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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 Odd Lots Podcast.

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Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 2: So, Tracy, one of the motifs I guess of some

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Speaker 2: of our recent convers chief is it's a good word,

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Speaker 2: isn't it? Thank you? One of the motifs of some

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Speaker 2: of our recent conversations. And also, I guess one of

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Speaker 2: the mega trends of our time is this idea of

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Speaker 2: just like scale as a competitive advantage, size returns to size,

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Speaker 2: returns to scale, the ability of someone to pick up

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Speaker 2: a phone and say I need a lot of money

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Speaker 2: right now, and the advantage that accrues to financial players.

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Speaker 2: It's we see it across a lot of sectors, but

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Speaker 2: it really it stands out a lot in our finance conversations.

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Speaker 3: It feels good to be big, yeah, business, right Like,

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Speaker 3: once you get big, you have all this competitive advantage

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Speaker 3: that allows you to get bigger. The place where this

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Speaker 3: is most noticeable, I would argue, and it's been this

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Speaker 3: way for a while, is the banking industry, right like, Yeah,

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Speaker 3: the long run trend among US banks is the big

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Speaker 3: get bigger, And we've all seen those charts of like

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Speaker 3: all the little banks that compose Bank of America or

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Speaker 3: a JP Morgan. I love those charts, but I do too,

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Speaker 3: and that's the direction the industry seems to be heading right, right.

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Speaker 2: It's so interesting to me to like to think about

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Speaker 2: like these analogs with tech, because it's the same in tech,

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Speaker 2: right if you look at big tech and we talk

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Speaker 2: about the mag seven, et cetera, they really outperformed middle

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Speaker 2: tech or small tech. It's truly incredible, and we have

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Speaker 2: intuitive reasons for understanding of that network effect locking and

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Speaker 2: so forth. And it's so fascinating that some of our

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Speaker 2: story that we tell in tech must be a little

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Speaker 2: bit insufficient because we see the same story replicated as

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Speaker 2: we're talking about with banks. And with banks it seems

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Speaker 2: to be a couple fold as like one is balance

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Speaker 2: sheet capacity, right and the ability to do big deals

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Speaker 2: of so forth, And then of course there's the other thing,

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Speaker 2: which is just like the sort of the menu of

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Speaker 2: services that you can offer a client if you're a

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Speaker 2: big bank.

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Speaker 3: Right, and also just the fact that you have well

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Speaker 3: balance sheet, You have a lot of money, so if

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Speaker 3: a juicy deal comes up, you can deploy it quite

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Speaker 3: quickly and you get like first DIBs on issuance, which

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Speaker 3: has been good in the recent credit market.

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Speaker 2: So we haven't done like a ton of pure banking episodes,

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Speaker 2: although we've done some, but you know, we did the most,

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Speaker 2: probably right in the wake of a Silicon Valley bank obviously,

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Speaker 2: and we talked about like the challenges of some of

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Speaker 2: the community banks or the sort of smaller regional banks,

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Speaker 2: et cetera. And then we know, like the huge scale

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Speaker 2: like the JP Morgans of the world, but also like

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Speaker 2: what about the banks like just sort of like sub

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Speaker 2: JP Morgan scale, not necessarily.

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Speaker 3: Super regionals, I believe they're called.

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Speaker 2: Super regionals, interesting perspectives of their own and so and

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Speaker 2: I've always said I love talking to bankers, people who

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Speaker 2: unders and how banking works, just like I think they're

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Speaker 2: usually some of are our most interesting guests.

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Speaker 3: Have you looked at the KBW Bank Index, by the way.

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Speaker 2: Not today, actually not in a few days or maybe

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Speaker 2: not in a few days. Yeah, what has it been doing?

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Speaker 3: It's up quite a lot. In fact, I think over

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Speaker 3: the past twelve months it's outperformed the S and P five hundred.

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Speaker 3: So that kind of tells you how people feel about

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Speaker 3: banks and bank regulation at the mater.

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Speaker 2: Absolutely right. One of the things when Trump wan is

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Speaker 2: the sort of like the deregulation trade, and that was

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Speaker 2: one element and arguably, I would say in finance, by

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Speaker 2: and large, though ten percent credit card caps does not

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Speaker 2: stream deregulation, I would say that's one of the areas

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Speaker 2: in which the expectations of Wall Street have actually been

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Speaker 2: met by and large by this administration. Anyway, I'm very

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Speaker 2: excited to say we really do have the perfect guests

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Speaker 2: to talk about the banking world. In twenty twenty six.

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Speaker 2: We're going to be speaking with Bill Demchak, CEO of

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Speaker 2: P and C Financial, which is the sixth biggest bank

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Speaker 2: in the country. I guess a super regional. So Bill,

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Speaker 2: thank you so much for coming on out lots.

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Speaker 4: It's it's great to be here. But I'm going to correct.

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Speaker 2: We're a national bank, national bank.

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Speaker 1: Yeah.

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Speaker 2: Why did I say super regional? Were you once there?

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Speaker 3: Yeah?

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Speaker 4: Because we have the megabanks and people don't know what

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Speaker 4: to call somebody like us, But yeah, we're in.

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Speaker 3: I always thought super regional was just like that category

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Speaker 3: below the mega.

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Speaker 4: But I mean a regional bank is a bank that

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Speaker 4: operates in a region. Okay, right, we're across the country

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Speaker 4: and the largest msas. We're in every state. So we're

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Speaker 4: just you know, one sixth the size of a JP.

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Speaker 2: Morgan, got it. But we're still a national bank actullent. Well,

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Speaker 2: since we're just in the since we're in the correcting

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Speaker 2: the record phase of the conversation. I'm certainly familiar with

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Speaker 2: the P and C brand. You see it on things elsewhere,

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Speaker 2: probably like a stadium somewhere or is there a stadium somewhere?

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Speaker 4: Uh, we have a P and C park for the Pirates.

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Speaker 2: PNC Park.

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Speaker 1: So there you go.

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Speaker 2: But what do you just give us the big over, Like,

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Speaker 2: what is PNC financial?

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Speaker 4: We are a traditional bank. We used to call it

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Speaker 4: main street bank. Maybe that fits, maybe it doesn't, but

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Speaker 4: you know, we help people save, We lend money, we

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Speaker 4: do investments, we move money through payments. We're a bank

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Speaker 4: that aspires to be you know, coast to coasts with

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Speaker 4: a retail presence. As retail share consolidates in the US,

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Speaker 4: you know, our sweet spot is larger middle market, smaller,

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Speaker 4: large corporate. But of course we do commercial and business banking.

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Speaker 4: We're active in the capital markets across basically every product,

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Speaker 4: but new equity issuance, but more of a traditional bank.

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Speaker 4: So when we talk about sk you were talking about

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Speaker 4: scaling your intro think at JP Morgan, they've got to

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Speaker 4: be in fifty countries, you know, they're in multiple lines

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Speaker 4: of business beyond traditional banking. We have scale and what

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Speaker 4: we want to do. So there's scale and the things

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Speaker 4: you choose to attack versus scale just measuring balance sheet size.

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Speaker 1: Got it.

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Speaker 3: You have been growing though you made an acquisition recently.

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Speaker 3: First Bank. Is the ambition to be, you know, as

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Speaker 3: big as some of the jesups, like the national giants.

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Speaker 4: Let's say, the ambition isn't about size, the ambition is

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Speaker 4: about being relevant. So one of the things going on

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Speaker 4: backdrop to the US market in particular is there has

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Speaker 4: been consolidation and retail share and banking, you know, ever

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Speaker 4: since they got rid of interstate banking laws. That continues

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Speaker 4: a pace with the megabanks largely winning at the expense

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Speaker 4: of the very small banks. And so at some point

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Speaker 4: in the not too distant future, I suspect that there's

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Speaker 4: going to be five or six players who control retail

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Speaker 4: in the US.

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Speaker 3: The Canada model.

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Speaker 4: Yeah, and our aspiration and we're on pace to do it,

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Speaker 4: and we think we can do it. Is to be

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Speaker 4: one of those five or six banks. What size that

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Speaker 4: leads to? I don't care. You just need to be

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Speaker 4: relevant to consumers everywhere in the country and you shouldn't

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Speaker 4: be disadvantaged because you don't have presence in a particular market.

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Speaker 2: Well, maybe we should just answer the really simple question.

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Speaker 2: It's like, how would you articulate why the really big

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Speaker 2: banks continue to win share of the retail market. I

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Speaker 2: could come up with some answer, but what's the real

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Speaker 2: How would you describe it?

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Speaker 4: It's ubiquitous market presence. You know, once you get over

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Speaker 4: seven percent branch density in a particular market, you tend

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Speaker 4: to outperform under the assumption you have an okay reputation

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Speaker 4: in decent products. You know that you don't have to

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Speaker 4: have the best, but you have to have the menu

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Speaker 4: of all the digital applications, digital signature, branches in the

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Speaker 4: right place, you know, electronic transfer, all the things you

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Speaker 4: come to expect from the bank. You guys probably bank it.

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Speaker 4: You get there and you grow. And if you think

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Speaker 4: about that definition, there's only three or four banks today,

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Speaker 4: actually only two that fit that definition and be of

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Speaker 4: a now Wells, you know, has the potential, you know,

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Speaker 4: now that they've gotten rid of the asset cap and

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Speaker 4: so forth tinue to grow. And then you have some

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Speaker 4: aspirins who'd like to get in there. We're clearly one

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Speaker 4: of those and one of the one of the people

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Speaker 4: who's been after this game, probably longer than most.

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Speaker 2: Just sorry, what was the metric that you sayd seven

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Speaker 2: percent branch density?

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Speaker 1: What is that?

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Speaker 4: Yeah? So, by the way, we use seven percent. Other

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Speaker 4: banks will talk about eight. But at somewhere around there,

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Speaker 4: you get to a place where if you have presence

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Speaker 4: in a market seven or eight percent of the share

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Speaker 4: of just physical presence with decent products, you control a

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Speaker 4: disproportionate share of the actual deposits and economics coming out

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Speaker 4: of that market.

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Speaker 3: So I'm actually surprised to hear that because the story,

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Speaker 3: you know, that's dominated since the financial crisis has basically

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Speaker 3: been that physical branches don't matter that much.

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Speaker 4: Yeah, so you've seen across the US, you know, netclosures

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Speaker 4: of branches is off the charts relative to new builds.

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Speaker 4: But what you see behind the scenes is somebody like

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Speaker 4: P and C. Where we are if you think about

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Speaker 4: our very dense markets where we've been for one hundred

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Speaker 4: and sixty five years. We're thinning those branches because you

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Speaker 4: don't need as much. But we're building aggressively in Houston

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Speaker 4: and Dallas and Miami. We were building in Denver till

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Speaker 4: we bought First Bank. So going to all these growing

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Speaker 4: markets where the population's moving.

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Speaker 2: Man, it just occurred to me, I already after we

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Speaker 2: do this episode, I want to follow up and like

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Speaker 2: do an interview with like whoever your main person doing

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Speaker 2: site selection is, because we've done episodes on like oh

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Speaker 2: we have killed drive through site selection, grocery store site selection.

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Speaker 4: We have a great team. We're building by the way,

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Speaker 4: you know, we've announced we're building three hundred branches in

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Speaker 4: these new markets and as an aside, once we knock

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Speaker 4: a hundred off, well at another hundred, so it's going

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Speaker 4: to keep going. We're probably one thousand short of where

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Speaker 4: we ultimately want to be, but we don't have to

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Speaker 4: be there tomorrow. But just building one hundred branches a

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Speaker 4: year is a massive exercise on site selection.

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Speaker 3: You're a construction company.

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Speaker 4: Yes, I mean exactly right, So you need to pick

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Speaker 4: the right location. You need to choose whether you're going

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Speaker 4: to do a drive through, or you're going to be

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Speaker 4: at the end of some shopping something where you're kind

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Speaker 4: of an end add on to a new development. The

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Speaker 4: physical You know, we have preset designs, so we know

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Speaker 4: what we're building, but you still need a contractor you

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Speaker 4: need somebody running at the physical plant that you move

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Speaker 4: into the branch.

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Speaker 2: We have pods.

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Speaker 4: Now, new branch opens, truck with a pod shows up

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Speaker 4: and everything that that branch needs to run, from computers

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Speaker 4: to paper to pens, is in a pod. Just deliver

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Speaker 4: it right into the branch, which is what we're going

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Speaker 4: to do when we do the first bank conversion.

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Speaker 3: It's in a side that's really interesting. Yeah, let's talk

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Speaker 3: about bank regulation because I think this is yeah, very fun.

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Speaker 3: This is part of the consolidation story, I think is

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Speaker 3: that even though post financial crisis there was more focus

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Speaker 3: on the significant banks, the gesips, at the same time

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Speaker 3: those gesips seem to have benefited from some of the regulation.

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Speaker 3: Is that how you would characterize it.

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Speaker 4: I don't know that they know they benefited from regulation

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Speaker 4: and to carry extra capital. They clearly, you know, in

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Speaker 4: times of crisis, have done better than the broad swath

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Speaker 4: of smaller banks. As we saw with Silicon Valley and

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Speaker 4: some others, but they were sort of walled off from competitors. So,

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Speaker 4: you know, I've talked for years about just the need

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Speaker 4: to be able to compete. Some of that competition comes

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Speaker 4: from M and A. Some of it just needs to

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Speaker 4: know the ability to do certain activities, and in theory,

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Speaker 4: now we're in an environment where it's easier to do that,

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Speaker 4: and I think practically that's probably true. Although I would

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Speaker 4: tell you I think we could have done large deals

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Speaker 4: had we chosen to back during the Biden administration. I

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Speaker 4: think people tend like one of the sound bites that

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Speaker 4: comes out of regulators is good deals are okay. Bad

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Speaker 4: deals aren't, but they won't tell you what a bad

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Speaker 4: deal is. But a good deal is typically when you

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Speaker 4: have one good bank, somebody's in charge, you have technology

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Speaker 4: that can actually absorb the other bank, and you can

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Speaker 4: protect consumers and so and so forth. Those could have

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Speaker 4: gotten done in the last administration.

