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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the out Thoughts podcast.

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Speaker 2: I'm Tracy Alloway.

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Speaker 3: And I'm Joe. Why isn't thal Joe?

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Speaker 2: I have a confession to make. Yeah, I do a

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Speaker 2: lot of online shopping, like a lot more than is healthy.

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Speaker 4: Probably more than I realized from looking over your shoulder

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Speaker 4: in the office and asking you what you're looking up,

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Speaker 4: because I do do that and you never like it.

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Speaker 2: But I still do know because sometimes I am, in

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Speaker 2: fact shopping online in the office. But a few years

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Speaker 2: ago I noticed a phenomenon, a change when it came

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Speaker 2: to online shopping. You know what that was, I can guess, yeah,

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Speaker 2: all right, all of a sudden, whenever I was checking out,

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Speaker 2: you would get an offer, a little button usually that

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Speaker 2: would say do you want to take a buy now

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Speaker 2: pay later option? And usually it's with a company like

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Speaker 2: Klarna or a firm or something like that. They're everywhere now.

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Speaker 4: So, like I've certainly seen all these buttons, I've never

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Speaker 4: used it. I don't really know why I haven't, because like,

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Speaker 4: why shouldn't I spread out? Why shouldn't I?

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Speaker 3: Like for real?

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Speaker 4: Like my understanding is that the core of the business

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Speaker 4: model works is that there's no like formal interest, right,

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Speaker 4: so if you make one hundred dollars purchase, you get

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Speaker 4: to spread it out, say in four payments of twenty

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Speaker 4: five dollars, and so whether it's one hundred dollars right

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Speaker 4: now or one hundred dollars and four months, it's the

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Speaker 4: same from the end buyer, which, of course in theory

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Speaker 4: because there's a.

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Speaker 3: Time value of money, is not intuitive.

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Speaker 4: But the idea is that the retailer implicitly pays the

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Speaker 4: interest because it's a form of customer acquisition, and so

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Speaker 4: the retailer is implicitly willing to take one hundred dollars

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Speaker 4: or whatever over x period of time rather than all

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Speaker 4: at once in exchange for essentially making it easier for

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Speaker 4: customers to buy. But from the customer person like you

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Speaker 4: were me, who presumably have the money to make to

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Speaker 4: buy what we're purchasing, I still don't get why we

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Speaker 4: don't all use buy now, pay later, because why not

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Speaker 4: spread out our purchases further out into the future.

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Speaker 2: You know how Rebel Wilson described it in a firm commercial.

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Speaker 3: I didn't. I don't. I haven't seen it.

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Speaker 2: Like eating a tub of ice cream, but spreading out

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Speaker 2: the calories.

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Speaker 3: Over four weeks exactly, why don't.

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Speaker 2: It's still six hundred calories, right, that's the issue. But

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Speaker 2: I think, okay, so this is obviously a nuanced subject. Yes,

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Speaker 2: so clearly zero percent interest doesn't sound like a problem.

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Speaker 2: But obviously if you fail to pay, you pay a penalty.

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Speaker 3: Then the penalties are building, right.

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Speaker 2: So there's that. But the other issue with some of

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Speaker 2: these buy now, pay later items, and it's really interesting.

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Speaker 2: I kind of think about this as a parallel to

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Speaker 2: the private market and credit. So this idea that there's

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Speaker 2: this like whole world of additional credit or leverage that

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Speaker 2: we don't actually have very good data on, and that

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Speaker 2: is growing, right. I think that is the key.

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Speaker 4: So here's how I see the issue, which is that

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Speaker 4: if I could have a tub of ice cream and

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Speaker 4: it spread out the calories over four months.

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Speaker 3: I would certainly do that.

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Speaker 4: That sounds pretty great because I burn a certain number

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Speaker 4: of calories in the day, and so if I had

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Speaker 4: four months to burn them that that would be far

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Speaker 4: more efficient. But the flip side is, well, I would

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Speaker 4: eat more ice cream, so I would eat four times.

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Speaker 4: I would eat four times as many tubs of ice

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Speaker 4: cream in that given day, knowing that I have this

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Speaker 4: four month calorie budget. And so then you still get

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Speaker 4: the question of like, Okay, maybe there's not a formal interest,

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Speaker 4: or maybe there's penalties, but I'm buying a lot more

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Speaker 4: with today's buying power, and that may still yet prove

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Speaker 4: to be unsustainable to the end consumer, even if it

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Speaker 4: doesn't formally. Clock is what we measure as consumer debt.

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Speaker 2: Here's the added layer. Let's say you're trying to lose

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Speaker 2: weight or trying to be healthy, and you have like

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Speaker 2: a dietician. Imagine eating the ice cream, but him never

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Speaker 2: knowing that you're eating the ice cream, right, Like, this is.

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Speaker 3: Well, I lied to my doctors all the time. I mean,

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Speaker 3: I'm used to that. Okay, all right.

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Speaker 2: Rather than us debate this, we do in fact.

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Speaker 4: How much didn't you well, you know, like once a week,

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Speaker 4: I like go through a three milligram Think I did

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Speaker 4: actually quit?

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Speaker 2: And I'm saying that told me this morning you said

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Speaker 2: you were not going to have any more zins for

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Speaker 2: the restaurant, and I think I would take the opposite

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Speaker 2: side of that bet. But now you've committed in public,

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Speaker 2: public best, So we'll see how it goes. Okay, rather

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Speaker 2: than us talk about this, we do, in fact have

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Speaker 2: the perfect guest. We're going to be speaking with Julie Morgan.

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Speaker 2: She is a president of the Century Foundation, formerly at

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Speaker 2: the Consumer Financial Protection Bureau of the CFPB, where she

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Speaker 2: thought a lot about the buy now, pay later phenomenon.

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Speaker 2: So we're going to get some of her thoughts right now. Julie,

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Speaker 2: welcome to the show.

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Speaker 5: Thank you. I'm so excited to be here.

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Speaker 2: So how did this happen? Like, how did this business

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Speaker 2: model actually come about? Because it felt very sudden to me,

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Speaker 2: But on the other hand, I was outside of the

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Speaker 2: US for a long time, so maybe it was quite gradual.

