FinTech Meetup - Liz Pagel

[00:00:00] Rich: Hi, this is Rich Alterman, and we're broadcasting live from the FinTech Meetup in Las Vegas. This is our third episode today of The Lending Link, and I'm happy today to be joined by Liz Pagel, Senior Vice President and the Leader of TransUnion's Consumer Lending Lineup Business. Within the financial services vertical. Liz was one of our guests so it's really wonderful to have her rejoining us. Hi, Liz. How are things going today?

[00:00:23] Liz: I’m doing great. There's a lot of energy here.

[00:00:25] Rich: It's definitely a good place to be and a lot of good people to hang out with. So, thanks for joining me. Liz, you and I had a lot of good discussions about the buy now pay later industry and how things were evolving. One of the things we talked about back then was where the bureaus were headed from a credit reporting standpoint. And, just curious, what type of progress has really been made in that area?

[00:00:47] Liz: Yeah. Well, we're plugging away at it. So, I think last time we talked, we talked about the need to handle the data differently for buy now pay later. Where these smaller, more frequent loans don't fit easily into the current credit reporting ecosystem. So, we're still running down the same path of putting them on the court credit file. We truly believe they belong there with traditional credit. There's so many consumers using this data and so many consumers continuing to use this type of credit. Credit product that clearly belongs on the core credit file. So, we're going to ingest it onto the core credit file. We're going to partition it away so that it won't automatically feed into anything, into scores, into attributes. If you think about something like average age of trade, clearly if you put a bunch of buy now pay later data. into that attribute, it's going to mess up the whole thing. So, it's going to be kind of sequestered on the core credit file. We'll let it build up. Eventually we'll get enough data to let it be used in data studies, and then it'll start being incorporated into credit scores, attributes, and lending decisions. So that path is still moving forward. We've built the rocket ship. my team likes to say we're ready for the fuel and we're working with the Buy Now Pay Later lenders, all the big ones. The good news is, I think they, more and more, see the value of reporting to the Bureau. Right, right. the importance of being able to help consumers build credit. I had a journalist last week asking me directly, hey, does this help consumers build credit? Many consumers think it does. Yeah. Can you debunk that? I'm like, what, not yet, but it will. So, we're getting there. I think consumers are going to really value that. ability to build credit and the lenders need it as well. They need both the carrot and the stick of credit building to help their performance.

[00:02:27] Rich: Right? So, when we think about the buy now pay later data and things like your credit vision trending type portfolio, how do you see the buy now pay later? Maybe fitting into the trending data because it's short. Period of time,

[00:02:41] Liz: I envision there's going to be by not paying later for specific attributes. So I don't know that it necessarily goes into very many attributes with other say installment loans. I think that we've already built a set of a hundred buy now pay later specific attributes, including trends to look at the trends in the buy now pay later specific ecosystem. I think there's going to be a lot of value. What is the behavior of consumers using these products? How often do they use them? How big, small, and then the positive payment trend. So, if you get every two weeks positive payment, that's a really fast feedback loop to start building up some positive credit history.

[00:03:19] Rich: Did I recently see an article about the CFPB, and some refocus on it? Find out how to pay later, sir. Any insight you can give from a regulatory standpoint on concerns that may have been raised?

[00:03:30] Liz: Yeah, the CFPB, the OCC are continuing to look at this, kind of on and off. I talk to them regularly and I know there's a lot of interest in the financial inclusion possibilities for this. And that's what gets me the most excited about this. I can't think of anywhere else where there is a concentrated, vast set of data on consumers that can easily be used in credit scoring.

I mean, there's not that many players. It's not like you have to go get all the mom and pop rental agencies in the world to furnish data, like The data is right there, we just have to work with the lenders to get it moving, and the exciting part is it's moving, like there are hands on keyboards across the industry, actually coding to Metro 2, actually getting ready to make this go live.

[00:04:19] Rich: So, are you working with the CDIA at all on how this kind of fits into the Metro 2?

[00:04:24] Liz: Yeah, so that's been done probably since you and I last talked, but there's a whole section now on the Metro 2 reporting guidance on how to fit these loans into Metro 2. It's not perfect. Metro 2 was not built for a 2-week product. So, there's guidance on how to kind of fit the oval peg into the round hole and we're going to see lenders get started and we'll see how it works and we'll pivot from there. The CBIA is involved and going to be watching this. so, as we begin to analyze the data, we'll make sure that we got it right.

[00:04:57] Rich: So, from a, integration perspective, if I heard you correctly, you're kind of keeping this data in its own separate bucket for now. So, lenders that actually want to take advantage of the data through systems like our Modellica platform, is that then basically a separate call into the repository?

[00:05:14] Liz: It'll be the same call, but there's an on off switch. Okay. So, do you want the data to come in with the rest of the data or not? And so, before you'd want to turn it on, you would want to ensure that your models are equipped to recognize it. And again, there's a bunch of different indicators on the find out fade later loads that'll help you isolate it and determine whether it goes into each different piece of the credit model that you've deployed.

[00:05:38] Rich: I know you guys did a lot of research on the market and I remember on our last call you had shared some really good reports that looked at uses of the product, age, distribution and whatnot. Any refresher when we think about it like the vantage score and the buy now pay later consumer. Where do you typically see they kind of fit from a van score?

[00:06:00] Liz: I mean, since we did that study a couple of years ago, I think it's expanded usage across all credit bands. Now, it's certainly more concentrated on younger and higher risk consumers. But because of all the different types of vendors, merchants know the fine operators are working with and all the different options to finance everything from a pair of speakers to your grocery bill to home improvement project. There’s really a lot of different consumers using this product. So, I wouldn't even say it's particularly a certain profile of consumer anymore.