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Speaker 2: That's interesting. Well, let's talk about more about your philosophy

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Speaker 2: of M and A, because so your stock has done

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Speaker 2: very well. That it is for a while, especially in

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Speaker 2: the last year up until recently, had been underperforming. Some

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Speaker 2: of the other banks and your quote is like everyone

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Speaker 2: thinks we're going to do something non exact quote but

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Speaker 2: like something, why does.

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Speaker 4: Ever the whole world's bad and I'm stupid?

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Speaker 2: Yeah right, but so like, but then how do you grow?

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Speaker 2: So okay, all the market they want to pay off

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Speaker 2: the business stock because they think that you're going to.

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Speaker 4: This isn't this isn't this isn't complex. We'll just start

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Speaker 4: with the basic.

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Speaker 2: Ye, all right.

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Speaker 4: Last year we outperformed I think everybody, including JP Morgan

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Speaker 4: and top line revenue growth and EPs, PP and R

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Speaker 4: growth with PP and R sorry to go, no pre

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Speaker 4: provision net revenue, okay, fantastic. Here we set records in

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Speaker 4: new client acquisition, revenue growth, expense controlled, great, new tech

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Speaker 4: role out on stuff, and we underperformed. Part of the

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Speaker 4: backdrop of that was the megabanks outperformed as people correctly

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Speaker 4: thought regulation was going to drop and they could return

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Speaker 4: more capital than they historically had. So take that bucket.

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Speaker 4: They're doing great because they have all this capital and

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Speaker 4: they're free to do whatever they want to. Smaller banks

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Speaker 4: went up in value because you know, people like Bill

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Speaker 4: Demcheck with a big mouth, said there's going to be consolidation.

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Speaker 4: So everybody's going to buy them, so they go up

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Speaker 4: in value. And because we are a likely acquirer, given

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Speaker 4: our historical performance and being good at integration, we get crushed.

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Speaker 2: Did you get penalized from the perception of a more

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Speaker 2: liberal M and A.

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Speaker 4: Absolutely, But it was part of It's my fault. And

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Speaker 4: you know, because I talk, you know, I talk about

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Speaker 4: the need for skill, I talk about the consolidation that's

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Speaker 4: happening in retail. I talk about why this makes sense,

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Speaker 4: and people make this leap and therefore and the sentence

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Speaker 4: with at any cost, and that's a terrible assumption. Not

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Speaker 4: at any cost. We can do this organically. It just

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Speaker 4: takes longer. And you know, today with everything flying in

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Speaker 4: value on us underperforming, it's extremely unlikely we find something

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Speaker 4: that makes economic sense for us. Plus the fact, just

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Speaker 4: as inside the environment is so good right now, nobody,

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Speaker 4: none of the reasonable size banks want to sell. This

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Speaker 4: is like a This is like a sunny day in August. Man,

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Speaker 4: You've got you know, rates are going the right way,

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Speaker 4: the curve is steepinging out, credits, great regulations coming down.

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Speaker 4: Nobody sells their banking.

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Speaker 3: Do you wake up every morning and check the P

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Speaker 3: and C? Share price. I always wonder about this CEOs

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Speaker 3: of public companies. You're graded instantaneously by the market, yes.

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Speaker 4: But the market at times is an unfair greater, so

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Speaker 4: I don't pay attention to it. You know, when we

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Speaker 4: I look at metrics all the time. Are we winning clients?

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Speaker 4: Are we automating things and getting more efficient? Are we

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Speaker 4: is our marketing campaign reaching people? What's our traction level?

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Speaker 4: And digital? You know, all these things that ultimately will

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Speaker 4: lead to a share price performance, but not in the day.

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Speaker 2: Well, let's talk then a little bit about Okay, so

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Speaker 2: a lot of banks are doing well. They don't they're

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Speaker 2: not They don't want to sell. You're not a stupid guy,

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Speaker 2: so you don't want to make like a dumb deal.

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Speaker 2: So there's not gonna be deals. Okay, So yet you

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Speaker 2: have this aspiration to one of the really big banks,

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Speaker 2: and so okay, that leaves organic growth.

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Speaker 4: What is the But it also so I mean in perspective, right,

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Speaker 4: we're in this the weird window in regulatory change. Isn't

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Speaker 4: the Trump administration. It was the Biden administration. Anytime before

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Speaker 4: Biden and now after Biden, deals get done all the time.

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Speaker 4: We're one hundred and sixty five years in business. You

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Speaker 4: know the little chart you talked about with all the banks. Yeah,

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Speaker 4: we've got to have one hundred and fifty banks under

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Speaker 4: us through time and that'll continue. But it doesn't have

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Speaker 4: to happen now, right. The weird thing about now is

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Speaker 4: just we ended the four years of strange period under Biden.

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Speaker 2: Got it.

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Speaker 4: Now we're just back to the old game. This will

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Speaker 4: go for you know, go on and on and on.

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Speaker 2: So what is the essence of the organic growth strategy

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Speaker 2: to different if it's yeah to grow?

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Speaker 4: So the so far I've been talking about retail, let

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Speaker 4: me talk about the reason for retail. So ultimate reason

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Speaker 4: for retail is retail deposits. Retail deposits are low costs,

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Speaker 4: they're sticky, they allow you to support your corporate community

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Speaker 4: and consumers loan. We've been able to win in what

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Speaker 4: we call our CNIB business of corporate institutional bank business,

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Speaker 4: gain share every year for the last I don't know,

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Speaker 4: fifteen years, and what is a very fragmented market. You know,

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Speaker 4: we talk about the megabanks gathering retail share, but in

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Speaker 4: corporate commercial nobody has any real share. It's shotgun everywhere,

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Speaker 4: and we win in that space and we do it

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Speaker 4: by you know, going to market locally. We organize. This

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Speaker 4: will all sound kind of silly, but we don't have

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Speaker 4: p and ls by product. We have p and ls

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Speaker 4: by client. We go into the market, we have a

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Speaker 4: regional president, we have product expertise, and we choose the

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Speaker 4: most important and impressive clients to cover in that market,

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Speaker 4: not anybody else. We don't do business with somebody necessarily

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Speaker 4: who walks through the door, because that's all that we'll

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Speaker 4: do business with it. We'll call on the same grade

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Speaker 4: a client you know that you know you want to cover.

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Speaker 4: If you're in MYAMI or iar in Texas, we'll call

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Speaker 4: on them for ten years until they call us back. Patients, persistence, consistency,

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Speaker 4: good product ideas are real low banker turnover, and we

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Speaker 4: win and you just you know, instant repeat, go into

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Speaker 4: new markets ad people keep growing just on acquisitions.

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Speaker 3: I'm really curious how difficult it is to integrate another

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Speaker 3: bank with your company, Because you just said you're good

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Speaker 3: at integration. What does that actually entail.

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Speaker 4: That's a great question. So integration, there's a mechanical component,

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Speaker 4: and then of course there's a cultural component. Mechanical component

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Speaker 4: should otherwise be easy, but has proven difficult for some

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Speaker 4: people over time. The mechanical component, let's just talk about

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Speaker 4: First Bank. For us, we simply take all of their

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Speaker 4: data records, so you know, record field data items for

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Speaker 4: one of their checking accounts, and we import that data,

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Speaker 4: but we don't import that application. All that data come

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Speaker 4: into P and C and then over a weekend, we

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Speaker 4: simply open up a million new accounts on our existing

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Speaker 4: products and services. Sounds pretty simple. If you can move

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Speaker 4: the data, you can just effectively open new accounts. My

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Speaker 4: team will scream, it's not that easy, but that's basically

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Speaker 4: what you do. Yeah, Now, all of a sudden, Okay,

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Speaker 4: now we have all these customers with new accounts. How

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Speaker 4: do they log in? How do they learn? Like our

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Speaker 4: online is different than somebody else's online. So now you're

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Speaker 4: getting into training modules, communications with clients, training their branches

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Speaker 4: with First Bank, tremendous frontline set of people, incredible bank

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Speaker 4: and history. We're keeping all their client facing employees. Now

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Speaker 4: they all of a sudden have to know the difference

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Speaker 4: between P and C products and their old products. So

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Speaker 4: how do we do that? That's what integration ultimately means.

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Speaker 4: There's a mechanical piece of it. Get the systems to work,

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Speaker 4: so you turn it on, the lights go on. There's

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Speaker 4: delivering pods to the branches so they have all the

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Speaker 4: P and C equipment and the signage and all that

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Speaker 4: other stuff. There's a lot of time with people. We

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Speaker 4: send in branch buddies. So we'll take branch managers from

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Speaker 4: all around the country and go have them spend you know,

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Speaker 4: a couple of weeks in each of you know, sitting

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Speaker 4: with the branches at first bank, not just helping people, yeah,

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Speaker 4: and the learning and it's I mean banking, you know.

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Speaker 4: In some ways it's simple. In some ways it's wildly complex.

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Speaker 4: An employee in a branch. The problems that you might

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Speaker 4: walk in, right, you say, oh, I rarely go into

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Speaker 4: a branch or I don't think about it. But when

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Speaker 4: you go into a branch, something's really busted. So the employees.

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Speaker 4: So the employees, right, they need to know how to

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Speaker 4: fix it. And that's when we say we're good at integrations.

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Speaker 4: It's because we focus on making sure that our new

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Speaker 4: teammates know how to do their job and can help

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Speaker 4: their clients. Because they have the same clients they've had forever.

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Speaker 4: They don't want to look bad in their new role.

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Speaker 2: Can you actually just talk more about that? So it's like, okay,

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Speaker 2: they need to know how to like sell or be

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Speaker 2: familiar with it P and C product. Now, like talk

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Speaker 2: a little bit more about like what that difference is,

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Speaker 2: what actually changes functionally? Why wouldn't be what it is

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Speaker 2: about either culture or products such that it's not just

401
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Speaker 2: replacing the logo and the pens and the.

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Speaker 4: Well everything works at the margin is slightly different. I

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Speaker 4: mean checking accounts a checking account, but imagine you're you're

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Speaker 4: in a branch or you're in the care center. You

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Speaker 4: have a problem. You made your car payment, you send

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Speaker 4: it through snail. It was timestamped at the right time,

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Speaker 4: but it went through snail mail, got lost, you got

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Speaker 4: penalized to fee, and you go into the branch and

409
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Speaker 4: you say, you know, it's really not fair I sent

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Speaker 4: this and you're you're hitting me with the fee and

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Speaker 4: the branch person what do they do?

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Speaker 1: Right?

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Speaker 4: It's there's there's some answer to that. What's our procedure? Well,

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Speaker 4: you know, now with AI, you can just go into

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Speaker 4: a large language model that is built off of all

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Speaker 4: of our customer service protocols and say what do I

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Speaker 4: do this just happened. They'll tell you, you know, credit the

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Speaker 4: customer the fee and send a note to the you know,

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Speaker 4: whatever the answer is for that particular problem. But the

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Speaker 4: person sitting there needs to know, like which system do

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Speaker 4: I query? What's my browser? What am I? Who do

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Speaker 4: I talk to?

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Speaker 3: You know?

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Speaker 4: If I'm going to make an exception, do I go

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Speaker 4: to the branch manager or can we do that here?

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Speaker 4: Are I going to call somebody in some strange room

427
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Speaker 4: in a back office in Philadelphia? All of that with

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Speaker 4: us because we automate, so much of it is now

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Speaker 4: sind of query and self learning in the branch, and

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Speaker 4: then importantly more and more with our customer self curing,

431
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Speaker 4: you know, they got locked out, you know, just just

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Speaker 4: be able to fix the problem yourself.

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Speaker 3: One thing I've wondered for a few years, does local

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Speaker 3: expertise matter anymore when it comes to actual underwriting, either

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00:22:38,440 --> 00:22:42,480
Speaker 3: in retail or commercial, Because I think most of America

436
00:22:42,560 --> 00:22:45,760
Speaker 3: used to have this very romanticized notion of the banks,

437
00:22:45,840 --> 00:22:47,480
Speaker 3: the sort of it's a wonderful.

438
00:22:47,200 --> 00:22:49,280
Speaker 2: Life model where your.

439
00:22:48,800 --> 00:22:52,399
Speaker 3: Branch manager knows you and knows your entire life and

440
00:22:52,640 --> 00:22:56,080
Speaker 3: business and all of that. My sense is that doesn't

441
00:22:56,119 --> 00:22:59,320
Speaker 3: exist so much anymore. But tell me if I'm wrong,

442
00:23:00,680 --> 00:23:01,160
Speaker 3: you're wrong.

443
00:23:01,400 --> 00:23:07,119
Speaker 4: Okay, it's harder to accomplish when you become a larger bank.

444
00:23:07,400 --> 00:23:10,960
Speaker 4: But you know how is credit of all through time consumers?