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Speaker 5: I'm really excited to answer this question for a number

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Speaker 5: of reasons, including the way that you all introduced this,

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Speaker 5: because you spent a lot of time talking about BUYEOW

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Speaker 5: pay leader as this pay in for zero interest maybe

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Speaker 5: like no fee or low feed model, and we did

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Speaker 5: see that model arise during the pandemic and become a

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Speaker 5: much more assalient part of the way consumers were paying

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Speaker 5: for goods during the pandemic. And so if I could

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Speaker 5: take you back to kind of those early years at

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Speaker 5: the CFPB. Where we're coming out of the pandemic, we're

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Speaker 5: seeing this rise in buy now pay lead. You know,

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Speaker 5: regulators and researchers started to investigate this to understand what

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Speaker 5: it meant for consumers and what the risks were. And

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Speaker 5: I think there were kind of two fundamental assumptions about

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Speaker 5: buyeow pay leader at that time. One was just what

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Speaker 5: we said, it's a pay in for no interest, low

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Speaker 5: or no fee model. And then two people were using

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Speaker 5: this in kind of the like what I think of

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Speaker 5: as a stereotypical gen Z kind of way. They were

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Speaker 5: using it to finance a purchase that was slightly larger

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Speaker 5: or more extravagant than what they would have bought, and

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Speaker 5: it was usually on something like a purse or an

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Speaker 5: item of clothing. What we've found over time is that

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Speaker 5: both of those assumptions turned out to be wrong. Right,

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Speaker 5: So number one, what we're seeing right now as buy now,

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Speaker 5: pay later and calling by now pay lead is not

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Speaker 5: in fact always that pay in for no interest product.

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Speaker 5: And in fact, many of the companies that we associate

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Speaker 5: with buy now pay lead, like a firm or Klarna,

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Speaker 5: are mostly not offering that product. So a firm says

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Speaker 5: that product is only twenty percent of their business. The

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Speaker 5: rest of their business is what I would think of

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Speaker 5: as like a point of sale loans or short term

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Speaker 5: installment loans with interest rates ranging from you know, ten

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Speaker 5: to thirty percent and terms that range from thirty days

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Speaker 5: to six or eighteen months.

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Speaker 4: So it's buy now, pay later, but it's more like

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Speaker 4: just a formal loan.

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Speaker 5: Yeah, I mean it's.

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Speaker 4: Payment and installment and there's a schedule, but also there

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Speaker 4: is a nominal interest rate that's visible.

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Speaker 5: To the consumer exactly, and I think it is by now,

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Speaker 5: pay later, in the sense that any loan, including your mortgage, yes,

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Speaker 5: buy now, pay later. Right. So they've kind of kept

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Speaker 5: this marketing that I think was really appealing to consumers

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Speaker 5: and that sort of soothed regulators that these products were

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Speaker 5: fairly low risk, and then started shifting that into these

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Speaker 5: products that look very different.

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Speaker 4: Okay, whether we're talking about the new products or the

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Speaker 4: sort of the traditional BNPL the way Tracy and I

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Speaker 4: described it in the beginning. Currently, what kind of collection

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Speaker 4: of data like when that happened? So we know, like

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Speaker 4: credit card data gets collected. There's public statistics about just

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Speaker 4: the sheer volume of credit cards what do we have

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Speaker 4: right now in terms of where this gets slotted and

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Speaker 4: how it's collected as data.

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Speaker 5: Yeah, we don't have a ton. So we have a

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Speaker 5: couple of different ways of looking at buy now, Pay later.

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Speaker 5: Most of it is through government sources, So the Federal

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Speaker 5: Reserve and the CFPB have the ability to pull data

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Speaker 5: from the companies, and if you look at the CFPB's

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Speaker 5: reports on buy now, Pay Later, that is what they're doing.

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Speaker 5: They're pulling transaction level data through market monitoring orders.

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Speaker 4: That's not but when you say market monitoring orders, just

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Speaker 4: to be clear, the companies are compelled to regularly update

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Speaker 4: the CFPB with some level of data.

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Speaker 5: They're compelled to provide data. The regularly piece is not

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Speaker 5: part of it. So this tends to be a one off, right.

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Speaker 5: We don't have a consistent stream of data coming from

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Speaker 5: the companies, and so that gives us like a small

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Speaker 5: window into what's happening in the buyout pay latter space,

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Speaker 5: but it's really not enough. And then we have survey data,

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Speaker 5: and that's where you see lending Tree and Axios and

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Speaker 5: others asking consumers, and I think it's important to understand

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Speaker 5: that they are you're just kind of getting what the

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Speaker 5: consumer thinks they have, and it's it really inhibits our

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Speaker 5: ability to get at are you using paying for are

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Speaker 5: you paying interest? And all those things?

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Speaker 2: How does buy now Pay later fit into private credit

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Speaker 2: scores like FICO?

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Speaker 5: Right?

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Speaker 2: Because I imagine you know, if you're applying to a bank,

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Speaker 2: they're going to look at your FICO score if you

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Speaker 2: want something like a mortgage. And even though buying now

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Speaker 2: Pay later seems to be for mostly smaller items at

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Speaker 2: the moment, so maybe you owe like a couple hundred

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Speaker 2: or something via a firm, maybe you also owe a

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Speaker 2: few thousand via Klarna, right, And I imagine if you're

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Speaker 2: a bank using Fyco scores, you would want to know

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Speaker 2: that information.

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Speaker 5: Yeah, so we don't know this information right now. Fico

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Speaker 5: announced recently that they plan to start using buy now,

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Speaker 5: Pay Later as part of their scoring model, and we've

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Speaker 5: seen the companies be extraordinarily resistant to that. So Clarnet

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Speaker 5: and others are saying that they're not going to turn

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Speaker 5: over data until they get certain assurances from Fyco about

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Speaker 5: how that data is going to be used. So what

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Speaker 5: we have right now is kind of snapshots in time

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Speaker 5: to understand how people are stacking, you know, buy Nowpay

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Speaker 5: Later on top of credit card debt and on top

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Speaker 5: of other products. And the research that we have there,

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Speaker 5: which is somewhat limited, does suggest that people are stacking this,

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Speaker 5: you know. It tells us that about sixty percent of

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Speaker 5: the people who are using buy now Pay Later have

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Speaker 5: a SubTime credit score. It tells us that they are

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Speaker 5: mostly using it on top of credit card debt. And

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Speaker 5: it's also suggesting that before people take on buy now

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Speaker 5: pay Leader, there's a slight uptick in their use of

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Speaker 5: their credit cards. So there's a suggestion that it's really

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Speaker 5: like you're maxing out one form of debt and then

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Speaker 5: taking on another.

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Speaker 4: I just looked up the Q two New York Fed

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Speaker 4: Household Debt Report.

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Speaker 3: I think it just came out a few days ago.

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Speaker 3: I must've it's Q two anyway.

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Speaker 4: It says there's one point two to one trillion in

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Speaker 4: total credit card balances outstanding. Do we have something that

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Speaker 4: we have a guess for what is the number of

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Speaker 4: BNPL loans of any form currently outstanding, so that we

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Speaker 4: can benchmark that against credit cards.

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Speaker 5: We have numbers that are reported by the companies. We

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Speaker 5: don't have data that is reported by a government. So

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Speaker 5: just like that, I think that the best we have

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Speaker 5: is that the transaction volume is around one hundred and

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Speaker 5: sixteen billion, and that's up from about thirteen point eight

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Speaker 5: billion in twenty twenty, so this is growing pretty rapidly.

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Speaker 4: So it's it's a non zero. I mean, it's not

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Speaker 4: like clear like still like a fraction of credit cards,

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Speaker 4: but this is beyond the rounding air, that's right, Okay.

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Speaker 2: Do we have any sense of how usage patterns have changed?