[00:06:32] Rich: Well, it's interesting. You mentioned sneakers and you mentioned home improved, brought home improvement products, sneakers that are. I know some people spend 200 on stickers, I don't get it, but certainly you just outlined a very large spread on what the loan size would be, for the audience. And I think it's maybe good to talk about the fines by now pay later, right? Because I think that's kind of not clear yet necessarily. But if the CDI has been involved, I assume that they've come up with a very definitive definition or they're trying.

[00:07:06] Liz: Yeah. So, I mean, there is a gray area. So, like, there's the pay in four, pay in six product is clearly a fine operator. That's kind of universally recognized as a buy now pay later product across the industry. When you get into a longer term, maybe six, 12-month loan for a larger purchase, that's, I'm calling those point-of-sale loans. But a lot of the features of those loans are more similar to the pay in for because consumers are meant to use them transactionally. So, when I think about something that deserves the special treatment on the credit file that really shouldn't flow into all the attributes today, It's something that's transactional. You're using a buy now pay later loan instead of a credit card swipe for a larger purchase. They're likely to be used more than once a year. And so that's really where the rubber hits the road in terms of what fits on the credit file and what could potentially not work in existing scores. So, if you're using something three, four times a year, that's when it starts breaking down and the consumer could have an undue negative impact on their scores. If you call it a personal loan, assume a consumer takes out five in December because they're holiday shopping, like that's not going to do what you would expect, you want it to do to our credit score. That, for an installment loan, would be risky behavior. For buy now pay later, when it's replacing a credit card swipe, which might not be risky behavior. That might be exactly what you're expected to do for the product. So, it's going to take some time for credit models to understand what to do with these loans, especially the ones that are more frequent.

[00:08:42] Rich: Now, are there any specific kinds of business industry code? Or remark code that's attached to a BMPL. So, do I know that this was used for groceries versus a retail product?

[00:08:57] Liz: No, they'll come in just like any other installment loan without a use, what it was used for in particular. But you'll just see the terms.

[00:09:06] Rich: Okay. So, Fern and Klarna, they'll have a special kind of business industry code that they use for buying out pay later.

[00:09:13] Liz: There'll be a specific one for these trades, and that will drive part of the partitioning.

[00:09:19] Rich: Right. Right. Now, what do you typically see? from the credit risk underwriting for buying out pay later products themselves. Like if I'm under, if I'm buying a 200 pair of sneakers, what are you seeing lenders do from an underwriting perspective in the buying out pay later space?

[00:09:35] Liz: Yeah, they underwrite it just like any other dynamic models.

[00:09:39] Rich: Good. So, you're feeling good about the progress that you guys are all making. Any sense of how your brothers and sisters at Equifax and Experian are doing there? Yeah.

[00:09:52] Liz: So, we're all kind of in this together, and I like to think that we're working towards a common goal of having this data available across the industry. I know that the industry is holding us accountable to get the data in the hands of especially the traditional lenders who. Is there to be able to access the data as they share their own data? So, we're working together on it. I know there's been some announcements that some lenders are beginning to report. And I think that's great news for the industry, regardless of which bureau that data comes into the first year. Ultimately, the data is going to have to reach critical mass in order to be available to the lending community. So, I think we'll all kind of be on the same path there. And any tipping point, any movement towards. Lenders actually getting the data in the hands of consumers is positive news. Once data starts coming in, we'll all, by law, have to expose it to consumers so they can audit it, see it on their personal credit report, even though it won't be used in Scoring or credit decisions yet. It's just a great first step for consumers to get used to seeing that audit report.

[00:10:51] Rich: Good, good. So, when you think about the product roadmap, I assume there's plans to integrate this data into trigger files and bill use for account monitoring, collections and stuff like that. Well, good. We'll be looking forward to seeing how things progress. And I know when we spoke last time, one of the things that was near and dear to your heart was around women in FinTech. And I think I, walking around, here at FinTech Meetup. I saw that they're either having a session or they have an area. Any update on progress that's been made there that you want to share?

[00:11:21] Liz: Yeah, well, I just got back from a roundtable lunch where women were talking about their careers in FinTech. So that was valuable and made some connections that I'm sure I'll run into at these conferences. I mean, just walking the floor here, there's a lot more women, a lot more women involved in this industry. A lot more women are excited about this industry, which I really like, even when I think about hiring for my own team, like. It's hard to find diverse candidates, across the financial services ecosystem, but I want to break down barriers. Like there's no reason you couldn't hire a rockstar product manager from a serial company to manage a credit product. I mean, there's a learning curve. No matter what, when you change companies, you can learn a new industry. People are flexible and they learn quickly. So, if we can open our eyes to hiring from more diverse backgrounds, we can help force or help enable a lot more diversity, right?

[00:12:16] Rich: Right. But you need to help promote the message, in, maybe high school and in college, right? This is a good area to focus on. when I talk to friends of mine and talk about their children, they say they enjoy math. I'll say, man, you got to just push that kid to learn how to get into statistics and model building. Because there's not one industry that doesn't use analytics, right? So that opportunity to influence the future is really good. Well, Liz, I want to thank you for coming today. Once again, this is Rich Alterman, broadcasting live at FinTech Meetup, our third episode today of The Lending Link, where we had the opportunity to spend time with Liz Pagel, Senior Vice President and the leader of TransUnion's consumer lending line of business within the financial services vertical.