445
00:23:11,119 --> 00:23:13,600
Speaker 4: Consumers now if they're an existing client of you, or

446
00:23:13,720 --> 00:23:16,600
Speaker 4: scored based on their cash flows that you see so

447
00:23:16,720 --> 00:23:18,840
Speaker 4: not a FICO score. You can actually see when did

448
00:23:18,840 --> 00:23:22,560
Speaker 4: somebody pay the rent or they're spending patterns predictable. You

449
00:23:22,600 --> 00:23:24,359
Speaker 4: can build models to say, you know what, this person

450
00:23:24,480 --> 00:23:26,520
Speaker 4: is a really diligent could credit even though it might

451
00:23:26,520 --> 00:23:30,159
Speaker 4: should not show up in a FICO. Local market business,

452
00:23:30,160 --> 00:23:32,800
Speaker 4: you're going to lend to this small developer, You're going

453
00:23:32,840 --> 00:23:36,200
Speaker 4: to lend to this store on the corner. Branches really matter,

454
00:23:36,280 --> 00:23:39,680
Speaker 4: and local bankers matter for small businesses because they see

455
00:23:39,680 --> 00:23:42,760
Speaker 4: where the foot traffic is. They know the reputation of

456
00:23:42,800 --> 00:23:45,520
Speaker 4: the person who's running that business to people. You know,

457
00:23:45,560 --> 00:23:50,720
Speaker 4: are they community influencer in a good way where there

458
00:23:50,760 --> 00:23:52,880
Speaker 4: you know people want to support the moment stuff still

459
00:23:52,880 --> 00:23:56,280
Speaker 4: matters and all that stuff. The more you can give

460
00:23:56,480 --> 00:24:01,600
Speaker 4: somebody voice locally in the decision, I always kind of

461
00:24:01,720 --> 00:24:03,840
Speaker 4: call it putting a thumb on the scale, a little

462
00:24:03,840 --> 00:24:06,199
Speaker 4: bit on a yes or no, that's otherwise mechanical. The

463
00:24:06,280 --> 00:24:07,879
Speaker 4: better your credit underwriting is.

464
00:24:08,680 --> 00:24:10,840
Speaker 2: Well, just as we're talking about consumer credit, what happens

465
00:24:10,840 --> 00:24:13,480
Speaker 2: to America or the industry if there's a ten percent

466
00:24:13,600 --> 00:24:17,600
Speaker 2: cap on credit card rates as President Trump has proposed.

467
00:24:18,040 --> 00:24:20,760
Speaker 4: See, the only thing I know for sure if that

468
00:24:20,880 --> 00:24:24,720
Speaker 4: happens is that all the credit card businesses in the

469
00:24:24,760 --> 00:24:28,040
Speaker 4: country lose money if we run exactly the way they

470
00:24:28,080 --> 00:24:31,880
Speaker 4: are today. So we have credit card business I get

471
00:24:31,960 --> 00:24:35,200
Speaker 4: swipe fees. I give most of those away with rewards.

472
00:24:35,359 --> 00:24:39,080
Speaker 4: I earn credit when somebody is revolving a balance. I

473
00:24:39,200 --> 00:24:41,240
Speaker 4: lend credit when somebody pays it at the end of

474
00:24:41,280 --> 00:24:43,399
Speaker 4: the month. Right, I'm fronting that money for you if

475
00:24:43,480 --> 00:24:45,919
Speaker 4: you clear your account every month. But hopefully I'm getting

476
00:24:45,960 --> 00:24:48,399
Speaker 4: some sweite fees to make that good. After all that

477
00:24:48,600 --> 00:24:51,280
Speaker 4: charge interest rates, you have losses, and you get a margin.

478
00:24:51,520 --> 00:24:54,959
Speaker 4: Maybe make four percent and assume interest I'm making up

479
00:24:55,000 --> 00:24:57,760
Speaker 4: numbers here, but assume maybe the average rate is eighteen percent.

480
00:24:57,840 --> 00:24:59,399
Speaker 4: I make four at the bottom and not going to

481
00:24:59,440 --> 00:25:02,399
Speaker 4: take my eight and I'm going to drop it to ten. Oh,

482
00:25:02,440 --> 00:25:05,800
Speaker 4: I just made minus four, and so all that math

483
00:25:05,920 --> 00:25:07,640
Speaker 4: is going to come out, which is why I don't

484
00:25:07,640 --> 00:25:10,080
Speaker 4: think this goes like there's almost no way this can happen.

485
00:25:10,080 --> 00:25:14,000
Speaker 4: You'll just simply shut down consumer credit in the US.

486
00:25:14,040 --> 00:25:16,800
Speaker 4: They can't force. They could say that the cap is

487
00:25:16,800 --> 00:25:20,200
Speaker 4: ten percent, and I'll say, fine, I'm out, Like, why

488
00:25:20,200 --> 00:25:21,640
Speaker 4: would I do it if I'm going to lose four

489
00:25:21,680 --> 00:25:24,200
Speaker 4: percent a year on that. Something's going to change. Fees

490
00:25:24,240 --> 00:25:25,639
Speaker 4: are going to go up, lines are going to get

491
00:25:25,680 --> 00:25:28,919
Speaker 4: cut or rewards will go away, you know, and something

492
00:25:28,960 --> 00:25:31,720
Speaker 4: will emerge out of the dust. But it's not going

493
00:25:31,760 --> 00:25:33,280
Speaker 4: to look at all like it does today.

494
00:25:33,640 --> 00:25:36,080
Speaker 3: What do you think the genesis of the proposal or

495
00:25:36,119 --> 00:25:39,520
Speaker 3: the rationale behind it actually is. If you know, everyone

496
00:25:39,560 --> 00:25:42,919
Speaker 3: in the industry says this basically ends credit cards.

497
00:25:43,080 --> 00:25:48,360
Speaker 4: Yeah, I think because, first of all, the emotional appeal

498
00:25:48,400 --> 00:25:50,760
Speaker 4: of it. I mean, it sounds absurd that you're charging

499
00:25:50,800 --> 00:25:54,600
Speaker 4: somebody eighteen percent for evolving credit. I think that sounds high.

500
00:25:54,880 --> 00:25:56,439
Speaker 4: That happens to be the rate that you need to

501
00:25:56,520 --> 00:25:58,920
Speaker 4: charge to make money, but it sounds which is why

502
00:25:58,960 --> 00:26:02,239
Speaker 4: I never revolve a balance. I paid off everyone. So

503
00:26:02,280 --> 00:26:04,120
Speaker 4: there's this emotional appeal. Jeez, you ought to be able

504
00:26:04,119 --> 00:26:06,119
Speaker 4: to lend, like I'll lend money to you at ten percent.

505
00:26:06,119 --> 00:26:08,439
Speaker 4: I'll leand money to you at ten percent, yes, right,

506
00:26:08,480 --> 00:26:11,000
Speaker 4: but I can't necessarily figure out who you are in

507
00:26:11,040 --> 00:26:13,360
Speaker 4: a sea of people. Right, there's a bunch of people.

508
00:26:13,400 --> 00:26:15,520
Speaker 4: I'll lend money at four percent because I'll never to pall.

509
00:26:16,000 --> 00:26:19,080
Speaker 4: You know, that's the issue. And you try to get

510
00:26:19,080 --> 00:26:22,600
Speaker 4: to the broader population and extend credit to the consumer

511
00:26:22,680 --> 00:26:26,640
Speaker 4: that drives our economy. Science isn't so exact to say

512
00:26:26,680 --> 00:26:29,320
Speaker 4: you get nine and you're twenty two. I don't know

513
00:26:29,320 --> 00:26:29,680
Speaker 4: about you.

514
00:26:30,280 --> 00:26:32,960
Speaker 2: Well, actually I want to press this further because intuitively,

515
00:26:34,320 --> 00:26:35,840
Speaker 2: I would think you'd be able to do that. You

516
00:26:36,040 --> 00:26:40,359
Speaker 2: just mentioned the fact that you have granular access to

517
00:26:40,680 --> 00:26:44,199
Speaker 2: propensity to payback that is not even captured by Yes, like,

518
00:26:44,400 --> 00:26:48,960
Speaker 2: you have incredible amounts of data and predictive capacity and

519
00:26:49,040 --> 00:26:52,200
Speaker 2: the difference between whether me or Tracy are going to evolve.

520
00:26:52,520 --> 00:26:56,200
Speaker 2: So when someone says eighteen percent seems high, Yeah, are

521
00:26:56,200 --> 00:26:58,639
Speaker 2: we supposed to just like believe? Oh, just trust me,

522
00:26:58,680 --> 00:27:00,320
Speaker 2: it's high. This is what I need to try arge

523
00:27:00,359 --> 00:27:02,159
Speaker 2: in order to make money in this business. I mean

524
00:27:02,200 --> 00:27:04,480
Speaker 2: I could. I'm like, how would you?

525
00:27:04,480 --> 00:27:05,720
Speaker 4: But the math is the math?

526
00:27:05,880 --> 00:27:07,200
Speaker 2: Okay, the end result? Right?

527
00:27:07,240 --> 00:27:10,720
Speaker 4: You say when you run your models and you look

528
00:27:10,760 --> 00:27:13,520
Speaker 4: at you know, propensity to pay you back. You basically

529
00:27:14,880 --> 00:27:16,879
Speaker 4: you know whether you're looking at fyco or anything. If

530
00:27:16,920 --> 00:27:19,240
Speaker 4: Fyco is only as good as whether that person has

531
00:27:19,240 --> 00:27:23,439
Speaker 4: a job next week, right they they You know, you

532
00:27:23,480 --> 00:27:26,080
Speaker 4: can look at predictability and yes, this person has been

533
00:27:26,119 --> 00:27:29,679
Speaker 4: paying the rent. They have a steady expenditure thing on

534
00:27:29,800 --> 00:27:34,200
Speaker 4: food and on gas and on entertainment, and their savings

535
00:27:34,240 --> 00:27:36,920
Speaker 4: account and balances always stay pretty steady. That ought to

536
00:27:36,960 --> 00:27:38,960
Speaker 4: be a good credit. Well, it isn't a good credit

537
00:27:39,000 --> 00:27:41,879
Speaker 4: if they get fired or lose their job because the

538
00:27:41,880 --> 00:27:43,880
Speaker 4: economies and the downturn and all of a sudden, Well

539
00:27:43,880 --> 00:27:45,520
Speaker 4: I thought that was a really good credit. I should

540
00:27:45,560 --> 00:27:48,160
Speaker 4: charge five percent. Turns out I just wrote the whole

541
00:27:48,160 --> 00:27:50,919
Speaker 4: thing off because there's no recovery value in that credit.

542
00:27:51,480 --> 00:27:55,560
Speaker 4: That's that's where the science fails. There's there's exogenous variables

543
00:27:55,600 --> 00:28:00,600
Speaker 4: you can't model in. The wider you cast the net, right,

544
00:28:00,640 --> 00:28:05,600
Speaker 4: the bigger the dispersion on outcomes. However, we've settled there

545
00:28:05,880 --> 00:28:11,080
Speaker 4: that interest rate kind of sets a reasonable profit margin.

546
00:28:11,119 --> 00:28:14,160
Speaker 4: The largest card issuer in the country. You can figure

547
00:28:14,200 --> 00:28:17,800
Speaker 4: out who this is right, best at it, best computer models,

548
00:28:17,840 --> 00:28:22,360
Speaker 4: best finding credit best expend, you know, extending credit to

549
00:28:22,600 --> 00:28:26,399
Speaker 4: non prime borrowers. They trade it one time's book value.

550
00:28:28,119 --> 00:28:30,680
Speaker 4: If this was a genius business like this way too much.

551
00:28:30,720 --> 00:28:33,600
Speaker 4: I dropped the rate by one percent, you know, and

552
00:28:33,640 --> 00:28:36,560
Speaker 4: growth share. The problem is it's not so great.

553
00:28:36,600 --> 00:28:41,200
Speaker 3: Actually, I want to talk about private credit a little

554
00:28:41,200 --> 00:28:44,760
Speaker 3: bit because obviously this is an area where we always

555
00:28:44,760 --> 00:28:48,480
Speaker 3: hear about banks face new competition from middle market lenders

556
00:28:48,840 --> 00:28:50,600
Speaker 3: and all of that. But at the same time, we

557
00:28:50,640 --> 00:28:54,080
Speaker 3: also see banks teaming up with private credit now and

558
00:28:54,240 --> 00:28:58,240
Speaker 3: it seems to also, Yeah, okay, talk us through that relationship.

559
00:28:58,520 --> 00:28:58,800
Speaker 1: Yeah.

560
00:28:58,840 --> 00:29:05,360
Speaker 4: So, traditionally, standalone high yield credit, if all you did

561
00:29:05,520 --> 00:29:09,360
Speaker 4: was walk around and just lend money with no particular expertise,

562
00:29:09,400 --> 00:29:12,560
Speaker 4: but across you know, leverage clients at a single big credit,

563
00:29:12,600 --> 00:29:17,040
Speaker 4: that's a really lousy return on capital, right that you know,

564
00:29:17,080 --> 00:29:19,920
Speaker 4: all the science going back to when Moody started tracking

565
00:29:19,920 --> 00:29:22,200
Speaker 4: this stuff in the eighties. You get kind of a

566
00:29:22,240 --> 00:29:25,440
Speaker 4: thirty five percent recovery, You have a ten percent default rate.

567
00:29:25,680 --> 00:29:28,080
Speaker 4: You go through cycles, sometimes a lot worse, sometimes a

568
00:29:28,120 --> 00:29:31,560
Speaker 4: lot better. It's not a particularly good business becomes a

569
00:29:32,760 --> 00:29:36,520
Speaker 4: better business and sometimes a fantastic business. If you couple

570
00:29:36,600 --> 00:29:39,680
Speaker 4: that with the wallet that goes with that company beyond

571
00:29:39,720 --> 00:29:43,160
Speaker 4: just the credit, so IPOs and an ape's bond field,

572
00:29:43,280 --> 00:29:45,280
Speaker 4: you know, whatever the stuff is that goes along with

573
00:29:45,320 --> 00:29:47,280
Speaker 4: all of a sudden it becomes a pretty good business.