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Speaker 2: So for instance, you know, during the pandemic, did we

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Speaker 2: see an uptick in buy now, pay later use as

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Speaker 2: people were struggling because they didn't have jobs or had

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Speaker 2: short term cash flow issues? Has it come down since then?

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Speaker 5: We're seeing an uptick in people's use of bineopi lator,

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Speaker 5: and we're seeing shifting patterns of what they're using it

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Speaker 5: to purchase. So I think I said earlier that there

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Speaker 5: was this assumption that people were using this to purchase

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Speaker 5: clothing or other kind of luxury goods. And you know,

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Speaker 5: even back in twenty twenty two when the CFPB put

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Speaker 5: out its first report here, we saw that that wasn't true.

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Speaker 5: People were using bunopaylator for things like groceries and education costs.

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Speaker 5: The surveys that have come out recently showed that that's growing.

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Speaker 5: So there was I think in an Axio survey and

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Speaker 5: a lending Tree survey, they showed somewhere between fourteen and

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Speaker 5: twenty five percent of bineopay lator users are using it

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Speaker 5: to purchase groceries. They're showing that a bigger percentage of

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Speaker 5: people are using binopulator for medical and dental costs than

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Speaker 5: for the things people might assume stereotypically it's used for,

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Speaker 5: like dining out or ordering in, or even like purchasing

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Speaker 5: concert tickets and things like that.

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Speaker 3: Is it bad?

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Speaker 4: I mean, like, you know, it seems like people are

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Speaker 4: using buy o pay later for dental or they're using

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Speaker 4: it for groceries or whatever, and like, you know, the

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Speaker 4: idea that people are obviously stressed and have to spread

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Speaker 4: out or borrow money to make those purchases is obviously

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Speaker 4: I guess there's an inherent angst about that because these

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Speaker 4: are human essentials. But is it really per se bad

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Speaker 4: or is it just like no, this is like yet

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Speaker 4: another tool that people have to sort of you know,

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Speaker 4: smooth consumption and so forth.

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Speaker 5: I think it's bad that we're seeing an uptick in

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Speaker 5: the use of credit products to pay for essentials. That's

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Speaker 5: not to say that buy now, pay later is particularly

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Speaker 5: the enemy there, but you know, this is part of

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Speaker 5: the reason I've been interested in consumer credit overall is that,

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Speaker 5: you know, we see these broader changes go on in

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Speaker 5: the policy space. In particular, recently, we've seen Donald Trump

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Speaker 5: and Republicans in Congress push through a bill that cuts Medicaid,

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Speaker 5: cuts food assistance, you know, makes education more expensive, and

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Speaker 5: the availability of consumer credit products that are kind of

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Speaker 5: waiting in the wings there blents our ability to really

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Speaker 5: understand what those changes mean. So, you know, we know

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Speaker 5: that the Medicaid cuts will result in people having more

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Speaker 5: expensive health care, but we need to be looking at

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Speaker 5: the fact that that's going to result in buy now,

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Speaker 5: pay later loans, It's going to be an opportunity for

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Speaker 5: medical credit cards from places like Synchrony with per credit,

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Speaker 5: and so it's relevant to our overall economic situation. It's

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Speaker 5: relevant to the kind of like what I think is

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Speaker 5: like a silent financial crisis that's happening for the vast

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Speaker 5: majority of families in the United States, but it's not tracked.

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Speaker 5: You know, we've been talking about buy now, Pay later

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Speaker 5: really not being tracked, but the overall sort of like

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Speaker 5: consumer credit conditions for families are really not tracked and

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Speaker 5: talked about in that way. We see economists going on

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Speaker 5: TV talking about consumption and CPI and GDP, where families

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Speaker 5: are kind of sitting around the kitchen table talking about

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Speaker 5: Klarna and a firm and Sally May and Abvian.

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Speaker 2: When you were at the CFPB, how are you thinking

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Speaker 2: about buy now, Pay Later from a sort of regulatory perspective.

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Speaker 5: Yeah, so we were looking at the risks of the products,

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Speaker 5: and then we were kind of trying to look you know,

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Speaker 5: we've had this phenomenon with consumer credit products across the board,

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Speaker 5: not just specific to buy now, Pay Later, but it

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Speaker 5: was really evident at CFPB that you have these sort

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Speaker 5: of like new entrants into a market whose products are

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Speaker 5: framed very differently than a traditional consumer credit product, and

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Speaker 5: it's often part of the marketing pitch. It's like we're

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Speaker 5: not alone, we're not equity, We're this totally different thing,

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Speaker 5: and it preys on consumers hesitants to use debt. And

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Speaker 5: then also you know this idea that we might float

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Speaker 5: above any regulatory scheme, so for buying out, pay lead

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Speaker 5: and for these other products like earned wage access or

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Speaker 5: income share agreements. You know, we were looking at like

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Speaker 5: what is this under the law, and with buying now

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Speaker 5: pay later, particularly the products like where Klarna or a

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Speaker 5: firm is offering basically like a digital or physical card

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Speaker 5: where you can make repeated purchases where you have a

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Speaker 5: you know, an overall lit It starts to sound like

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Speaker 5: a credit card, right, And so you know, cfhobe's initial

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Speaker 5: approach on regulation here was to really just say, like,

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Speaker 5: you've got to follow some of the fundamental basics that

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Speaker 5: apply to the product that you are, right, And so

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Speaker 5: that was really meant to help people with some of

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Speaker 5: the real kind of basic consumer protections around managing disputes

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Speaker 5: and getting accurate billing statements. The other thing CFPP was

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Speaker 5: doing was on the enforcement and supervision side, so really

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Speaker 5: taking a look at these companies the same way we

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Speaker 5: do any other financial institution and making sure they're following

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Speaker 5: the law. And I think it's important to talk about

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Speaker 5: that piece because we've seen the Trump administration pull back

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Speaker 5: on the proposed regulations, but we've also seen them pull

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Speaker 5: back on enforcement. They specifically said they would deprioritize by now,

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Speaker 5: pay later in enforcement, and so there are these other

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Speaker 5: real basics that we're seeing fall apart. You know, if

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Speaker 5: you look at what consumers are complaining about right now,

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Speaker 5: they're saying, I had auto pay on my you know,

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Speaker 5: my Clarina or my after pay and I tried to

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Speaker 5: take it off and the company continues to charge me.

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Speaker 5: These are like really basic questions of whether the companies

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Speaker 5: are falling consumer financial laws and you know cfpps just

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Speaker 5: off the job.

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Speaker 4: So you mentioned that in the research, prior to someone

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Speaker 4: beginning to use BNPL, increase in their credit card usage, right,

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Speaker 4: why does someone flip over? Like why doesn't so some

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Speaker 4: of these purchases. What is insufficient about the existing credit

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Speaker 4: card system? Is it that they're hitting their credit limits

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Speaker 4: or what is the situation such that then BNPL becomes

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Speaker 4: more attractive.

