New Information Has Come To Light

If financial inclusion is the birthday cake, alternative data is the flour. And the eggs. And sometimes, the icing. In this episode of The Finance Frontier, host Eric Hathaway is joined in the studio by Zach Tondre, Director of Strategy for the Digital Economy at LexisNexis. Eric and Zach discuss the importance of alternative data to complement traditional credit reports and to inform lenders about potential new customers.  With a wide range of alternative data available, banks have a whole new way to understand their customers, and provide them with the products and services that they desperately need. And that is definitely something to celebrate.


Zach Tondre is the Director of Strategy for the Digital Economy at LexisNexis, where he leads thought around financial inclusion through underwriting with alternative data. Prior to joining LexisNexis, Zach spent 8 years in credit union management and 6 years in mortgage banking. Zach holds a BS degree in Business Finance.

Eric: 00:01 Welcome to The Finance Frontier, I’m your host, Eric Hathaway. This is the final episode of five on financial inclusion.

Eric: 00:19 With a wide range of alternative data available, banks have a whole new way to understand their customers, and provide them with the products and services that they desperately need, and that is definitely something to celebrate.

Eric: 00:29 I had the pleasure of being joined in person by Zach Tondre, who’s the Director of Strategy for the Digital Economy at LexisNexis. Before this, Zach spent eight years in credit union management, and six years in mortgage banking, so he’s been on both sides of the fence as the data provider, and the banker.

Eric: 00:54 I don’t know if many of you know this, but The Finance Frontier podcast is based out of Bozeman, Montana, a special place with mountain views in every direction, and a place where people get genuinely excited when it snows. Zach also calls Bozeman home.

Eric: 01:08 So, Zach, how long you been in Montana?

Zach: 01:11 Oh, well, let’s see, I’ve been in Montana since ’98. Grew up in Colorado, came out to Montana to attend MSU, and never left.

Eric: 01:21 So, today we’re going to talk a little bit about financial inclusion and alternative data in the realm of addressing a huge number of individuals that lack access to basic banking services. Globally, 1.7 billion people, approximately, are lacking access to banking services.¹

Eric: 01:42 And in the US, if we start looking at credit scores, I think there’s a statistic that 55% of credit scores are below 700, which makes it difficult for some of these individuals to get access to some of the normal credit products that are out there.²

Eric: 02:00 So, wanted to discuss that a little bit today in regard to the entirety of financial inclusion, and would love your point of view of why you are in this part of the industry, and how you as an individual, and as a business, look at why financial inclusion is important.

Zach: 02:18 Yeah, you know, we see that there’s just a huge gap in the number of individuals that are reporting positive trade lines to the three major credit bureaus versus the entire population. We’re seeing, we estimate, around 40 million individuals are thin or no file when it comes to the credit bureaus, and so they just simply don’t have a presence at the credit bureau, or their presence is so small that they’re unscorable.

Zach: 02:46 That is further compounded with a large portion of people who … you know, that you mentioned, that might fall below a certain threshold. They may have a presence at the bureau, but it’s entirely negative in nature. And so, they don’t have positive trade lines at the bureau. The only record they may have may be a collection record that came over from, you know, either a cell phone provider or a public utility.

Zach: 03:11 And, of course, the problem with that is they didn’t get all of that positive history for those positive past payments that they made for, you know, the last four or five years. They missed one payment, and that gets reported negatively as a collection under the bureau, and now they’re unscorable in a really negative manner.

Zach: 03:26 And so, you know, a lender really doesn’t have a lot of data to go off of when they’re looking at approving or evaluating this consumer for a loan. They just really see them as negative in nature, and that ends up being a pretty quick decline.

Eric: 03:41 So, to address that population, you mention negative impacts. How, and what kinds of data, do you look at, or provide to financial firms? Or what can they go out and look for to find data that can help those individuals?

Zach: 03:59 Yeah, so there’s a lot of different types of data out there that can kind of add some additional dimension to a consumer. We have a wealth of public record data that goes back from the time this, you know, adult … from the time this consumer first became an adult.

Zach: 04:18 And so, you know, any address information that they’ve got out there, professional licenses, college attendance or enrollment that’s out there. We also have records around the value of those properties. And so, we’re able to add a lot of different positive attributes to a consumer by just looking at how substantial their footprint is kind of out there in the real world.