574
00:29:47,440 --> 00:29:49,560
Speaker 4: What's happened in the last couple of years, we've seen

575
00:29:49,560 --> 00:29:53,920
Speaker 4: this drift into more and more credit leaving banks going

576
00:29:53,960 --> 00:29:57,360
Speaker 4: into private equity and other alternative forms. At P and C.

577
00:29:57,560 --> 00:30:00,160
Speaker 4: It manifested itself where we'd have a client that we

578
00:30:00,240 --> 00:30:03,120
Speaker 4: might have banked for one hundred years on by third

579
00:30:03,160 --> 00:30:06,840
Speaker 4: generation family that just got sold to private equity, and

580
00:30:06,880 --> 00:30:10,160
Speaker 4: our client's gone because I don't want to make that

581
00:30:10,320 --> 00:30:15,320
Speaker 4: loan at that leverage ratio, and whoever does make the

582
00:30:15,360 --> 00:30:18,680
Speaker 4: loan is going to demand the treasury management relationship and

583
00:30:18,760 --> 00:30:22,320
Speaker 4: all the other stuff that goes with that client. So

584
00:30:22,400 --> 00:30:24,520
Speaker 4: where we come out at it is, I don't know

585
00:30:24,560 --> 00:30:28,080
Speaker 4: that I necessarily want to make the loan. I will sometimes,

586
00:30:28,640 --> 00:30:32,000
Speaker 4: but I want to keep the relationship. So our partnership

587
00:30:32,040 --> 00:30:35,640
Speaker 4: with TCW is an example is when that happens now,

588
00:30:36,480 --> 00:30:39,040
Speaker 4: you know, we put capital in the TCW fund, as

589
00:30:39,080 --> 00:30:41,440
Speaker 4: did a bunch of other people, but happens now, we'll

590
00:30:41,520 --> 00:30:45,040
Speaker 4: keep the client, you know, we'll diversify the credit risk

591
00:30:45,280 --> 00:30:48,440
Speaker 4: but keep the fees, so the return on equity on

592
00:30:48,480 --> 00:30:53,880
Speaker 4: that this thing goes way up. The whole notion. Remember

593
00:30:54,080 --> 00:30:56,080
Speaker 4: like private credit in the last three years. Oh my god,

594
00:30:56,080 --> 00:30:57,800
Speaker 4: it's a new new thing. We're great at it. We're

595
00:30:57,800 --> 00:31:00,240
Speaker 4: going to make you a fortune. Why is that? Private

596
00:31:00,240 --> 00:31:02,760
Speaker 4: equity kind of hit the skids. Nothing was getting sold.

597
00:31:02,840 --> 00:31:05,400
Speaker 4: They need to raise new money. We haven't had defaults

598
00:31:05,400 --> 00:31:09,080
Speaker 4: in ten years. So a manager shows up and says,

599
00:31:09,080 --> 00:31:11,200
Speaker 4: look at my track record, I can earn you know,

600
00:31:11,240 --> 00:31:14,760
Speaker 4: with two times leverage. I'll give you ten percent return

601
00:31:15,000 --> 00:31:18,400
Speaker 4: after fees. This is fantastic. Private equity sucks. Come with me.

602
00:31:19,160 --> 00:31:21,480
Speaker 4: Of course, we haven't had a credit cycle in ten years,

603
00:31:21,480 --> 00:31:23,760
Speaker 4: so those stats are all garbage. Yeah, you know, I

604
00:31:23,800 --> 00:31:25,959
Speaker 4: actually go through a credit cycle and you have two

605
00:31:26,040 --> 00:31:28,240
Speaker 4: times leverage on that thing, and you start having the faults,

606
00:31:28,240 --> 00:31:29,560
Speaker 4: which we've had a few of, all of a sudden,

607
00:31:29,600 --> 00:31:44,160
Speaker 4: it's not really exciting.

608
00:31:46,400 --> 00:31:49,360
Speaker 3: Can I ask one macro question in this conversation, which

609
00:31:49,400 --> 00:31:51,440
Speaker 3: is where do you think we are in the credit

610
00:31:51,480 --> 00:31:52,360
Speaker 3: cycle at the moment.

611
00:31:52,800 --> 00:31:55,520
Speaker 4: I've given up trying to predict it. Credit today is

612
00:31:55,600 --> 00:31:58,880
Speaker 4: pretty good, better than pretty good. A lot of the

613
00:31:59,000 --> 00:32:02,120
Speaker 4: leverage that's in the system that everybody complains about are

614
00:32:02,240 --> 00:32:06,120
Speaker 4: actually with things that are very good enterprise value. So

615
00:32:06,160 --> 00:32:10,760
Speaker 4: I don't worry so much about the disappearance of a company.

616
00:32:10,840 --> 00:32:13,200
Speaker 4: I think about it in terms of reallocation of the

617
00:32:13,200 --> 00:32:16,680
Speaker 4: ownership structure. Right. If they're overlevered, they go down, either

618
00:32:16,760 --> 00:32:20,080
Speaker 4: private equity firm bails them out, or they say no,

619
00:32:20,240 --> 00:32:22,440
Speaker 4: here are the keys, and now the debt holder owns them,

620
00:32:22,440 --> 00:32:24,600
Speaker 4: and then the debt holder restructures the debt and sells

621
00:32:24,640 --> 00:32:27,480
Speaker 4: them again. Somebody lost money, but the company is still

622
00:32:27,520 --> 00:32:30,880
Speaker 4: a good company. Was just overleavered to go. Most of

623
00:32:30,920 --> 00:32:34,000
Speaker 4: the noise you're hearing right now on credit beyond the fraud.

624
00:32:34,240 --> 00:32:36,880
Speaker 4: You know the cockroaches that JBSID come up when things

625
00:32:36,960 --> 00:32:41,240
Speaker 4: get stretched. Most of it are decent enterprises, just overlevered.

626
00:32:42,440 --> 00:32:43,960
Speaker 4: And then of course you have the fraud. You haven't

627
00:32:44,000 --> 00:32:47,040
Speaker 4: had anything that's really going down today because the economy

628
00:32:47,840 --> 00:32:50,560
Speaker 4: just crashed on a spot. Other than some isolated issues

629
00:32:50,600 --> 00:32:51,360
Speaker 4: with tariffs.

630
00:32:51,720 --> 00:32:55,440
Speaker 2: There is a cyclical element to fraud, right, because in

631
00:32:55,720 --> 00:32:59,400
Speaker 2: good times people look the other way, and I don't.

632
00:33:00,040 --> 00:33:02,200
Speaker 4: Yeah, I don't take its cyclical. I think it's always there.

633
00:33:02,280 --> 00:33:06,239
Speaker 4: I just think you know the who's this said? You

634
00:33:06,280 --> 00:33:08,000
Speaker 4: know when the when the water goes out, you can

635
00:33:08,040 --> 00:33:11,520
Speaker 4: figure out who's swim and nude buffet. Yeah yeah.

636
00:33:11,560 --> 00:33:14,040
Speaker 2: Well gall Braves, however, talked about the bezel right, the

637
00:33:15,000 --> 00:33:20,800
Speaker 2: the the propensity of flim flams and scams who exist

638
00:33:20,880 --> 00:33:23,800
Speaker 2: during the good times and people just like don't and

639
00:33:23,800 --> 00:33:27,160
Speaker 2: that accumulates and accumulates, and that could we've had like

640
00:33:27,200 --> 00:33:29,640
Speaker 2: twenty years, as you said, like we've had a credit size.

641
00:33:29,640 --> 00:33:32,160
Speaker 2: I mean we're it's almost twenty years since the financial crisis.

642
00:33:32,200 --> 00:33:35,000
Speaker 4: Now, Yeah, the finance always finds its way, it finds

643
00:33:35,000 --> 00:33:37,200
Speaker 4: a way to blow itself up. I mean, it's it's

644
00:33:37,840 --> 00:33:41,800
Speaker 4: I mean, you know, it's not an opinion. And just

645
00:33:42,000 --> 00:33:44,600
Speaker 4: look at history. I think what happens is you get

646
00:33:44,680 --> 00:33:46,959
Speaker 4: you get you know, we find good things and then

647
00:33:47,000 --> 00:33:50,280
Speaker 4: you write them and you in every instance where something's

648
00:33:50,320 --> 00:33:53,960
Speaker 4: going wrong, you just keep pushing on something until you

649
00:33:54,040 --> 00:33:57,000
Speaker 4: take it over the edge. Right, finances in industry where

650
00:33:57,120 --> 00:34:01,200
Speaker 4: historically you can make a lot of money at it, right,

651
00:34:01,200 --> 00:34:05,360
Speaker 4: you're playing with other people's money, you know, pennies and

652
00:34:05,440 --> 00:34:07,840
Speaker 4: nickels coming off of other people's money. I'm just going

653
00:34:07,920 --> 00:34:10,000
Speaker 4: to keep pushing. I'm going to keep pushing on, keep pushing.

654
00:34:10,040 --> 00:34:13,520
Speaker 4: When it finally falls, it falls hard. We haven't had that.

655
00:34:14,160 --> 00:34:16,840
Speaker 4: You know, what's the new new thing now? It's fintech,

656
00:34:16,880 --> 00:34:20,319
Speaker 4: and it's crypto, and it's you know, banking has been

657
00:34:20,440 --> 00:34:22,960
Speaker 4: kind of throttled by regulation for ten years.

658
00:34:23,880 --> 00:34:26,520
Speaker 3: Well, speaking of finance always finding a way to blow

659
00:34:26,560 --> 00:34:29,440
Speaker 3: itself up, can we talk about Silicon Valley Bank and

660
00:34:29,719 --> 00:34:32,520
Speaker 3: twenty twenty three, because you know, they managed to blow

661
00:34:32,560 --> 00:34:35,680
Speaker 3: themselves up for a variety of reasons, but one of

662
00:34:35,680 --> 00:34:38,160
Speaker 3: them was they just had too many bonds, which is

663
00:34:38,280 --> 00:34:41,400
Speaker 3: kind of, you know, an unusual one. What was that

664
00:34:41,520 --> 00:34:43,000
Speaker 3: experience like for you?

665
00:34:43,120 --> 00:34:47,680
Speaker 4: First of all, let's go back to Silicon I actually

666
00:34:47,719 --> 00:34:49,040
Speaker 4: think they had a great institution.

667
00:34:50,440 --> 00:34:53,400
Speaker 2: We've been saying that. Who's been saying that times on

668
00:34:53,480 --> 00:34:57,640
Speaker 2: this podcast? The Silicon Valley Bank perspector is okay, keep going.

669
00:34:57,560 --> 00:35:00,520
Speaker 4: No, it's just it's it was a unique business model

670
00:35:00,560 --> 00:35:04,759
Speaker 4: with a very loyal client base that they not that

671
00:35:04,880 --> 00:35:07,879
Speaker 4: loyal in the I mean, look, you know, if you're dying,

672
00:35:07,960 --> 00:35:11,200
Speaker 4: loyalty goes out the window. But they blew themselves up

673
00:35:11,239 --> 00:35:13,120
Speaker 4: in something that was not at all court of their

674
00:35:13,160 --> 00:35:16,440
Speaker 4: basic business right interest rates are super low. They got

675
00:35:16,480 --> 00:35:19,640
Speaker 4: as long by some calculations as like long term capital

676
00:35:19,800 --> 00:35:22,280
Speaker 4: was back in the night. I mean they were really

677
00:35:22,320 --> 00:35:27,919
Speaker 4: really levered and underestimated the negative convexity deposits, right you think,

678
00:35:28,000 --> 00:35:29,960
Speaker 4: you know, on average through the cycle, they're going to

679
00:35:30,000 --> 00:35:32,799
Speaker 4: be their seven years. Oops, they're gone tomorrow. And so

680
00:35:32,840 --> 00:35:34,960
Speaker 4: they just they blew themselves up on rates that had

681
00:35:35,120 --> 00:35:37,799
Speaker 4: nothing to do with their core competence or what they

682
00:35:37,800 --> 00:35:40,600
Speaker 4: were as a franchise. And it's it's unfortunate. It was

683
00:35:40,640 --> 00:35:45,320
Speaker 4: a rookie mistake. There was nothing. It's just a crazy position.

684
00:35:45,080 --> 00:35:45,440
Speaker 3: To be in.

685
00:35:46,320 --> 00:35:48,799
Speaker 2: How scary you know those those days afterwards and then

686
00:35:48,840 --> 00:35:52,839
Speaker 2: people started hunting for like any any bank that had

687
00:35:52,880 --> 00:35:55,520
Speaker 2: to word California in the name, like people were shorting

688
00:35:55,520 --> 00:35:56,240
Speaker 2: at or people.

689
00:35:56,000 --> 00:35:59,160
Speaker 4: Were saying it or you know, you had headlines where

690
00:35:59,280 --> 00:36:01,600
Speaker 4: you know, the region bank index, the regional bank. And

691
00:36:01,719 --> 00:36:03,480
Speaker 4: that's why I'm a national bank. We are not a

692
00:36:03,520 --> 00:36:05,759
Speaker 4: regional bank because the next time they put regional bank

693
00:36:05,800 --> 00:36:08,360
Speaker 4: headlines in there, I don't want to be associated. No,

694
00:36:08,520 --> 00:36:12,040
Speaker 4: we actually benefited during that, you know, we're a safe

695
00:36:12,120 --> 00:36:15,840
Speaker 4: haven and we actually gathered to positus and open thousands

696
00:36:15,880 --> 00:36:18,880
Speaker 4: of new accounts during that period of time, and then

697
00:36:18,920 --> 00:36:21,440
Speaker 4: every day somebody would flash the regional bank index and

698
00:36:21,560 --> 00:36:24,200
Speaker 4: we'd get blasted on our valuation, which is fine, it

699
00:36:24,280 --> 00:36:27,520
Speaker 4: always yeah, you know, valuations. So short term, it all

700
00:36:27,600 --> 00:36:28,799
Speaker 4: came ripping back after that.