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Speaker 5: Yeah, I think we need to take a deeper look

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Speaker 5: at credit utilization because I do think there are people

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Speaker 5: who are flipping over and they're using their available credit

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Speaker 5: balance and then they're shifting to bind a pay leader.

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Speaker 5: But I think there are a lot of other people

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Speaker 5: who are just trying to juggle their essentials, you know,

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Speaker 5: their groceries, and their kids back to school clothes, and

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Speaker 5: they're doing it through whatever feels like the most convenient

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Speaker 5: payment method. So the buy now pay lead companies have

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Speaker 5: made a real effort at being that convenient payment method,

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Speaker 5: both through kind of point of sale but also through

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Speaker 5: their apps. So they're pushing their users towards an app

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Speaker 5: that both makes it more convenience to make the payment

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Speaker 5: but also pushes you advertisements for different products. And so

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Speaker 5: I think we see people brought in there. But you

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Speaker 5: mentioned earlier why you don't use buye now Pay later,

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Speaker 5: And I do think it's important to great question. We

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Speaker 5: could do a half hour on your personal finances, but

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Speaker 5: I think it's important to know that even while there's

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Speaker 5: a big percentage of people using this to cover basics,

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Speaker 5: there is kind of a split in buy now pay

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Speaker 5: lead where if you talk to consultants who look at

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Speaker 5: this really closely, they're seeing people like you or I

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Speaker 5: who can I'm making an assumption about your finances get paid. Well, okay,

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Speaker 5: you know about like you or I who can make

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Speaker 5: a purchase, but it's a painful purchase, you know, maybe

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Speaker 5: it's like a new television or something that you just

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Speaker 5: don't want to pay upfront, so there are people using

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Speaker 5: it in that way too interesting.

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Speaker 2: You mentioned ease of use, and I think this is

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Speaker 2: really key. What are the conversations like with retailers, So like,

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Speaker 2: what's the pitch that buy now, pay later platforms are

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Speaker 2: going to make to I don't know, a Walmart or

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Speaker 2: whoever to kind of plug directly into their websites.

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Speaker 5: Yeah, so the pitch is basically basket size is and

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Speaker 5: then overall transaction volume. Right, So they're showing that they're

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Speaker 5: getting the retailers about ten percent more in terms of

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Speaker 5: the basket size, so the amount of things that people

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Speaker 5: are purchasing, and then some of the companies are reporting

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Speaker 5: up to eighty percent more in terms of the actual transaction.

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Speaker 5: So that's pretty significant for retailers, and I think it

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Speaker 5: is what is propelling retailers to cut deals with Klarna

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Speaker 5: or after pay and to pay the costs which are

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Speaker 5: higher than interchange on a credit card. So in many cases,

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Speaker 5: you know, we're looking at costs that are somewhere between

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Speaker 5: one to eight percent of the transaction, plus like a

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Speaker 5: flat fee that might be like thirty cents per transaction.

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Speaker 5: So it's pretty significant. I think the other part of

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Speaker 5: the pitch is and this is this is one of

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Speaker 5: the pieces that was really kind of consuming a lot

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Speaker 5: of our attention at CFB is the ability to use

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Speaker 5: people's data to drive those purchases. So I think if

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Speaker 5: you're a merchant looking at those big fees, if you're

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Speaker 5: a Walmart or Amazon or Whoe or you're not just

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Speaker 5: saying like, let me put this in Klarna or a

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Speaker 5: firm's hands to hope that people use it more, but

400
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Speaker 5: let me use the data I have on hand to

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Speaker 5: try to drive more purchases. And like a company that

402
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Speaker 5: has a lot of information about what I like to

403
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Speaker 5: buy and a lot of information about how much money

404
00:20:18,600 --> 00:20:20,440
Speaker 5: I have to do it has like a pretty potent

405
00:20:20,840 --> 00:20:23,720
Speaker 5: set of data to help drive those purchases. So I

406
00:20:23,720 --> 00:20:25,760
Speaker 5: think that's part of the appeal for emergents as well.

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Speaker 4: Do we have a way of essentially measuring from the

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Speaker 4: consumer's perspective a sort of like for like interest rate

409
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Speaker 4: that they payment because as you described, like in some

410
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Speaker 4: cases they're clear right, in some cases they're more embedded

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Speaker 4: because it's free for four months, but then there's like

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Speaker 4: a penalty. In some instances, it's this thing that looks

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Speaker 4: like a credit card, but maybe structurally there's a different

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Speaker 4: way of getting from point A to point B, Like

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Speaker 4: can we measure like, okay, if this were a credit

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Speaker 4: card payment with a fifteen percent rate, this is the

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00:20:59,119 --> 00:21:01,919
Speaker 4: apples to apples compared you're paying eighteen percent or twelve percent?

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Speaker 3: Like, are we able to are you able to do that?

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Speaker 5: Me personally, I'm not able to do that. But you

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Speaker 5: know at the CFPV we did do that on certain products.

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Speaker 5: We didn't do that for buying now pay later in

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Speaker 5: many cases because the products we were studying had almost

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Speaker 5: no cost, right, they were no interest in, no fee.

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Speaker 5: Maybe they had a late fee that might kick in,

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Speaker 5: but remember where they're requiring auto payments, which most of

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Speaker 5: the companies are, those late fees are less likely to

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00:21:27,880 --> 00:21:31,439
Speaker 5: be incurred. But I think the sort of relevant thing

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Speaker 5: is to understand how much people are paying overall in access,

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Speaker 5: fees and interest for the goods and services that they need.

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Speaker 5: And that's incredibly difficult to do because these companies are

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Speaker 5: sort of layering these different products. So you have a

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00:21:46,560 --> 00:21:48,840
Speaker 5: credit card with a set interest rate, but you might

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Speaker 5: be incurring debt on a number of different products across

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Speaker 5: multiple band pail providers. That each have different terms and

435
00:21:57,440 --> 00:22:00,960
Speaker 5: different costs, so it's really difficult. It's difficult for us

436
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Speaker 5: to understand from a research perspective, it's even more difficult

437
00:22:03,960 --> 00:22:06,800
Speaker 5: for a consumer, I think, to make those choices in

438
00:22:06,840 --> 00:22:20,600
Speaker 5: those trade offs.

439
00:22:22,880 --> 00:22:24,879
Speaker 2: Just going back to the data aspect of those for

440
00:22:24,880 --> 00:22:27,640
Speaker 2: a second, you said that some of the platforms are

441
00:22:27,760 --> 00:22:35,120
Speaker 2: reluctant to report to FYCO and are asking for certain assurances.

442
00:22:35,800 --> 00:22:37,320
Speaker 2: What exactly do they want here?

443
00:22:37,760 --> 00:22:40,600
Speaker 5: I mean, I've looked into this question. They haven't been

444
00:22:40,680 --> 00:22:43,000
Speaker 5: super clear I think about what they want. They've sort

445
00:22:43,000 --> 00:22:45,119
Speaker 5: of made some general statements that they want to make

446
00:22:45,160 --> 00:22:49,600
Speaker 5: sure that you reporting this data won't be used negatively

447
00:22:49,680 --> 00:22:53,600
Speaker 5: to affect people. I mean, this is really just my speculation.