Zach: 04:41 So, you know, for example, if we’ve got someone who has had fairly stable address history, and we see that when they have made moves, those moves have been in a positive trajectory. And even this … this applies even to renters.

Zach: 04:55 So, we’re looking at the value of that property, you know, and then the person that’s actually living there, regardless of whether they’re the owner or not. And we know that if they’re moving from a property that’s less valuable to a property that’s more valuable, we see that kind of positive economic trajectory. And so, that adds some positive light to this consumer’s file.

Zach: 05:15 Same thing if we see professional licenses. We know that they’ve got a higher propensity to earn income. And this isn’t just … You know, when we say professional licenses, we’re not just talking about doctors and lawyers, we’re talking about estheticians, we’re talking about hair stylists, anyone that has to have some kind of licensing in the state that they’re doing business. And so, that gives us some more positive attributes.

Zach: 05:37 And so, all of these different factors can really help the lender make an informed decision about this consumer, and can help them rank order risk, and know kind of what the odds are with this loan. So, at least this consumer that may not have gotten a loan at all before now is at least in the running for some type of financing.

Eric: 05:55 So, we talk about, or you mentioned, a lot of the data that we provide for that. So, let’s talk a little bit about the institution, and how they’re addressing this customer.

Eric: 06:13 We talked the other day, and you mentioned a case study that’s been done around a couple of institutions that have implemented a … some competition to the cash stores that are out there. And I think it’s well known in the industry about how high the APRs are for some of these individuals that are having difficult times to getting access to typical financial products.

Eric: 06:40 So they’re using a lot of these services, paying very high APRs. How can this data help address that situations?

Zach: 06:48 When we look at addressing alternatives to payday and short term lenders that are out there, there’s a few things that are really crucial about making that work.

Zach: 06:57 The research has been done across the industry that price is not the end all be all driver of whether a consumer makes a decision to go to a payday loan shop, or some other kind of alternative lender. Really, what’s important to them at that point is kind of how quickly, and how easily, they can get that money.

Zach: 07:14 A lot of times these short term loans, you know, they’re unfortunately kind of born out of a desperate situation. The consumer needs the money now. They need it to pay bills. They need it, typically, in cash.

Zach: 07:25 And so, there’s … you know, really, there’s more to the equation than just providing a low price. We’ve got to provide a low price, we’ve got to provide ease of access, and we’ve got to provide really quick funding on these loans.

Zach: 07:37 And so, you know, a great example of a credit union that was able to do this is Kinecta Credit Union out in LA. And what they found when they first started this pilot program to do these alternative loans, they set these up, and they were offering them in their branches, but what they found is that these customers that would typically utilize these payday loans were really uncomfortable with coming into the branches of the traditional financial institution.

Zach: 08:01 And I think a lot of that has to do with maybe past experience of being declined for a loan, because they know that their credit profiles are not great. You know, some of that just was … had to do with just kind of, you know, what they were used to, and how they were used to getting this need met.

Zach: 08:15 So, what Kinecta Credit Union did was went out and actually opened up a chain of stores called Nix Neighborhood Lending that looks like payday lending shops. However, they were driven by the credit union, the lending policies were using alternative data to pre-qualify these folks, and get them loans quickly.

Zach: 08:34 And really, at the end of the day, they were able to provide payday type loans, and even consolidations of those loans, so that they could go back and pay off some of the loans that were out there. And they could do this with these consumers at, you know, a tenth of the rate that they would typically have out there.

Eric: 08:51 So, you bring up a great point as far as the rate, and I know you mention that it’s not particularly about price that is the main driver.

Zach: 08:59 Yeah, but you know, one thing that we found, Pew Charitable Trust has done a lot of work around alternative data, but one thing that Pew has noticed is that there’s a really not a huge percentage of folks that are using these payday loans that don’t have checking accounts. Because one of the qualifications for most of these products is that you have a checking account, there’s somewhere to grab that payment when it comes due.

Zach: 09:25 So, most payday lenders, they’re requiring that payment via ACH, and so it’s happening in a couple weeks. And so, they typically have to have a bank account out there to be able to get these kind of products.

Zach: 09:36 If you don’t have a bank account, you’re really stuck with some kind of a collateralized loan, either an auto title loan, pawn loan, something like that is going to really be your only option.