701
00:36:29,920 --> 00:36:32,520
Speaker 3: So one of the issues that came up during the

702
00:36:32,560 --> 00:36:36,240
Speaker 3: twenty twenty three banking drama, I guess, is the discount

703
00:36:36,280 --> 00:36:40,680
Speaker 3: window and banks reluctance collectively to use it. And this

704
00:36:40,719 --> 00:36:43,120
Speaker 3: is something that the Fed's been trying to change, right

705
00:36:43,440 --> 00:36:45,960
Speaker 3: And actually, do you remember in late twenty twenty two

706
00:36:46,080 --> 00:36:50,680
Speaker 3: there was a big spike in tapping usage of the

707
00:36:50,680 --> 00:36:53,360
Speaker 3: discount window, and it turned out that wasn't any of

708
00:36:53,400 --> 00:36:56,200
Speaker 3: the banks that like ended up getting into trouble. It

709
00:36:56,239 --> 00:36:57,880
Speaker 3: was Buel Bank down in Texas.

710
00:36:58,000 --> 00:36:58,759
Speaker 2: It's a weird bank.

711
00:36:58,920 --> 00:37:02,800
Speaker 3: Yeah, it's a very weird bank. But anyway, regulators trying

712
00:37:02,800 --> 00:37:04,839
Speaker 3: to get banks to use the discount when you more

713
00:37:04,880 --> 00:37:07,680
Speaker 3: feel more comfortable with it, how do you feel about it?

714
00:37:09,480 --> 00:37:14,719
Speaker 4: But that's a great question. So before the financial crisis

715
00:37:14,760 --> 00:37:19,320
Speaker 4: and before regulation and LCR and long term debt proposals

716
00:37:19,360 --> 00:37:21,320
Speaker 4: and all that other stuff, there was a very active

717
00:37:21,360 --> 00:37:25,040
Speaker 4: FED funds market. So in a given day, the excess

718
00:37:25,080 --> 00:37:30,440
Speaker 4: Reserve bank would hold deposited in its FED account was tiny,

719
00:37:30,480 --> 00:37:33,080
Speaker 4: and we would trade FED funds around with each other.

720
00:37:33,160 --> 00:37:35,360
Speaker 4: If I needed two billion, you'd give me two billion.

721
00:37:35,840 --> 00:37:38,120
Speaker 4: Next day I might give you two billion, like big

722
00:37:38,200 --> 00:37:41,279
Speaker 4: size FED fund positions. And that's how you balanced your

723
00:37:41,280 --> 00:37:44,120
Speaker 4: books at the end of every night. And then they

724
00:37:44,160 --> 00:37:47,759
Speaker 4: come out and they say, hang on, we don't want

725
00:37:47,800 --> 00:37:50,640
Speaker 4: you doing that anymore. There's an LCR acquirement. You have

726
00:37:50,719 --> 00:37:54,680
Speaker 4: to have this much liquidity stuck aside instead of going

727
00:37:54,719 --> 00:37:57,960
Speaker 4: into the FED funds market that gave rise to all

728
00:37:57,960 --> 00:38:01,239
Speaker 4: these excess reserves. Fed fund market it's basically dead. The

729
00:38:01,239 --> 00:38:03,680
Speaker 4: FED sets that rate, nobody trades on that rate. It's

730
00:38:03,680 --> 00:38:05,759
Speaker 4: as dead as live or it's gone. I don't even

731
00:38:05,760 --> 00:38:08,880
Speaker 4: know why they talk about it anymore. Now you have,

732
00:38:09,920 --> 00:38:12,280
Speaker 4: they basically said to you, don't use the discount window

733
00:38:12,320 --> 00:38:15,239
Speaker 4: and hold all this cash. You just froze the economy

734
00:38:15,320 --> 00:38:21,960
Speaker 4: because you stopped the circulation of capital transformation. Home loan

735
00:38:22,000 --> 00:38:24,640
Speaker 4: Bank step in. Because I have to hold all of

736
00:38:24,680 --> 00:38:27,799
Speaker 4: these treasuries and mortgage securities, I can pledge them to

737
00:38:27,840 --> 00:38:31,239
Speaker 4: Home Loan and they'll lend me money at pretty good rate.

738
00:38:31,600 --> 00:38:33,560
Speaker 4: So that's going to be my outlet. I'm going to

739
00:38:33,600 --> 00:38:35,080
Speaker 4: go to home loan and I'm just going to borrow

740
00:38:35,080 --> 00:38:36,600
Speaker 4: there at a pretty good rate, or I'm going to

741
00:38:36,680 --> 00:38:42,200
Speaker 4: repo my treasuries that I hold. Silicon Valley happens, and

742
00:38:43,080 --> 00:38:45,360
Speaker 4: they had home loan lines. They didn't have any discount

743
00:38:45,440 --> 00:38:50,160
Speaker 4: window lines. Now, the discount window will lend against treasuries

744
00:38:50,560 --> 00:38:57,359
Speaker 4: and agencies pretty simply because it's basically settled electronically through

745
00:38:57,400 --> 00:39:03,040
Speaker 4: effectively the fed wire system. Pledging loans, it's really hard

746
00:39:03,360 --> 00:39:06,920
Speaker 4: to pledge. So if all I have is securities to pledge,

747
00:39:06,960 --> 00:39:08,680
Speaker 4: I should just repo them, or I'll give them to

748
00:39:08,680 --> 00:39:11,560
Speaker 4: the home loan bank. Discount window will do more than that.

749
00:39:11,960 --> 00:39:14,320
Speaker 4: But if I want to draw against my loan balance,

750
00:39:14,400 --> 00:39:18,240
Speaker 4: I need to give them physical wet signature loan docs

751
00:39:18,360 --> 00:39:23,319
Speaker 4: in a vault, guarded audited twenty four hours a day,

752
00:39:23,360 --> 00:39:24,839
Speaker 4: like there's got to be a garden. I have wet

753
00:39:24,840 --> 00:39:28,200
Speaker 4: signature stacked up so they know that when I draw

754
00:39:28,200 --> 00:39:30,560
Speaker 4: against that loan, I actually have that loan. Because it

755
00:39:30,600 --> 00:39:33,080
Speaker 4: doesn't settle fed wire like a bond does. Is it

756
00:39:33,120 --> 00:39:35,120
Speaker 4: a loan, right, I'd lend you money, you sign the thing.

757
00:39:35,160 --> 00:39:38,160
Speaker 4: I put it in the drawer somewhere. Then you say,

758
00:39:38,160 --> 00:39:41,080
Speaker 4: all right, let's preposition these loans. We spent P ANDC

759
00:39:41,200 --> 00:39:44,279
Speaker 4: spent millions and millions and millions of dollars. So the

760
00:39:44,400 --> 00:39:47,759
Speaker 4: vast majority of our CNI loans are now at the

761
00:39:47,840 --> 00:39:50,560
Speaker 4: discount window. We don't borrow on them, but they're there

762
00:39:51,640 --> 00:39:54,879
Speaker 4: if we wanted to borrow under them. I got a call.

763
00:39:54,920 --> 00:39:57,920
Speaker 4: It's gotten a little better. But basically, like during COVID,

764
00:39:58,280 --> 00:40:00,720
Speaker 4: you know, or post COVID, when nobody is in the office,

765
00:40:00,760 --> 00:40:02,960
Speaker 4: you'd call, like the way you borrow from thiscount win Is,

766
00:40:03,000 --> 00:40:05,840
Speaker 4: you call Cleveland there are there are FED twelve this,

767
00:40:06,000 --> 00:40:08,960
Speaker 4: and then they say, okay, yes, you're in good shape.

768
00:40:09,040 --> 00:40:12,600
Speaker 4: We'll allow Washington to allow you to use the discount window.

769
00:40:12,640 --> 00:40:15,160
Speaker 4: And then they'll call Washington and then maybe like a

770
00:40:15,200 --> 00:40:17,520
Speaker 4: day or so later, you can have money. When nobody's

771
00:40:17,560 --> 00:40:19,640
Speaker 4: coming into the office, nobody would answer the phone. I

772
00:40:19,680 --> 00:40:21,720
Speaker 4: got to draw on the discount window. That just rings.

773
00:40:21,760 --> 00:40:24,520
Speaker 4: Nobody's there. God, there's no system. It's not like I

774
00:40:24,560 --> 00:40:26,279
Speaker 4: can type in and draw. It's not like I can

775
00:40:26,320 --> 00:40:28,480
Speaker 4: do a FED funds trade through a broker and draw

776
00:40:28,480 --> 00:40:30,640
Speaker 4: the money. It's it's unusable.

777
00:40:31,160 --> 00:40:35,279
Speaker 3: But now you're prepositioned to borrow against loans if you

778
00:40:35,320 --> 00:40:35,640
Speaker 3: need to.

779
00:40:35,840 --> 00:40:38,719
Speaker 4: Yeah, we have enough liquidity on hand basically to cover

780
00:40:38,760 --> 00:40:41,520
Speaker 4: anything that can happen because we positioned all of our

781
00:40:41,640 --> 00:40:46,120
Speaker 4: collateral and the optimal spots.

782
00:40:45,200 --> 00:40:48,600
Speaker 2: Business are you're talking about physical spots when you say

783
00:40:48,640 --> 00:40:51,560
Speaker 2: you because you're talking about like wet signature document.

784
00:40:51,680 --> 00:40:55,040
Speaker 4: Yeah. So the only place you can borrow against C

785
00:40:55,239 --> 00:40:59,600
Speaker 4: and EE loans in effect is the discount window. So okay,

786
00:40:59,680 --> 00:41:02,400
Speaker 4: so they're to go over there. That's what signature. I

787
00:41:02,440 --> 00:41:06,120
Speaker 4: can borrow against securities in the repo market, at the

788
00:41:06,160 --> 00:41:08,920
Speaker 4: home loan or the FED. So this is kind of

789
00:41:08,920 --> 00:41:12,760
Speaker 4: the confusion that happened. Where was stuff pledged with Silicon Valley.

790
00:41:13,520 --> 00:41:16,359
Speaker 4: I have, you know, fifty billion of cash sitting on

791
00:41:16,400 --> 00:41:20,360
Speaker 4: the Fed's excess reserve book, So that's instant liquidity. And

792
00:41:20,400 --> 00:41:22,319
Speaker 4: I own a whole bunch of treasuries that I can

793
00:41:22,760 --> 00:41:26,440
Speaker 4: sell if I had to. So you just you figure

794
00:41:26,480 --> 00:41:30,000
Speaker 4: out how do I optimally earn whatever I can on

795
00:41:30,040 --> 00:41:32,839
Speaker 4: these assets I'm prepositioning, but get them in the right

796
00:41:32,840 --> 00:41:35,120
Speaker 4: place so if I need liquidity in an emergency, I

797
00:41:35,120 --> 00:41:37,360
Speaker 4: can get it. Silicon Valley did not do that, and

798
00:41:37,400 --> 00:41:39,279
Speaker 4: the FED did not make it easy for them to

799
00:41:39,280 --> 00:41:39,560
Speaker 4: do that.

800
00:41:39,600 --> 00:41:40,879
Speaker 2: And I'm just sure to be clear when you say

801
00:41:40,960 --> 00:41:43,040
Speaker 2: get them in the right place, you mean like a

802
00:41:43,080 --> 00:41:44,080
Speaker 2: physical place.

803
00:41:44,719 --> 00:41:47,399
Speaker 4: Some of it's physical, like the loans, the wet signature loans.

804
00:41:47,440 --> 00:41:49,239
Speaker 4: I literally think we take them down to like a

805
00:41:49,320 --> 00:41:51,200
Speaker 4: vault and spin a dial, you know, with the big

806
00:41:51,680 --> 00:41:53,759
Speaker 4: the big crank, and they get locked in there. But

807
00:41:54,520 --> 00:41:57,680
Speaker 4: no securities By q SIP. You would say, like, this

808
00:41:57,840 --> 00:42:00,640
Speaker 4: batch of q SIPs is prepositioned with the FED, so

809
00:42:00,680 --> 00:42:02,360
Speaker 4: I just know where they are, I know the balances,

810
00:42:02,400 --> 00:42:04,040
Speaker 4: I know I can send them right over FED wire

811
00:42:04,080 --> 00:42:07,960
Speaker 4: and draw on that. It's that thing. Yeah.

812
00:42:07,960 --> 00:42:10,239
Speaker 3: One thing I always wondered with the discount window. So

813
00:42:10,360 --> 00:42:13,120
Speaker 3: part of the reluctance historically to tap it on the

814
00:42:13,160 --> 00:42:16,399
Speaker 3: part of banks has been they're worried it'll get out

815
00:42:16,440 --> 00:42:19,080
Speaker 3: into the market, right and there'll be rumors that they're

816
00:42:19,120 --> 00:42:22,760
Speaker 3: tapping the market. How does that actually happen, I don't understand.

817
00:42:24,560 --> 00:42:28,520
Speaker 4: So in theory, because let's imagine somebody drew on the FED,

818
00:42:28,680 --> 00:42:31,560
Speaker 4: so the discount window is regional FED. Let's assume somebody

819
00:42:31,600 --> 00:42:34,640
Speaker 4: took down one hundred billion from there, or even twenty

820
00:42:34,680 --> 00:42:37,719
Speaker 4: billion from the discount window. Through the Cleveland FED kind

821
00:42:37,760 --> 00:42:40,160
Speaker 4: of e VP and C so the people can figure

822
00:42:40,160 --> 00:42:41,960
Speaker 4: out who it is. So why are you doing that?