448
00:22:53,800 --> 00:22:56,159
Speaker 5: But what I think is really interesting about this is

449
00:22:56,160 --> 00:22:59,119
Speaker 5: the companies are playing on this kind of broader skepticism

450
00:22:59,359 --> 00:23:03,320
Speaker 5: about credit reporting and FYCO overall, because I think, you

451
00:23:03,359 --> 00:23:06,159
Speaker 5: know people's experience and certainly what we saw in the

452
00:23:06,200 --> 00:23:11,959
Speaker 5: research at CFPP is that credit reporting has predominantly been

453
00:23:12,040 --> 00:23:15,040
Speaker 5: used to ding people and to coerce them into making

454
00:23:15,080 --> 00:23:17,320
Speaker 5: payments they might not want to make. So like medical

455
00:23:17,359 --> 00:23:21,040
Speaker 5: debt is a great example of that. Cfb's research showed

456
00:23:21,800 --> 00:23:25,199
Speaker 5: it really had no value for the fundamental purpose of

457
00:23:25,280 --> 00:23:29,280
Speaker 5: credit reporting, which is evaluating people's ability to repay, and

458
00:23:29,320 --> 00:23:32,240
Speaker 5: it was mostly being used to course. So when Klarna says,

459
00:23:32,840 --> 00:23:34,360
Speaker 5: you know, we want to make sure this isn't used

460
00:23:34,359 --> 00:23:37,440
Speaker 5: to ding people, that feels very noble. But I think

461
00:23:37,480 --> 00:23:38,800
Speaker 5: at the end of the day we should be kind

462
00:23:38,840 --> 00:23:40,560
Speaker 5: of skeptical of why they don't want to report.

463
00:23:40,720 --> 00:23:44,520
Speaker 4: Explain this further about medical debt and would you say

464
00:23:44,560 --> 00:23:47,000
Speaker 4: it has no value? But also it's been used to course,

465
00:23:47,080 --> 00:23:49,720
Speaker 4: what are the conditions with medical debt and FYCO.

466
00:23:50,160 --> 00:23:52,959
Speaker 5: Yeah, So we looked at medical debt and credit reporting

467
00:23:53,000 --> 00:23:56,320
Speaker 5: really closely at CFPB, and what we saw was that

468
00:23:56,520 --> 00:23:59,920
Speaker 5: it didn't have predictive value. So medical debt was not

469
00:24:00,200 --> 00:24:03,119
Speaker 5: predictive of whether you would repay other types of debt.

470
00:24:03,680 --> 00:24:05,480
Speaker 5: And part of the reason is that a lot of

471
00:24:05,520 --> 00:24:08,560
Speaker 5: medical debt that is reported on people's credit reports is

472
00:24:08,640 --> 00:24:11,480
Speaker 5: inaccurate in one way or another. You know, it might

473
00:24:11,560 --> 00:24:14,320
Speaker 5: be that the debt collector is going after you for

474
00:24:14,480 --> 00:24:17,080
Speaker 5: a debt that is larger than what you think you

475
00:24:17,119 --> 00:24:19,840
Speaker 5: pay because you think your insurance company ought to have paid.

476
00:24:20,240 --> 00:24:22,560
Speaker 5: It might be that you actually already paid the debt

477
00:24:22,600 --> 00:24:25,199
Speaker 5: and that information hasn't gotten to the debt collector, so

478
00:24:25,280 --> 00:24:27,600
Speaker 5: it's really kind of this junk data that's sitting on

479
00:24:27,640 --> 00:24:28,880
Speaker 5: people's credit reports.

480
00:24:29,480 --> 00:24:32,560
Speaker 4: And then explain the coercion element, like how does it

481
00:24:32,880 --> 00:24:36,720
Speaker 4: so medical debt isn't a good predictor of someone's ability

482
00:24:36,760 --> 00:24:40,119
Speaker 4: to pay a future loan, so it's not great for that.

483
00:24:40,280 --> 00:24:43,280
Speaker 4: And yet what it's still like people because it affects

484
00:24:43,320 --> 00:24:46,040
Speaker 4: their credit card scorers, feel this extra pressure to pay it.

485
00:24:46,240 --> 00:24:49,879
Speaker 4: Like explain sort of like the effect on the patient

486
00:24:50,000 --> 00:24:50,520
Speaker 4: in this case.

487
00:24:50,640 --> 00:24:54,159
Speaker 5: Yeah, exactly. So the constituency that wants medical debt on

488
00:24:54,200 --> 00:24:57,159
Speaker 5: your credit reports is primarily third party debt collectors. And

489
00:24:57,200 --> 00:24:59,400
Speaker 5: why do they want that there? They want it there

490
00:25:00,080 --> 00:25:02,919
Speaker 5: so that they can call you and say, you know,

491
00:25:03,200 --> 00:25:04,679
Speaker 5: this is going to be this debt or it's going

492
00:25:04,720 --> 00:25:07,480
Speaker 5: to hurt your credit. And I think at CFB we

493
00:25:07,520 --> 00:25:09,240
Speaker 5: tried to spend a lot of time thinking this through

494
00:25:09,320 --> 00:25:11,840
Speaker 5: from the consumer perspective and what that feels like.

495
00:25:12,200 --> 00:25:14,080
Speaker 4: So actually I was not familiar with that at all.

496
00:25:14,119 --> 00:25:17,800
Speaker 4: But just to go further, is this, like I hadn't

497
00:25:17,880 --> 00:25:20,840
Speaker 4: really thought about the sort of behind the scenes fight

498
00:25:21,359 --> 00:25:24,400
Speaker 4: of what does and doesn't go into a fight though

499
00:25:24,400 --> 00:25:27,920
Speaker 4: but obviously that's very intuitive. Is this like a common

500
00:25:28,000 --> 00:25:31,720
Speaker 4: thing across a range of either formal debt products or

501
00:25:31,800 --> 00:25:34,520
Speaker 4: quasi debt products, where they're all like sort of jocking

502
00:25:34,600 --> 00:25:37,600
Speaker 4: either to be in or out of the calculation basket.

503
00:25:38,000 --> 00:25:40,320
Speaker 5: I mean, I think it is. The medical debt is

504
00:25:40,320 --> 00:25:42,880
Speaker 5: a thing because see if he be made it a thing.

505
00:25:43,160 --> 00:25:46,439
Speaker 5: I don't think we have enough research on what is

506
00:25:46,560 --> 00:25:49,560
Speaker 5: or isn't on your credit report and why. But when

507
00:25:49,600 --> 00:25:54,359
Speaker 5: regulators start to pick those fights, yeah, you start to

508
00:25:54,359 --> 00:25:57,199
Speaker 5: get this jocking for what's on there. I think student

509
00:25:57,240 --> 00:26:00,560
Speaker 5: debt is another good example where there are legitimate quessions

510
00:26:00,600 --> 00:26:02,960
Speaker 5: of what ought to and not not be on there.