Eric: 09:57 So, let’s talk about the profitability of these loans. I think you’d mentioned, in a prior conversation we had, that typical payday, 400% sort of APR. As these institutions, like the credit union, US Bank, are coming into the market, they’re reducing that down to 70%.³

Eric: 10:15 Are these profitable loans when we look at the risk-return ratio? I mean, the risk of nonpayment increases, obviously. With the credit union example, they had to go start up new brick and mortar to be able to access some of these customers. Is this a profitable market for these institutions? And what is it that people are going to have to start to look at, or institutions have to look at, before they move into this segment?

Zach: 10:36 Right, yeah. So, there’s really two things that are controlling the profitability of these loans. Obviously the ability to predict risk in these loans is key. We have to add new data to be able to predict the outcome of these loans.

Zach: 10:50 And we find that, you know, alternative data really allows that lender to rank order their risk. And so, they can then decide how they want to price these loans, and they can predict what the charge off is going to be on the back end.

Zach: 11:03 The other piece is really controlling the cost to originate these loans. And so, the only way to really do this profitably, at a lower interest rate, is to have a highly automated process. You know, there really isn’t a lot of room for manual review. This process needs to be done in an automated fashion. And then, also, the loans need to be funded nearly instantaneously to meet these borrowers’ needs, like we had mentioned before.

Zach: 11:26 And so, you know, with those things, we’re seeing that fintechs are doing a great job of getting lower priced loans out there. We’re seeing, you know, typically around 75% APR for shorter term loans. They’re originated in a very automated fashion.

Zach: 11:42 Some of the fintechs are still struggling to get the loans funded right away. So, sometimes there may be a one day or two day lag, which is … really can be kind of a deal breaker for the consumer.

Zach: 11:53 Or, you know, we’re seeing fintechs wanting to fund them into other digital wallets. And so, that can also be a problem for the consumer as well. If they’ve got this funded into their Apple Wallet, or Google Wallet, or Samsung Pay, whatever it might be, they need to be able to turn that into cash in many times, because the bills that they need to be paid … that they need to pay, need to be paid in cash. So, you know, that creates a whole nother problem of how do we get this cash in this consumer’s bank account basically today.

Eric: 12:21 So, that leads me into a very interesting question. In another episode we are speaking with CFI, and this group’s done a report recently about the security of data. And you talk about fintechs, a lot of fintechs, globally and in the US, are now addressing the under banked population. One of the concerns is the security of that data. So, how are we making sure that we’re protecting these consumers and their data?

Zach: 12:52 For us, the most important piece of providing data for credit decisioning has to do with, obviously, being compliant with the law. And so, when we’re talking about being able to provide this kind of data in an FCRA setting, you know, the Fair Credit Reporting Act requires that we have a consumer center where these consumers can call in, they can dispute this data.

Zach: 13:12 And then they have to … they not only have to be able to dispute it, we also need to be able to fully disclose all the data that we have on a consumer when they request it, and we need to be able to correct it if they ask for it.

Zach: 13:22 So, those are really important pieces that, you can imagine, like, if a fintech is using a data source that they’re creating themselves, they may not have the ability to disclose it, and they may not have the ability to actually correct the data if the consumer asks them to.

Zach: 13:37 And so, that’s kind of, like … I think that’s where lenders can start to walk on thin ice a little bit. They need to be careful with what kind of data they’re using in credit decisioning in the United States, because that’s all governed, of course, by the FCRA.

Eric: 13:52 So, you mentioned earlier the consumer center, and how consumers have the ability to call in and confirm or deny the data that exists. How do the consumers know that this exists?

Zach: 14:08 LexisNexis is a credit reporting agency. And so, any credit reporting agency, under law, has to disclose how a consumer can get their full disclosure.

Zach: 14:17 And so, when a consumer is denied for a loan, they, as part of that denial, they’re going to get an adverse action from their lender, and listed on that adverse action is going to be the providers of the data. They can dispute those, any of the data elements that they think are erroneous, and then we would work with our data providers.

Eric: 14:48 Let’s talk about one more interesting topic, when we look at the under banked, and how do we deal with bias. In the market today we hear a lot of bias in machine learning and AI, and as far as data is concerned when we’re looking at that, is that being addressed?

Zach: 15:06 Yeah, absolutely. I mean, that’s … Any time that we’re building models internally, or helping our customers build their models, you know, any kind of disparate impact that model may have is … you know, it’s a high priority to kind of assess that, and mitigate any kind of disparate impact that that model may have.