823
00:42:42,120 --> 00:42:44,000
Speaker 4: Then I got a story and I got to explain

824
00:42:44,040 --> 00:42:46,879
Speaker 4: it where I think this should go. So go back

825
00:42:46,880 --> 00:42:48,759
Speaker 4: to the original where I said FED Funds is dead.

826
00:42:48,800 --> 00:42:52,160
Speaker 4: Nobody trays it. We had to just trade the discount window, right.

827
00:42:52,200 --> 00:42:54,440
Speaker 4: The discount raid ought to be I'm just lending and

828
00:42:54,520 --> 00:42:58,480
Speaker 4: borrowing all day long. I can leave money excess reserves,

829
00:42:59,280 --> 00:43:01,600
Speaker 4: right if I have money to give, and I can

830
00:43:01,680 --> 00:43:04,920
Speaker 4: borrow money discount window and I need money, and there

831
00:43:04,920 --> 00:43:06,960
Speaker 4: ought to be like a five basis point bitter offer

832
00:43:07,040 --> 00:43:10,080
Speaker 4: on that. You'd put more liquidity into the system here

833
00:43:10,120 --> 00:43:12,759
Speaker 4: to grow the economy if you did that, you know,

834
00:43:12,800 --> 00:43:15,360
Speaker 4: in a great way. But it takes like they wiped

835
00:43:15,360 --> 00:43:18,359
Speaker 4: out library. They wiped out FED Funds through all their

836
00:43:18,440 --> 00:43:20,920
Speaker 4: LCR crome. The you know, I have the discount rate,

837
00:43:21,560 --> 00:43:23,400
Speaker 4: just bid offer on the discount right, and then all

838
00:43:23,400 --> 00:43:26,600
Speaker 4: of a sudden, discount window becomes right way transaction. It's happening.

839
00:43:26,680 --> 00:43:27,920
Speaker 3: Yeah, day stigma goes away.

840
00:43:28,000 --> 00:43:30,200
Speaker 2: Yeah, So you have to forgive me because there's a

841
00:43:30,200 --> 00:43:31,640
Speaker 2: lot going on in the world these days. So I

842
00:43:31,640 --> 00:43:36,040
Speaker 2: can't pay attention to everything. I have no idea where

843
00:43:36,160 --> 00:43:41,720
Speaker 2: you stand on yield bearing stable coins, which I understand

844
00:43:41,719 --> 00:43:45,080
Speaker 2: to be a big thing of debate in the sector.

845
00:43:45,280 --> 00:43:48,920
Speaker 2: And I'm again I have I've been begging attention to

846
00:43:49,000 --> 00:43:51,000
Speaker 2: other things. But I see headlines about this pop up.

847
00:43:51,280 --> 00:43:55,279
Speaker 4: Yeah, so stable clon legislation GENISAC comes out. They left

848
00:43:55,280 --> 00:43:57,560
Speaker 4: a little loophole in there. They said no interest, but

849
00:43:57,640 --> 00:44:01,319
Speaker 4: it kind of got run around by offering reward promos.

850
00:44:01,760 --> 00:44:06,000
Speaker 4: Same thing. Clarity Act is what's being debated right now.

851
00:44:06,000 --> 00:44:08,719
Speaker 4: They're trying to close that loophole. They being kind of

852
00:44:08,760 --> 00:44:12,880
Speaker 4: banks and people around banks keeping the loophole open. As crypto,

853
00:44:13,200 --> 00:44:14,839
Speaker 4: you know, why not if you can pay interest, it's

854
00:44:14,880 --> 00:44:16,960
Speaker 4: going to drive volume. I want to pay interest, banks out.

855
00:44:16,960 --> 00:44:18,520
Speaker 4: I want you to pay interest because you're going to

856
00:44:18,880 --> 00:44:22,440
Speaker 4: potentially suck deposits out of the system, and you're messing

857
00:44:22,440 --> 00:44:25,600
Speaker 4: around with a capital system that's worked for two hundred

858
00:44:25,600 --> 00:44:27,319
Speaker 4: and fifty years. You share you want to mess with it.

859
00:44:27,560 --> 00:44:29,799
Speaker 4: At the end of the day, I think of I

860
00:44:29,840 --> 00:44:35,680
Speaker 4: think the following stable coin has been built, launched, marketed

861
00:44:36,640 --> 00:44:40,040
Speaker 4: as a payment mechanism. It's somehow by the way, I'm

862
00:44:40,040 --> 00:44:42,120
Speaker 4: not sure it will but you know it's going to

863
00:44:42,120 --> 00:44:48,839
Speaker 4: make payments easier, faster, cheaper, whatever, not as an investment vehicle, right,

864
00:44:48,880 --> 00:44:52,239
Speaker 4: an investment vehicle is a different animal and needs different regulation.

865
00:44:52,480 --> 00:44:54,520
Speaker 4: So all of a sudden, like a stable coin that's

866
00:44:54,560 --> 00:44:58,200
Speaker 4: buying t bills that pays you interest, that looks an

867
00:44:58,200 --> 00:45:01,080
Speaker 4: awful lot like a money fund to me, And that's fine.

868
00:45:01,080 --> 00:45:02,840
Speaker 4: If it's a money fund, then regulate it like a

869
00:45:02,880 --> 00:45:04,799
Speaker 4: money fund. Can't break the buck. You got to have

870
00:45:04,840 --> 00:45:08,160
Speaker 4: this much liquidity on hand, you know, all the disclosure

871
00:45:08,239 --> 00:45:10,880
Speaker 4: rules that go around with it. Don't do what you

872
00:45:10,920 --> 00:45:12,600
Speaker 4: want with it, you know, go ahead and pay people

873
00:45:12,600 --> 00:45:15,920
Speaker 4: in money funds. I just think they're mixing things and

874
00:45:15,960 --> 00:45:22,520
Speaker 4: I think, you know, my my uninformed worry is we're

875
00:45:22,520 --> 00:45:24,480
Speaker 4: not done blowing up in crypto yet.

876
00:45:25,640 --> 00:45:25,799
Speaker 1: Right.

877
00:45:25,840 --> 00:45:28,640
Speaker 4: It's in fits and starts as you learn these things.

878
00:45:29,280 --> 00:45:32,040
Speaker 4: Why do we need to start everything at once. Let's

879
00:45:32,080 --> 00:45:34,560
Speaker 4: figure some stuff out. If it works and there's a

880
00:45:34,760 --> 00:45:36,680
Speaker 4: logic to doing it, then go ahead and do it later.

881
00:45:36,719 --> 00:45:38,200
Speaker 4: I just I don't know that you need to do

882
00:45:38,239 --> 00:45:38,879
Speaker 4: it all at once.

883
00:45:39,120 --> 00:45:39,800
Speaker 2: What's the rush?

884
00:45:40,040 --> 00:45:41,000
Speaker 4: Yeah?

885
00:45:41,040 --> 00:45:44,720
Speaker 3: You know, we are coming up to the super Bowl,

886
00:45:45,600 --> 00:45:48,680
Speaker 3: and when I think it's not super Bowl I always

887
00:45:48,719 --> 00:45:52,239
Speaker 3: think of the basil endgame proposals and that ad that

888
00:45:52,280 --> 00:45:56,239
Speaker 3: they ran in twenty twenty four. It was a very

889
00:45:56,280 --> 00:46:00,200
Speaker 3: effective marketing tool, at least for me. And we are

890
00:46:00,200 --> 00:46:04,799
Speaker 3: expecting a reproposal of the endgame. What's your sense of

891
00:46:04,840 --> 00:46:09,279
Speaker 3: what might change between you know, the original document or

892
00:46:09,320 --> 00:46:10,800
Speaker 3: suggestions versus now.

893
00:46:11,000 --> 00:46:14,239
Speaker 4: I have no inside information on that, but the original

894
00:46:14,280 --> 00:46:19,879
Speaker 4: proposal that was written was in contempt of math. They

895
00:46:19,920 --> 00:46:24,040
Speaker 4: basically they had a goal of raising capital and then

896
00:46:24,040 --> 00:46:27,239
Speaker 4: they used bad math to cause that outcome, which was

897
00:46:27,360 --> 00:46:31,120
Speaker 4: a lot of what the outrage was over, because if

898
00:46:31,120 --> 00:46:35,560
Speaker 4: you start making up rules in capital allocation, you inadvertently

899
00:46:35,960 --> 00:46:40,040
Speaker 4: cause risk to emerge in places they shouldn't. So I

900
00:46:40,080 --> 00:46:42,560
Speaker 4: think the math is fixed. You know the lawsuit on

901
00:46:42,600 --> 00:46:45,279
Speaker 4: the stress test and the fact the models need to

902
00:46:45,280 --> 00:46:46,960
Speaker 4: be public, so the math is going to be a

903
00:46:46,960 --> 00:46:51,120
Speaker 4: lot better on what they measure. You've heard about indexing

904
00:46:51,280 --> 00:46:56,200
Speaker 4: gesub scores to inflation and some of the boundaries. I

905
00:46:56,239 --> 00:46:58,480
Speaker 4: don't know what they're going to do in operated risk capital.

906
00:46:58,520 --> 00:47:00,160
Speaker 4: It's been a fight. I think they're going to have

907
00:47:00,200 --> 00:47:03,400
Speaker 4: some credit on credit risk gratings that Europe gives that

908
00:47:03,440 --> 00:47:05,920
Speaker 4: we don't, and my guess is there'll be something in

909
00:47:05,960 --> 00:47:10,640
Speaker 4: there for everybody, and then everybody will collectively be disappointed

910
00:47:10,680 --> 00:47:12,640
Speaker 4: about something. They're not going to give away the shop,

911
00:47:12,680 --> 00:47:16,600
Speaker 4: but they're going to try to make it directionally correct

912
00:47:17,800 --> 00:47:21,280
Speaker 4: and based on real math instead of made up stuff.

913
00:47:21,640 --> 00:47:23,640
Speaker 2: I'm going to go back to something you said about

914
00:47:23,640 --> 00:47:28,000
Speaker 2: the bank integration and you actually gave a very rare

915
00:47:28,200 --> 00:47:33,680
Speaker 2: I would say, actual twenty twenty six application of generative

916
00:47:33,719 --> 00:47:38,319
Speaker 2: AI implementation where you mentioned that the person at the

917
00:47:38,320 --> 00:47:41,399
Speaker 2: local bank branch can actually like ask a question about

918
00:47:41,400 --> 00:47:44,239
Speaker 2: the policies and learn. So that wasn't just a hypothetical,

919
00:47:44,280 --> 00:47:45,520
Speaker 2: that's a real thing that's happening right now.

920
00:47:45,560 --> 00:47:46,120
Speaker 4: Yeah, that's rue.

921
00:47:46,320 --> 00:47:48,960
Speaker 2: Can you talk this is a theme that's been we've

922
00:47:48,960 --> 00:47:53,960
Speaker 2: been talking about on the podcast lately, which is, you know,

923
00:47:54,000 --> 00:47:56,840
Speaker 2: not just AI per se or implementing or we're going

924
00:47:56,880 --> 00:47:59,400
Speaker 2: to like implement AI, but like, you know, the changing

925
00:47:59,440 --> 00:48:03,399
Speaker 2: relationship between a company and their software vendors, and you know,

926
00:48:03,520 --> 00:48:06,080
Speaker 2: does AI change some of these relationships? Do you have

927
00:48:06,160 --> 00:48:08,759
Speaker 2: leverage because there are certain like niche functions that you

928
00:48:08,760 --> 00:48:11,040
Speaker 2: could like build yourself. Can you talk a little bit

929
00:48:11,080 --> 00:48:13,399
Speaker 2: about how you're seeing some of these things play out?

930
00:48:13,440 --> 00:48:14,359
Speaker 2: Within your front.

931
00:48:14,719 --> 00:48:17,840
Speaker 4: Yeah, this is fun because there's so much bad information

932
00:48:18,440 --> 00:48:21,960
Speaker 4: about that. So start with the basics. In order to

933
00:48:22,120 --> 00:48:26,400
Speaker 4: utilize AI, you need clean data, define data with a

934
00:48:26,440 --> 00:48:30,839
Speaker 4: single source of truth. We've spent ten years doing that

935
00:48:30,920 --> 00:48:34,240
Speaker 4: in a fortune. So, you know, silly as that sounds,

936
00:48:34,239 --> 00:48:36,760
Speaker 4: I think how many different places inside of a company

937
00:48:36,800 --> 00:48:40,239
Speaker 4: you might have, you know, a checking account balance that's

938
00:48:40,280 --> 00:48:43,760
Speaker 4: then morphed into the average checking account balance over seven days,

939
00:48:44,160 --> 00:48:46,200
Speaker 4: or the median or this or the that. And so

940
00:48:46,280 --> 00:48:48,400
Speaker 4: when someone's trying to pull data to run a reporter

941
00:48:48,480 --> 00:48:50,560
Speaker 4: or build a model, they don't even know what they're real.

942
00:48:50,680 --> 00:48:52,799
Speaker 4: They don't even know what they're pulling. So you got

943
00:48:52,800 --> 00:48:55,480
Speaker 4: to get it clean at the base source and an index.