511
00:26:03,080 --> 00:26:05,959
Speaker 5: And some of it is about the predictive value, but

512
00:26:06,119 --> 00:26:08,800
Speaker 5: a lot of it is about putting debt on people's

513
00:26:08,840 --> 00:26:11,000
Speaker 5: credit reports that we know is inaccurate.

514
00:26:11,640 --> 00:26:14,520
Speaker 2: It is true that every few years we see like

515
00:26:14,800 --> 00:26:17,959
Speaker 2: these efforts from private companies I guess, to try to

516
00:26:18,040 --> 00:26:22,400
Speaker 2: augment credit scores with new technology. So I remember very

517
00:26:22,400 --> 00:26:25,879
Speaker 2: clearly being at a startup's offices in San Francisco in

518
00:26:26,040 --> 00:26:28,600
Speaker 2: like I guess it would have been twenty fifteen or something,

519
00:26:28,920 --> 00:26:31,439
Speaker 2: and they were walking me through the data that they

520
00:26:31,480 --> 00:26:34,960
Speaker 2: could use to make short term loans. And one of

521
00:26:35,000 --> 00:26:37,199
Speaker 2: the things they were talking about was like just the

522
00:26:37,240 --> 00:26:41,560
Speaker 2: basic cursor movement on the screen, like how fast you

523
00:26:41,640 --> 00:26:44,800
Speaker 2: click the button to get money, whether or not you know,

524
00:26:44,840 --> 00:26:47,840
Speaker 2: if it's a sliding bar that goes from like one

525
00:26:47,880 --> 00:26:50,719
Speaker 2: thousand dollars to ten thousand dollars, Like whether or not

526
00:26:50,800 --> 00:26:53,480
Speaker 2: you hesitate to go to ten thousand and then go

527
00:26:53,600 --> 00:26:56,480
Speaker 2: back and then go back to ten thousand again. It

528
00:26:56,600 --> 00:27:01,359
Speaker 2: seemed very dystopian to me, And I'm curious what those

529
00:27:01,440 --> 00:27:05,359
Speaker 2: efforts are like right now given the new emphasis on

530
00:27:05,400 --> 00:27:08,480
Speaker 2: AI and maybe you know, even more data that is

531
00:27:08,600 --> 00:27:11,840
Speaker 2: now available to private companies to look at to try

532
00:27:11,840 --> 00:27:14,040
Speaker 2: to judge whether people are credit worthy or not.

533
00:27:14,680 --> 00:27:17,840
Speaker 5: Yeah, if you listen to the earnings calls where CEOs

534
00:27:17,840 --> 00:27:21,320
Speaker 5: are talking to investors about their underwriting models for a

535
00:27:21,400 --> 00:27:24,480
Speaker 5: variety of different products, whether it's kind of traditional credit

536
00:27:24,480 --> 00:27:27,879
Speaker 5: cards or things like bnpl ai comes up a lot.

537
00:27:28,040 --> 00:27:29,960
Speaker 5: You know, everybody sort of wants to say that they've

538
00:27:30,000 --> 00:27:32,680
Speaker 5: got this proprietary am that's exactly it.

539
00:27:32,920 --> 00:27:35,800
Speaker 2: Yeah, they were saying the same thing ten years ago. Yeah.

540
00:27:35,920 --> 00:27:38,760
Speaker 5: Yeah, And I think there's a lot of things to

541
00:27:38,840 --> 00:27:41,400
Speaker 5: unpack there and to be concerned about so I think

542
00:27:41,400 --> 00:27:44,320
Speaker 5: we should You know, we know that AI models often

543
00:27:44,359 --> 00:27:48,080
Speaker 5: bake in racial biases, so we know that there's some

544
00:27:48,119 --> 00:27:51,520
Speaker 5: concern there about these models, you know. But I also

545
00:27:51,560 --> 00:27:54,560
Speaker 5: think to your point there, we're dragging in a lot

546
00:27:54,680 --> 00:27:58,320
Speaker 5: of extraneous data about people into the way we look

547
00:27:58,320 --> 00:28:00,840
Speaker 5: at their credit worthiness, some of which should be relevant,

548
00:28:00,840 --> 00:28:03,840
Speaker 5: some of which really should not. And what I think

549
00:28:03,920 --> 00:28:06,960
Speaker 5: is most important to think about there is that those

550
00:28:07,000 --> 00:28:11,000
Speaker 5: models are completely opaque, and company is often when they're

551
00:28:11,040 --> 00:28:14,320
Speaker 5: faced with regulatory scrutiny say kind of like the AI

552
00:28:14,440 --> 00:28:17,840
Speaker 5: did it right, as though the people at the companies

553
00:28:17,840 --> 00:28:21,280
Speaker 5: are not responsible for the outcomes of the technology. So

554
00:28:21,760 --> 00:28:24,160
Speaker 5: you know, under the Biden administration, the approach there had

555
00:28:24,200 --> 00:28:28,480
Speaker 5: really been to say you're responsible. It sounds so basic,

556
00:28:28,640 --> 00:28:32,240
Speaker 5: but the laws are the laws, whether it's about consumer

557
00:28:32,359 --> 00:28:36,520
Speaker 5: protection or about you know, basic discrimination among credit applicants,

558
00:28:36,760 --> 00:28:38,440
Speaker 5: the laws are the laws, and you're responsible for the

559
00:28:38,480 --> 00:28:41,479
Speaker 5: outcomes of those models. And I think that shift is

560
00:28:41,680 --> 00:28:44,280
Speaker 5: or that kind of approach is really really important here

561
00:28:44,800 --> 00:28:47,800
Speaker 5: to drive some accountability, because otherwise it does kind of

562
00:28:47,840 --> 00:28:49,200
Speaker 5: get out of hands.

563
00:28:49,760 --> 00:28:53,080
Speaker 2: So I mentioned in the intro that the parallel that

564
00:28:53,120 --> 00:28:55,160
Speaker 2: I reach for when thinking about buy Now, Pay Later

565
00:28:55,400 --> 00:28:58,640
Speaker 2: is sort of private credit. So this idea that you know,

566
00:28:59,000 --> 00:29:02,960
Speaker 2: it's a more opaque market and people are worried about

567
00:29:02,960 --> 00:29:06,280
Speaker 2: its size, and they're worried about transparency and hidden leverage

568
00:29:06,280 --> 00:29:09,280
Speaker 2: and all of that. But on the other hand, you know,

569
00:29:09,400 --> 00:29:14,240
Speaker 2: it is an extra credit bigot that companies can tap

570
00:29:14,320 --> 00:29:17,400
Speaker 2: in times of emergency, and we certainly saw that over

571
00:29:17,440 --> 00:29:20,520
Speaker 2: the past five years or so. Net net. When you

572
00:29:20,560 --> 00:29:22,800
Speaker 2: look at buy now, Pay Later, would you say the

573
00:29:22,800 --> 00:29:26,720
Speaker 2: effects are good, more good or more bad? This is

574
00:29:26,760 --> 00:29:27,479
Speaker 2: a tough one.