Zach: 15:24 What we’re finding, though, is that, really, adding alternative data into the mix is actually just part of the financial inclusion story. It’s really just adding lift to almost every typically advantaged or traditionally disadvantaged group.

Zach: 15:43 And so, what we’re seeing is that when we look at just bureau data, and so, a lender is only using bureau data, a lot of times these traditionally disadvantaged groups are just completely left out of the mix, because they may not have a bureau profile, or, like we talked about earlier, it may be entirely negative because it’s only got collections on it.

Zach: 16:00 So, we add the alternative data back into the mix, and now we’ve got some positive data elements that are getting attached to these consumers. And what we’re seeing is that if we take a population that we score with just a bureau header file, and then we look at that same population, and we score them with the alternative file, we see that we’re able to add, without increasing the risk to the lender’s portfolio, we’re able to add additional approved applications to every single group.

Eric: 16:38 As we’ve seen US Bank, some of the big boys, come into this market, in the next three to five years, do you see this as a competitive space? Do you see more of the big players coming in and addressing the under banked, and the … this segment of the population?

Zach: 16:54 Yeah, absolutely. I mean, I think it’s definitely a natural fit, and with US Bank coming out there and having a loan that’s really competing in this payday lending space, I think the rest of the big banks are going to be soon to follow. You know, there’s some profitability out there, and there’s also just a great story to be told.

Zach: 17:13 One of the things that we’ve seen that’s been really interesting is around the subprime auto lending market. When we first started seeing alternative data really penetrating into the subprime auto lending market, the rates in that market were also extraordinarily high.

Zach: 17:28 As that alternative data started getting introduced, some of those lenders were using that additional predictive ability to just kind of fatten their margins. But, of course, as margins fatten competitive pressure increases, now all the other lenders are looking for this type of data. That starts to, you know, basically drive rates down.

Zach: 17:46 And now we’re seeing that the subprime auto lending market is completely saturated with alternative data use. They’re probably one of the biggest adopters of alternative data. Fintechs, you know, are right there as well, but fintechs aren’t doing as much lending right now as the subprime auto market is. And so, you know, as we see this penetration come across, it’s really benefited consumers.

Eric: 18:08 So, you mentioned risks, where do you see the biggest risks in this market being addressed over the next three to five years? Do you see an opening for more fraudulent activity? Do you see risk to the consumer, risk to the institution?

Zach: 18:20 When we’re talking about short term lending, and meeting the needs of the short terms loans, those are … they’re very risky loans. I mean, they’re born out of desperation. And so, the consumer needs these loans because they’re in some kind of desperate situation, whether it might be job loss or a medical event, whatever might have happened, they need cash now that they normally they haven’t needed in the past.

Zach: 18:44 And so, these loans are definitely riskier, and they need to be underwritten very carefully. We need diverse data sets in order to be able to do that.

Zach: 18:52 You know, the identity problems, when we start talking about loans that are originated completely online and are highly automated, then we do run into some issues with identity, because we’ve got the potential for synthetic identities, we’ve got the potential for manipulated identities. And so, really, lenders are going to have to figure out other ways to continue to verify the riskiness of this loan.

Zach: 19:20 And then, once we’re certain that this individual is who they say they are, then, at that point, it’s fairly easy for us to predict what kind of credit risk this person is, and how likely they are to pay back the loan based on other attributes, and based on the past performance of that lender’s portfolio.

Eric: 19:42 So, Zach, thank you very much for joining us today, really appreciate your time.

Zach: 19:46 Absolutely. Thanks a lot. Thanks for having me, really appreciate it.

Eric: 19:54 Thanks for listening to The Finance Frontier. I’m your host, Eric Hathaway, and until next time, subscribe on your favorite podcast app.

1. The World Bank. (2018, April 19). Financial Inclusion on the Rise, But Gaps Remain, Global Findex Database Shows. Retrieved September 17, 2018, from

2. Frankel, M., & CFP. (2017, September 28). Here’s What Americans’ FICO Scores Look Like — How Do You Compare? Retrieved September 14, 2018, from

3. Wisniewski, M. (2018, September 12). US Bank Launches Payday Alternative. Bankrate. Retrieved January 5, 2019, from


Enterprise Case Study Solving Underbanked Credit Scoring


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