdata and its continued success. And hope you have a successful night at the craps table tonight.
[00:10:09] Mike: Thanks guys. Good to see you. Thanks for having me. See you, Rich.