575
00:29:27,680 --> 00:29:30,720
Speaker 5: It is a really tough one because I don't I

576
00:29:30,760 --> 00:29:35,640
Speaker 5: said this earlier. I don't want to single out buy

577
00:29:35,760 --> 00:29:38,960
Speaker 5: Now Pay Later as the bad guy here. I think,

578
00:29:39,480 --> 00:29:41,760
Speaker 5: first of all, there are a lot of bad guys.

579
00:29:41,960 --> 00:29:44,479
Speaker 5: There are a lot of good guys too. But you know,

580
00:29:44,560 --> 00:29:46,640
Speaker 5: I think the thing to really focus on is, like

581
00:29:47,160 --> 00:29:50,680
Speaker 5: we're in what the Trump administration is describing as like

582
00:29:50,680 --> 00:29:53,400
Speaker 5: a relatively good economy, and we're sitting here talking about

583
00:29:53,400 --> 00:29:56,320
Speaker 5: like turning on this pigot for these kind of like

584
00:29:56,960 --> 00:29:59,440
Speaker 5: new and interesting types of debt to help people handle

585
00:29:59,600 --> 00:30:04,160
Speaker 5: really basic expenses. That seems like a problem we should

586
00:30:04,160 --> 00:30:07,120
Speaker 5: not be in an emergency situation and yet we are,

587
00:30:07,880 --> 00:30:12,600
Speaker 5: and so I'm concerned that that availability of these credit

588
00:30:12,640 --> 00:30:18,520
Speaker 5: products is obscuring, like the kind of growing emergency around

589
00:30:19,120 --> 00:30:22,040
Speaker 5: people's cost of living, which often you know in the

590
00:30:22,080 --> 00:30:26,240
Speaker 5: news is about their grocery prices, the eggs, their sneakers

591
00:30:26,240 --> 00:30:28,280
Speaker 5: for back to school, and those are really important, but

592
00:30:28,280 --> 00:30:32,280
Speaker 5: it's also their healthcare, the cost of college, being able

593
00:30:32,320 --> 00:30:36,440
Speaker 5: to retire. So families are feeling really squeezed, and you know,

594
00:30:36,480 --> 00:30:39,440
Speaker 5: to me, the most important thing there is like where

595
00:30:39,560 --> 00:30:42,560
Speaker 5: I think we're seeing the Trump administration sort of test

596
00:30:42,640 --> 00:30:45,240
Speaker 5: out this theory that they can keep the high level

597
00:30:45,360 --> 00:30:49,239
Speaker 5: metrics of whether the economy is on track, steady or

598
00:30:49,320 --> 00:30:53,240
Speaker 5: growing while letting somewhere around sixty percent of people have

599
00:30:53,320 --> 00:30:56,600
Speaker 5: these private financial crises. We're going to see that play

600
00:30:56,600 --> 00:30:58,560
Speaker 5: out over the next couple of years. And so that's

601
00:30:58,680 --> 00:31:01,280
Speaker 5: that's the way I think about these. It's not really

602
00:31:01,320 --> 00:31:04,000
Speaker 5: like is the individual one good, but it's like, do

603
00:31:04,080 --> 00:31:06,440
Speaker 5: you know what we're doing overall? And is anyone doing

604
00:31:06,440 --> 00:31:07,840
Speaker 5: the math? All right?

605
00:31:07,960 --> 00:31:10,000
Speaker 2: Julian Morgan, thank you so much for coming on all

606
00:31:10,000 --> 00:31:11,080
Speaker 2: thoughts really appreciated.

607
00:31:11,280 --> 00:31:12,440
Speaker 5: Thank you, thanks for having me.

608
00:31:26,080 --> 00:31:28,600
Speaker 2: Joe. I'm glad we finally did that episode We've been

609
00:31:28,600 --> 00:31:31,800
Speaker 2: talking about doing one on BNPL for a while. BNPL.

610
00:31:31,880 --> 00:31:34,280
Speaker 2: It always reminds me of like it sounds like BNP

611
00:31:34,400 --> 00:31:38,280
Speaker 2: Paribo's like Italian subsidiary or something. But there are a

612
00:31:38,320 --> 00:31:41,080
Speaker 2: bunch of things to pick out there. I mean, number one,

613
00:31:42,040 --> 00:31:44,840
Speaker 2: it's clearly a nuanced issue, yeah right, and like clearly,

614
00:31:44,960 --> 00:31:47,320
Speaker 2: as you laid out in the intro, it can have

615
00:31:47,360 --> 00:31:51,000
Speaker 2: a smoothing effect. The second thing that always seems to

616
00:31:51,040 --> 00:31:54,120
Speaker 2: come up is this idea that like, well, it's not

617
00:31:54,200 --> 00:31:57,440
Speaker 2: that big now, right, Like it's not big enough to

618
00:31:57,600 --> 00:32:03,040
Speaker 2: have an effect right now. But as Julie mentioned, we

619
00:32:03,160 --> 00:32:05,800
Speaker 2: are seeing, you know, volumes slowly pick up, and I

620
00:32:05,800 --> 00:32:08,640
Speaker 2: guess slowly, not that slowly, Yeah, yeah, right, And I

621
00:32:08,680 --> 00:32:12,400
Speaker 2: guess like I kind of wonder what happens like as

622
00:32:12,480 --> 00:32:16,080
Speaker 2: more and more of this activity actually stretches into services

623
00:32:16,120 --> 00:32:18,920
Speaker 2: forus goods, like that seems to be quite a big shift.

624
00:32:19,120 --> 00:32:22,520
Speaker 4: Well, I'd say, like, I think Julie's last point is

625
00:32:22,600 --> 00:32:25,000
Speaker 4: very relevant, which is like, you know, when we think

626
00:32:25,040 --> 00:32:29,000
Speaker 4: about household borrowing, and again probably you and I because

627
00:32:29,120 --> 00:32:31,920
Speaker 4: we're sort of children of or you know, grew up

628
00:32:31,960 --> 00:32:34,040
Speaker 4: during the financial crisis, you know, we sort of think

629
00:32:34,040 --> 00:32:34,520
Speaker 4: about like.

630
00:32:34,880 --> 00:32:36,960
Speaker 2: We weren't children, No, we're children, But.