944
00:48:55,719 --> 00:48:58,000
Speaker 4: Now you have the capacity to build a model, whether

945
00:48:58,040 --> 00:49:02,400
Speaker 4: it's large language or analytical large language models. There's open

946
00:49:02,440 --> 00:49:04,640
Speaker 4: models out there you can just use, which is what

947
00:49:04,680 --> 00:49:07,680
Speaker 4: we tend to do to build our document sorting. We're

948
00:49:07,760 --> 00:49:10,239
Speaker 4: using it on our own data, our own documents. I

949
00:49:10,239 --> 00:49:13,040
Speaker 4: don't need their data, and I don't need to retrain

950
00:49:13,120 --> 00:49:15,279
Speaker 4: their model per se, other than running it on my

951
00:49:15,320 --> 00:49:19,360
Speaker 4: own data, which is this much smaller compute factory. I

952
00:49:19,400 --> 00:49:22,040
Speaker 4: don't have the whole Internet, downdate, download it. I just

953
00:49:22,080 --> 00:49:24,400
Speaker 4: got this thing right. And then once we train the model,

954
00:49:24,800 --> 00:49:27,360
Speaker 4: running the model doesn't take a lot of compute. Training

955
00:49:27,360 --> 00:49:31,080
Speaker 4: the model takes a lot of compute when we implement

956
00:49:31,200 --> 00:49:35,759
Speaker 4: talk about AI. Some of it's the fancy object thing. Oh,

957
00:49:35,760 --> 00:49:39,160
Speaker 4: I'm going to implement copilot and now people can write

958
00:49:39,200 --> 00:49:41,920
Speaker 4: emails for me. Everybody knows when copilot wrote my email

959
00:49:41,960 --> 00:49:44,360
Speaker 4: because it's way too polite and the punctuation is correct.

960
00:49:45,120 --> 00:49:47,759
Speaker 4: Does that make us any money? Probably not, you know,

961
00:49:47,840 --> 00:49:49,719
Speaker 4: but it's cool and yeah, that's so when you hear

962
00:49:49,760 --> 00:49:52,200
Speaker 4: most people say, oh, we're using AI, they just plugged

963
00:49:52,200 --> 00:49:55,160
Speaker 4: in copilot and paid somebody twenty million bucks a year

964
00:49:55,200 --> 00:50:00,520
Speaker 4: for licenses. Where it starts to make a difference is

965
00:50:00,520 --> 00:50:02,600
Speaker 4: when it can answer questions in an hurry for our

966
00:50:02,640 --> 00:50:06,960
Speaker 4: employees or our clients, and where it can automate process

967
00:50:07,719 --> 00:50:10,239
Speaker 4: the same way. We've been on this automation journey for

968
00:50:10,440 --> 00:50:13,040
Speaker 4: twenty years now, right starting with Internet and then kind

969
00:50:13,040 --> 00:50:16,719
Speaker 4: of micro services and applications. AI is just the next

970
00:50:16,760 --> 00:50:19,719
Speaker 4: stage of that. You know, we've got we over the

971
00:50:19,760 --> 00:50:24,480
Speaker 4: last ten years probably get thirty points of productivity efficiency

972
00:50:24,480 --> 00:50:28,479
Speaker 4: in our operating environments, probably less seven years. Actually AI

973
00:50:28,600 --> 00:50:31,839
Speaker 4: is going to give us another thirty and it's going

974
00:50:31,920 --> 00:50:34,520
Speaker 4: to come through time. But think of it as automation.

975
00:50:34,680 --> 00:50:37,480
Speaker 4: All it is is even when people talk about AI,

976
00:50:37,560 --> 00:50:39,439
Speaker 4: they'll say, oh, you know it's an AI model. It's

977
00:50:39,440 --> 00:50:42,520
Speaker 4: not an AI model. It's just a decision tree. You know.

978
00:50:42,560 --> 00:50:46,799
Speaker 4: It's automating a script and an answer, moving data in

979
00:50:46,880 --> 00:50:50,200
Speaker 4: a way where somebody doesn't have to swivel, you know,

980
00:50:50,280 --> 00:50:51,440
Speaker 4: pretty basic stuff.

981
00:50:51,719 --> 00:50:55,280
Speaker 3: What about on underwriting? Would you use AI to automate

982
00:50:55,320 --> 00:50:56,759
Speaker 3: that particular process.

983
00:50:57,200 --> 00:51:03,719
Speaker 4: It's actually not legally allowed today, So there's I mean,

984
00:51:03,760 --> 00:51:06,520
Speaker 4: it's truth in lending or something. What any of these

985
00:51:06,600 --> 00:51:09,799
Speaker 4: laws where if I tell you no, you applied for

986
00:51:09,840 --> 00:51:12,080
Speaker 4: your credit card. They just kept my rates at ten percent.

987
00:51:12,160 --> 00:51:13,719
Speaker 4: So I said no. I've got to give you the

988
00:51:14,080 --> 00:51:17,319
Speaker 4: three reasons you know of why I said no. With

989
00:51:17,360 --> 00:51:20,960
Speaker 4: an AI model, we look at a thousand variables, and

990
00:51:21,000 --> 00:51:24,920
Speaker 4: the weight of each of those variables is dependent upon

991
00:51:25,040 --> 00:51:28,759
Speaker 4: their interaction and your particular profile. So the weight of

992
00:51:28,840 --> 00:51:31,560
Speaker 4: rent might be very heavy for one person and not

993
00:51:31,600 --> 00:51:33,920
Speaker 4: for another based on the profile. I can't give you

994
00:51:34,000 --> 00:51:37,239
Speaker 4: three reasons. The model told me no, the model told

995
00:51:37,239 --> 00:51:41,279
Speaker 4: me yes. So what ends up happening today in credit decisions.

996
00:51:42,360 --> 00:51:45,440
Speaker 4: This is crazy, but it works. Is we'll set with

997
00:51:45,520 --> 00:51:49,000
Speaker 4: our normal criteria, this fight, go, this debt to income,

998
00:51:50,080 --> 00:51:52,440
Speaker 4: you know, whatever else you look at, we'll say yes

999
00:51:52,520 --> 00:51:54,600
Speaker 4: or no, and then if we tell you no, we'll

1000
00:51:54,680 --> 00:51:58,120
Speaker 4: use AI to try to tell you yes, because I

1001
00:51:58,120 --> 00:52:01,160
Speaker 4: can still use My original excuse is no. But if

1002
00:52:01,160 --> 00:52:03,799
Speaker 4: I turn you say yes after the fact, you're not

1003
00:52:03,840 --> 00:52:04,319
Speaker 4: mad at me.

1004
00:52:05,560 --> 00:52:08,879
Speaker 2: So the post is it's crazy, But the point is

1005
00:52:09,000 --> 00:52:12,600
Speaker 2: that legally speaking, if you say no, you have to

1006
00:52:12,600 --> 00:52:14,000
Speaker 2: be able to articulate a reason.

1007
00:52:14,120 --> 00:52:14,319
Speaker 1: Yeah.

1008
00:52:14,360 --> 00:52:16,719
Speaker 4: So what ends up happening is everybody sets this bar

1009
00:52:16,840 --> 00:52:19,200
Speaker 4: way too high. This is the one of these silly

1010
00:52:19,280 --> 00:52:22,320
Speaker 4: rules that needs to be looked at. I mean, we

1011
00:52:22,719 --> 00:52:25,799
Speaker 4: do use it's not so much AI. There's a lot

1012
00:52:25,840 --> 00:52:29,080
Speaker 4: of decision tree models on automation that help with underwriting,

1013
00:52:29,480 --> 00:52:32,560
Speaker 4: but it's not necessarily a machine learning application.

1014
00:52:33,040 --> 00:52:34,960
Speaker 2: Tracy and I, I mean, I find I get this

1015
00:52:35,080 --> 00:52:39,240
Speaker 2: idea of like AI as another form of workflow automation.

1016
00:52:39,400 --> 00:52:43,520
Speaker 2: Very intuitive. Yes, Tracy and I used to publish transcripts

1017
00:52:43,520 --> 00:52:45,800
Speaker 2: of the podcast, but it was a very labor intensive

1018
00:52:45,840 --> 00:52:48,040
Speaker 2: process to like clean it up, et cetera. And so

1019
00:52:48,080 --> 00:52:50,239
Speaker 2: I thought like AI was going to be the panacea.

1020
00:52:50,280 --> 00:52:52,960
Speaker 2: It's like clean this text. It just when we were

1021
00:52:53,000 --> 00:52:55,440
Speaker 2: doing it, it wasn't there yet. It was like creating

1022
00:52:55,520 --> 00:52:58,200
Speaker 2: fake It was creating sentences that weren't there. But I

1023
00:52:58,320 --> 00:53:01,920
Speaker 2: understand this intuition. AI can then be like trained on

1024
00:53:01,960 --> 00:53:05,120
Speaker 2: a repeatable process right now. And again, the things that

1025
00:53:05,160 --> 00:53:08,719
Speaker 2: we call AI which do today's large language models, those

1026
00:53:08,760 --> 00:53:13,040
Speaker 2: workflow automations, like are they are there certain workflow automations

1027
00:53:13,080 --> 00:53:16,120
Speaker 2: that today workflow processes that you could say we've been

1028
00:53:16,120 --> 00:53:17,880
Speaker 2: able to automate them in a way we couldn't have

1029
00:53:17,920 --> 00:53:18,760
Speaker 2: five years ago.

1030
00:53:19,040 --> 00:53:22,359
Speaker 4: Oh absolutely, But let me let me just give it

1031
00:53:23,320 --> 00:53:26,360
Speaker 4: kind of some scope and some numbers and what's actually happening.

1032
00:53:26,400 --> 00:53:29,719
Speaker 4: And I we have a pretty clean TEX stack. So

1033
00:53:29,840 --> 00:53:32,319
Speaker 4: maybe we're a little further ahead at this than some

1034
00:53:32,520 --> 00:53:35,680
Speaker 4: but we have. We focused on a billion four of

1035
00:53:35,719 --> 00:53:40,480
Speaker 4: addressable spend inside of our care centers and our operating environments.

1036
00:53:40,840 --> 00:53:44,160
Speaker 4: This is just solving problems for customers moving loan all

1037
00:53:44,160 --> 00:53:47,000
Speaker 4: that stuff. A billion four we spend. We've come up

1038
00:53:47,000 --> 00:53:51,040
Speaker 4: with one hundred and seventy one different ways to use

1039
00:53:51,120 --> 00:53:56,360
Speaker 4: AI in reducing that spend, and the addressable piece of

1040
00:53:56,400 --> 00:53:58,680
Speaker 4: the billion, four we think is probably forty percent of it.

1041
00:53:59,719 --> 00:54:01,920
Speaker 4: Of the one hundred and seventy one use cases, we

1042
00:54:02,040 --> 00:54:05,640
Speaker 4: prioritize five that are live and so what do you know,

1043
00:54:05,680 --> 00:54:07,839
Speaker 4: what does one of those look like? For example, so

1044
00:54:07,920 --> 00:54:10,759
Speaker 4: imagine inside of our wealth business. You know, we got

1045
00:54:10,800 --> 00:54:14,440
Speaker 4: a bunch of trust lawyers. So my grandfather, it's actually

1046
00:54:14,440 --> 00:54:16,680
Speaker 4: with P and C. My grandfather created this small little

1047
00:54:16,719 --> 00:54:21,000
Speaker 4: trust skipt generations and it's written beautiful. It's handwritten on

1048
00:54:21,080 --> 00:54:24,760
Speaker 4: like three pages. It was my greatfoot grandfather. Some lawyer

1049
00:54:24,800 --> 00:54:26,840
Speaker 4: every month has to look at that thing and say,

1050
00:54:27,320 --> 00:54:30,400
Speaker 4: you know this qualifieser doesn't. We're going to do this distribution.

1051
00:54:30,560 --> 00:54:33,279
Speaker 4: All of those things are manual. Now just read them

1052
00:54:33,320 --> 00:54:36,440
Speaker 4: into the document writer, and all of the things that

1053
00:54:36,440 --> 00:54:40,200
Speaker 4: are mechanical outcomes from those millions of pages of trust

1054
00:54:40,200 --> 00:54:44,000
Speaker 4: documents are right in front of you, right payment on

1055
00:54:44,040 --> 00:54:49,280
Speaker 4: this state, hear the beneficiaries. It just makes it real simple.

1056
00:54:49,440 --> 00:54:52,560
Speaker 4: So document reading, expression of what's in documents on the

1057
00:54:52,560 --> 00:54:55,239
Speaker 4: basic stuff. You know, now I don't have to just

1058
00:54:55,360 --> 00:54:58,600
Speaker 4: go and search and find and human error that that

1059
00:54:58,640 --> 00:55:02,359
Speaker 4: stuff will make a difference. I worry we too much

1060
00:55:02,400 --> 00:55:05,719
Speaker 4: focus on the cool fun toy chatsy TP. I mean

1061
00:55:05,719 --> 00:55:07,399
Speaker 4: this is you know, it's a new Google search. It's

1062
00:55:07,440 --> 00:55:09,920
Speaker 4: cool to give me the answer that that isn't going

1063
00:55:09,960 --> 00:55:12,640
Speaker 4: to make the world better if that's all it's used for.

1064
00:55:13,360 --> 00:55:16,000
Speaker 3: How did you get a clean tech stack? Because this

1065
00:55:16,120 --> 00:55:18,880
Speaker 3: is something that also goes hand in hand with bank consolidation.

1066
00:55:19,000 --> 00:55:21,279
Speaker 3: You always hear that, you know, banks end up with

1067
00:55:21,360 --> 00:55:25,760
Speaker 3: this tangled mess of technological systems, maybe some that include

1068
00:55:26,160 --> 00:55:30,840
Speaker 3: cobble or like old languages and things like that, coyl.