631
00:32:37,320 --> 00:32:39,280
Speaker 4: You know what I'm saying, Yeah, you know, I think

632
00:32:39,360 --> 00:32:42,360
Speaker 4: you and I often think about credit from the effects

633
00:32:42,400 --> 00:32:44,760
Speaker 4: of defaults and so forth, and that could be an effect,

634
00:32:44,880 --> 00:32:47,680
Speaker 4: and that's not something to be dismissed. But the other

635
00:32:47,920 --> 00:32:50,680
Speaker 4: idea is like how much of like sort of like

636
00:32:50,800 --> 00:32:56,560
Speaker 4: aggregate economic conditions consumption is sort of debt financed and

637
00:32:56,880 --> 00:32:59,160
Speaker 4: in a way that we can't like fully measure, right,

638
00:32:59,200 --> 00:33:02,040
Speaker 4: because we comfort we can measure credit card dead and

639
00:33:02,080 --> 00:33:04,240
Speaker 4: so we have some sense of credit card dead is

640
00:33:04,280 --> 00:33:06,680
Speaker 4: the size of incomes or whatever, But if there's this

641
00:33:06,760 --> 00:33:10,000
Speaker 4: other rapidly growing source and it's pretty crucial for like

642
00:33:10,040 --> 00:33:13,560
Speaker 4: sort of keeping the headline figures of consumption or GDP,

643
00:33:14,160 --> 00:33:17,200
Speaker 4: you know, like my conclusion or like my sense it's like, yeah,

644
00:33:17,240 --> 00:33:19,520
Speaker 4: it's not that huge yet, but it's definitely not nothing,

645
00:33:19,640 --> 00:33:21,680
Speaker 4: and it's definitely not you know, it's bigger than.

646
00:33:21,640 --> 00:33:24,200
Speaker 2: A rounding air, right, And it certainly says something to

647
00:33:24,320 --> 00:33:28,120
Speaker 2: Julia's point about like the state of the average American

648
00:33:28,160 --> 00:33:32,880
Speaker 2: consumer who probably is struggling to pay something like dental fees.

649
00:33:33,040 --> 00:33:35,400
Speaker 4: Yeah, yeah, no, and like the fact in like, you know,

650
00:33:35,440 --> 00:33:38,000
Speaker 4: I think there's I thought it was interesting too that

651
00:33:38,080 --> 00:33:40,520
Speaker 4: like maybe both of us are least just me had

652
00:33:40,520 --> 00:33:41,480
Speaker 4: a certain.

653
00:33:41,400 --> 00:33:44,320
Speaker 3: Maybe already outdated view of what BNPL is.

654
00:33:44,480 --> 00:33:46,280
Speaker 2: Oh yeah, because like the idea of like I'm going

655
00:33:46,360 --> 00:33:48,560
Speaker 2: to polize that they have like actual apps, yees.

656
00:33:48,600 --> 00:33:50,560
Speaker 4: So the idea that like, oh, I'm going to buy

657
00:33:50,560 --> 00:33:53,320
Speaker 4: a television that's a thousand dollars and make it like

658
00:33:53,400 --> 00:33:56,560
Speaker 4: four two hundred and fifty dollars purchases, like that sounds great.

659
00:33:56,640 --> 00:33:59,280
Speaker 4: Should I should be doing this? But if that's just

660
00:33:59,320 --> 00:34:01,360
Speaker 4: sort of like on business model, but that there's like

661
00:34:01,400 --> 00:34:03,880
Speaker 4: this growing business model that looks more like installment loans

662
00:34:03,880 --> 00:34:06,160
Speaker 4: where there is a formal interest rate, but it also

663
00:34:06,200 --> 00:34:08,080
Speaker 4: sort of looks like the same thing and you also

664
00:34:08,120 --> 00:34:09,960
Speaker 4: get a card, et cetera. But it's just like a

665
00:34:09,960 --> 00:34:12,920
Speaker 4: slightly different structure, and formerly it isn't called debt or

666
00:34:13,000 --> 00:34:16,040
Speaker 4: isn't categorized or is it reported as debt than then

667
00:34:16,080 --> 00:34:18,200
Speaker 4: I think obviously we need to continue to get a

668
00:34:18,200 --> 00:34:19,799
Speaker 4: better handle on you know.

669
00:34:19,800 --> 00:34:20,719
Speaker 3: How big this is. Yeah.

670
00:34:20,719 --> 00:34:23,960
Speaker 2: Absolutely, Also Klarna says it's a bank now, which is

671
00:34:24,040 --> 00:34:24,840
Speaker 2: kind of confusing.

672
00:34:25,120 --> 00:34:29,200
Speaker 4: Yeah, it's like the hottest thing everyone wants everyone for

673
00:34:29,239 --> 00:34:30,440
Speaker 4: bank charters these days.

674
00:34:30,600 --> 00:34:33,040
Speaker 2: Oh yeah, that would be interesting to look at because

675
00:34:33,040 --> 00:34:35,560
Speaker 2: we went after the financial crisis, speaking of two thousand

676
00:34:35,600 --> 00:34:38,200
Speaker 2: and eight, we went years and years in brand.

677
00:34:38,320 --> 00:34:41,440
Speaker 4: Everyone, so every like every random brokerage or fine, we

678
00:34:41,480 --> 00:34:43,480
Speaker 4: got a bank charter. We acquired a company that has

679
00:34:43,480 --> 00:34:44,040
Speaker 4: a bank charter.

680
00:34:44,280 --> 00:34:46,719
Speaker 2: Time is a flat circle show? Yeah, all right, shall

681
00:34:46,719 --> 00:34:47,200
Speaker 2: we leave it there.

682
00:34:47,239 --> 00:34:47,919
Speaker 3: Let's leave it there.

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00:34:48,000 --> 00:34:50,320
Speaker 2: This has been another episode of the Odd Thoughts podcast.

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00:34:50,360 --> 00:34:53,200
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.

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00:34:53,360 --> 00:34:55,600
Speaker 3: And I'm Jill Wisenthal. You can follow me at the Stalwart.

686
00:34:55,800 --> 00:34:59,160
Speaker 4: Follow our guest Julie Morgan, She's at Jay Margetta, follow

687
00:34:59,200 --> 00:35:02,520
Speaker 4: our producers and Rodriguez at Carmen armand Dashel Bennett at

688
00:35:02,560 --> 00:35:05,399
Speaker 4: dashbod and Kale Brooks and Kale Brooks. For more Odd

689
00:35:05,440 --> 00:35:08,040
Speaker 4: Lots content, go to Bloomberg dot com slash odd Lots.

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00:35:08,160 --> 00:35:10,600
Speaker 4: We have a daily newsletter and all of our episodes,

691
00:35:10,840 --> 00:35:12,680
Speaker 4: and you can chat about all of these topics twenty

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00:35:12,719 --> 00:35:16,919
Speaker 4: four to seven in our discord Discord dot gg slash odlocks.

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00:35:17,000 --> 00:35:19,319
Speaker 2: And if you enjoy odd Lots, if you like it

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00:35:19,400 --> 00:35:22,040
Speaker 2: when we talk about the state of consumer debt, then

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00:35:22,080 --> 00:35:25,359
Speaker 2: please leave us a positive review on your favorite podcast platform,

696
00:35:25,440 --> 00:35:28,040
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697
00:35:28,080 --> 00:35:31,319
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00:36:01,320 --> 00:36:02,560
Speaker 2: Stood in a