1069
00:55:30,960 --> 00:55:33,480
Speaker 4: You can't even Yeah, you weren't even born yet when

1070
00:55:33,480 --> 00:55:39,240
Speaker 4: we were programming. So how do we so honest answer,

1071
00:55:39,960 --> 00:55:42,279
Speaker 4: right in the middle of the financial crisis, right, we

1072
00:55:42,440 --> 00:55:45,640
Speaker 4: merge with National City under duress from both firms, kind

1073
00:55:45,680 --> 00:55:51,400
Speaker 4: of a regulatory wedding. Neither of us had the capacity

1074
00:55:52,200 --> 00:55:55,800
Speaker 4: to run a larger firm. I mean, I distinctly remember,

1075
00:55:57,560 --> 00:55:59,920
Speaker 4: you know, talking to the combined organization. We have to

1076
00:56:00,080 --> 00:56:04,080
Speaker 4: rethink how we run something you can't manage by walking

1077
00:56:04,120 --> 00:56:09,160
Speaker 4: around anymore. And neither tech stack could survive on its own.

1078
00:56:09,520 --> 00:56:14,279
Speaker 4: And so we saved less money than we said we

1079
00:56:14,320 --> 00:56:17,440
Speaker 4: would in the announcement, or actually we saved what we

1080
00:56:17,480 --> 00:56:21,760
Speaker 4: said we would save, but quietly we reinvested two billion

1081
00:56:21,800 --> 00:56:26,760
Speaker 4: bucks a year for ten years. Wow, So literally all new.

1082
00:56:27,000 --> 00:56:30,200
Speaker 4: We went from single server stack application. You know, so

1083
00:56:30,520 --> 00:56:34,600
Speaker 4: this computer runs that application, and each computer is different

1084
00:56:34,640 --> 00:56:38,160
Speaker 4: because whoever the application owner was went to dinner with

1085
00:56:38,239 --> 00:56:41,359
Speaker 4: Dell that day or HP the next day. We had

1086
00:56:41,400 --> 00:56:44,840
Speaker 4: eleven data centers. None of it was in a virtual environment.

1087
00:56:44,960 --> 00:56:50,320
Speaker 4: So building our own first cloud environment and then secondly

1088
00:56:50,440 --> 00:56:54,040
Speaker 4: writing everything in cloud language in a micro service application,

1089
00:56:54,200 --> 00:56:57,160
Speaker 4: so it's cloud native. That's forever. But we spent a

1090
00:56:57,239 --> 00:57:00,880
Speaker 4: ton of dough and just have invested in that, invested

1091
00:57:00,880 --> 00:57:02,880
Speaker 4: in got to a place where our tech stack is

1092
00:57:02,920 --> 00:57:05,319
Speaker 4: as modern as any fintech you'll find out there.

1093
00:57:05,719 --> 00:57:08,279
Speaker 2: All right, Oh, Bill Dems is fun. This is a

1094
00:57:08,320 --> 00:57:10,399
Speaker 2: lot of fun. Thank you so much for taking the time.

1095
00:57:10,480 --> 00:57:13,520
Speaker 2: We had a lot of interested Yeah, that was great,

1096
00:57:13,640 --> 00:57:15,520
Speaker 2: It was great. Really appreciate you coming out our life.

1097
00:57:16,040 --> 00:57:30,560
Speaker 2: Good to be here, Thank you, Tracy. I really enjoyed

1098
00:57:30,600 --> 00:57:35,040
Speaker 2: that conversation. There was I learned a lot about banking conversation.

1099
00:57:35,120 --> 00:57:37,560
Speaker 2: We hit a lot in that, but I just wanted

1100
00:57:37,560 --> 00:57:39,160
Speaker 2: to keep going. There are so many interesting angles.

1101
00:57:39,240 --> 00:57:41,320
Speaker 3: Bill actually answered the questions.

1102
00:57:41,080 --> 00:57:43,520
Speaker 2: Which cannot be said for every CEO. No, I would

1103
00:57:43,560 --> 00:57:46,240
Speaker 2: say most of our guests are very good at answering questions.

1104
00:57:46,440 --> 00:57:49,480
Speaker 2: Not every CEO is great at always answering questions. But

1105
00:57:49,560 --> 00:57:51,280
Speaker 2: I was like, oh, I'm like learning each other with

1106
00:57:51,360 --> 00:57:52,840
Speaker 2: each one of these things. Let's keep going.

1107
00:57:52,960 --> 00:57:55,400
Speaker 3: Also, genuinely, he seemed to have a good handle on

1108
00:57:55,520 --> 00:57:59,160
Speaker 3: like the granular practical aspects on running a bank. So

1109
00:57:59,280 --> 00:58:01,800
Speaker 3: I was really interest said to hear how they redesigned

1110
00:58:02,000 --> 00:58:05,640
Speaker 3: their tech after the financial crisis. I guess they got

1111
00:58:05,640 --> 00:58:08,760
Speaker 3: a head start on everyone else. And then also the

1112
00:58:08,800 --> 00:58:12,840
Speaker 3: prepositioning of collateral at the discount window. I knew that

1113
00:58:12,960 --> 00:58:16,360
Speaker 3: the FED had been encouraging banks to do it. I

1114
00:58:16,400 --> 00:58:19,160
Speaker 3: did not realize that you had to send the actual

1115
00:58:19,360 --> 00:58:21,840
Speaker 3: mortgage docks if you borrow against those.

1116
00:58:22,440 --> 00:58:27,920
Speaker 2: It is crazy. How much is not just done automatically electronically, right?

1117
00:58:28,000 --> 00:58:31,320
Speaker 2: And here is I didn't ask it, but I was

1118
00:58:31,400 --> 00:58:34,160
Speaker 2: going to ask, does blockchain solve this question? But the

1119
00:58:34,240 --> 00:58:36,720
Speaker 2: idea of that, like, okay, all of these things really

1120
00:58:36,800 --> 00:58:41,760
Speaker 2: are electronic ish, although there's clearly this physical component. But

1121
00:58:41,840 --> 00:58:45,840
Speaker 2: the idea that here is something which is theoretically monetizable,

1122
00:58:45,920 --> 00:58:50,320
Speaker 2: which has liquid value, is a liquid instrument. But because

1123
00:58:50,360 --> 00:58:53,960
Speaker 2: of the way that the system is structured, cannot actually

1124
00:58:54,000 --> 00:58:58,280
Speaker 2: be liquidated or turned into liquid as it's immediately if

1125
00:58:58,320 --> 00:59:01,560
Speaker 2: it had it were not prepositioned and et cetera. That

1126
00:59:01,680 --> 00:59:04,400
Speaker 2: actually does still seem a little bit wild to me

1127
00:59:04,520 --> 00:59:05,440
Speaker 2: that that is the case.

1128
00:59:05,680 --> 00:59:09,320
Speaker 3: I always thought that mortgage assignations were like one use

1129
00:59:09,360 --> 00:59:12,320
Speaker 3: case for blockchain, right right, Yeah, yeah, yeah, you could

1130
00:59:12,400 --> 00:59:15,360
Speaker 3: reasonably see blockchain, you know, helping out with that problem

1131
00:59:15,520 --> 00:59:18,880
Speaker 3: versus I don't know, tracking the origins of lettuce and

1132
00:59:18,920 --> 00:59:21,160
Speaker 3: things like that always seemed kind of ridiculous.

1133
00:59:21,200 --> 00:59:23,360
Speaker 2: Well, it seems like one of these things, which is

1134
00:59:23,400 --> 00:59:27,880
Speaker 2: in banking and again is like integrated databases. So there's

1135
00:59:27,920 --> 00:59:30,280
Speaker 2: like'll be a central bank database, there will be a

1136
00:59:30,320 --> 00:59:33,600
Speaker 2: home loan bank database, and the regional bank and the

1137
00:59:33,600 --> 00:59:37,040
Speaker 2: bank database, right, And the question is like, why can't

1138
00:59:37,040 --> 00:59:39,720
Speaker 2: they just all see talk to each other, you see

1139
00:59:39,760 --> 00:59:43,600
Speaker 2: the relevant parts of the database, right, And I suppose

1140
00:59:43,840 --> 00:59:46,920
Speaker 2: that's easier said than done. I really liked hearing him

1141
00:59:46,960 --> 00:59:51,760
Speaker 2: talk too about practical applications of AI, which again maybe

1142
00:59:51,800 --> 00:59:54,200
Speaker 2: feels a little different than some of our conversations last year.

1143
00:59:54,320 --> 00:59:58,880
Speaker 2: It feels like now there's more articulated examples of this

1144
00:59:59,000 --> 01:00:02,200
Speaker 2: is a sort of labor saving, time saving technology so

1145
01:00:02,520 --> 01:00:04,280
Speaker 2: that's another thing I think we have to pay more

1146
01:00:04,280 --> 01:00:05,800
Speaker 2: attention to in twenty twenty six.

1147
01:00:06,080 --> 01:00:09,320
Speaker 3: Also, the database issue in the context of AI is

1148
01:00:09,400 --> 01:00:12,200
Speaker 3: kind of interesting because we were talking about how so

1149
01:00:12,400 --> 01:00:15,560
Speaker 3: much of programming nowadays, so much of the tech issues

1150
01:00:15,600 --> 01:00:19,600
Speaker 3: are those coordination problems. Yeah, right, And so even if

1151
01:00:19,600 --> 01:00:23,680
Speaker 3: we have something like Claude we recorded that episode recently,

1152
01:00:24,040 --> 01:00:27,439
Speaker 3: even if you have Claude writing software, it doesn't necessarily

1153
01:00:28,120 --> 01:00:31,040
Speaker 3: solve that coordination issue totally.

1154
01:00:31,920 --> 01:00:35,040
Speaker 2: And then you know, bank branches, how crucial they are.

1155
01:00:35,160 --> 01:00:36,600
Speaker 3: Yeah, that was surprising.

1156
01:00:36,800 --> 01:00:38,720
Speaker 2: I had heard that. You know, there's and again it

1157
01:00:38,760 --> 01:00:41,240
Speaker 2: may be just like one of these sort of things

1158
01:00:41,280 --> 01:00:44,000
Speaker 2: people say, like people are less likely to switch banks

1159
01:00:44,000 --> 01:00:46,440
Speaker 2: than they are to get a divorce, and so therefore

1160
01:00:47,040 --> 01:00:49,760
Speaker 2: it still makes sense to build out a physical branch.

1161
01:00:50,040 --> 01:00:52,920
Speaker 2: But like the strategy of that, and think about where

1162
01:00:53,040 --> 01:00:55,840
Speaker 2: and how the fact that they're building hundreds of branches

1163
01:00:55,920 --> 01:00:58,720
Speaker 2: still even if the total number of bank branches is

1164
01:00:58,760 --> 01:01:03,760
Speaker 2: actually declining, that's just like a super interesting, real part

1165
01:01:03,800 --> 01:01:04,440
Speaker 2: of the business.

1166
01:01:04,720 --> 01:01:07,400
Speaker 3: It is true that I've had the same bank since college.

1167
01:01:07,680 --> 01:01:11,000
Speaker 3: I don't know about you, uh, and I hate.

1168
01:01:10,800 --> 01:01:14,400
Speaker 2: Them nice, But since I moved to New Yeah, since

1169
01:01:14,400 --> 01:01:16,080
Speaker 2: I moved to New York. I think I've had so

1170
01:01:16,200 --> 01:01:19,280
Speaker 2: that's twenty one years. So yeah, basically, yeah, one bank.

1171
01:01:19,280 --> 01:01:19,960
Speaker 2: It's kind of wild.

1172
01:01:20,120 --> 01:01:23,520
Speaker 3: I'm going to switch. I'm going to prove the anecdote run.

1173
01:01:23,560 --> 01:01:24,800
Speaker 2: You're just going to switch for the hell of it.

1174
01:01:24,960 --> 01:01:27,480
Speaker 3: Yeah, absolutely, Okay, shall we leave it there?

1175
01:01:27,560 --> 01:01:28,440
Speaker 2: Yeah, let's leave it there.

1176
01:01:28,560 --> 01:01:31,440
Speaker 3: This has been another episode of the Audthlots podcast. I'm

1177
01:01:31,480 --> 01:01:34,640
Speaker 3: Tracy Alloway. You can follow me at Tracy Alloway.

1178
01:01:34,320 --> 01:01:37,120
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.

1179
01:01:37,360 --> 01:01:40,520
Speaker 2: Follow our producers Kerman Rodriguez at Kerman Arman, dash Hall,

1180
01:01:40,560 --> 01:01:43,440
Speaker 2: been At at Dashbot at keil Brooks at Kilbrooks. For

1181
01:01:43,440 --> 01:01:45,880
Speaker 2: more odd Laws content, go to Bloomberg dot com slash

1182
01:01:45,960 --> 01:01:48,840
Speaker 2: odd Lots for a daily newsletter and all of our episodes,

1183
01:01:49,040 --> 01:01:50,880
Speaker 2: and you can chat about all of these topics twenty

1184
01:01:50,920 --> 01:01:54,320
Speaker 2: four to seven in our discord Discord dot gg slash

1185
01:01:54,320 --> 01:01:55,640
Speaker 2: odd Lots.

1186
01:01:55,200 --> 01:01:57,400
Speaker 3: And if you enjoy odd Lots, if you like it

1187
01:01:57,440 --> 01:01:59,960
Speaker 3: when we look into the business models of banks, then

1188
01:02:00,040 --> 01:02:03,280
Speaker 3: please leave us a positive review on your favorite podcast platform.

1189
01:02:03,640 --> 01:02:06,440
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1190
01:02:06,480 --> 01:02:09,920
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1191
01:02:09,960 --> 01:02:12,040
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1192
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