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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. Hello and welcome to

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Speaker 1: another episode of the All Thoughts Podcast.

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Speaker 2: I'm Tracy Alloway and I'm Joe Wisenthal.

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Speaker 1: Joe, I feel like I start every private credit episode

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Speaker 1: with the same point, but I mean, private credit it's

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Speaker 1: everywhere right now. I think I counted like dozens and

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Speaker 1: dozens of stories on private credit that came out just

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Speaker 1: on the Bloomberg in the past week.

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Speaker 3: There's two funny things that are going on. Which is

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Speaker 3: one private credit. So these non bank entities providing loans,

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Speaker 3: et cetera wanting to get into credit, and there's more

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Speaker 3: and more about that every day. And then there's banks

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Speaker 3: wanting to get more and more into private credit, which

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Speaker 3: is his own thing of Okay, you're still at the bank,

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Speaker 3: but you're doing it in some sort of bald and

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Speaker 3: she'd structure that resembles private credit, and what's up with that?

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Speaker 1: What's up with that? Indeed, this is the what's up

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Speaker 1: with that? Episode? I'm so glad you asked that question.

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Speaker 1: But we're going to be talking about the relationship between

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Speaker 1: banks and private credit because the other thing that's been

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Speaker 1: happening is every time we talk to a bank or

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Speaker 1: a private credit entity on this show and we ask

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Speaker 1: about the relationship between regulated banks and non banks, you

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Speaker 1: get this really diplomatic, kind of awkward answer, like while

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Speaker 1: we view our bank partners as opportunities and no one

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Speaker 1: will really explain how they actually feel about each other totally.

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Speaker 2: You know.

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Speaker 3: The one other thing before we get into it that

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Speaker 3: I think about it a lot is I look at

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Speaker 3: the rise of private credit and there is a big

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Speaker 3: part of me that says, this is what regulatory success

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Speaker 3: looks like. This is what post DoD Frank's success looks like.

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Speaker 3: That there is more of this risk taking, right, this

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Speaker 3: was the intent, Yeah, happening outside of the deposit taking

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Speaker 3: banking institution. On the other hand, if a lot of

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Speaker 3: the leverage for private credit and a lot of these

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Speaker 3: relationships is being plied by banks and so forth, then

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Speaker 3: it makes me wonder, did we actually extricate the risk.

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Speaker 1: Or in the air we just put a wrapper on it, we.

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Speaker 3: Put a rapper on it. In the end, does all

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Speaker 3: financial risk readown back to the banking system?

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Speaker 1: That's exactly it. And I got to say, you know,

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Speaker 1: there is a lot of discourse from which we can

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Speaker 1: pull from a lot of historical analogies, because of course,

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Speaker 1: bank disintermediation is not a new thing. It's basically been

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Speaker 1: happening for as long as we've had banks. And if

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Speaker 1: I think back to like two big moments in the

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Speaker 1: process of bank disintermediation, it has to be the invention

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Speaker 1: of the junk bond market in the nineteen eighties, securitization.

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Speaker 1: Also in the nineteen eighties and nineteen nineties, peer to

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Speaker 1: peer lending. That was a fun one, remember that.

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Speaker 3: Well the other thing too, you know, it just occurred

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Speaker 3: to me. And bear Stearns was not a retail deposit

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Speaker 3: taking institution. But part of why they blew up is

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Speaker 3: like they had these in house hedge funds, right, and

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Speaker 3: so even this idea of hedge funds and non bank

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Speaker 3: entities sort of existing within more larger, traditional regulated financial

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Speaker 3: institutions is not that new. That was a story of

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Speaker 3: the Great Financial Crisis.

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Speaker 1: That's exactly right. So I'm very happy to say this

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Speaker 1: is our banks and private credit basically Frenemies episode. We're

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Speaker 1: going to be speaking with really the perfect guest. It's

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Speaker 1: someone that I've known for a long time and we've

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Speaker 1: actually had him on the podcast before. But I don't

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Speaker 1: think you were there. I promise you are really going

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Speaker 1: to love this. We're going to be speaking with Hugh

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Speaker 1: van Steinis. He is the vice chair at Oliver Wyman

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Speaker 1: and also the former global head of Banking Research over

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Speaker 1: at Morgan Stantley. That's where I got to know his

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Speaker 1: during the depths of the euro Zone financial crisis when

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Speaker 1: I was on FT Alpha bel He's also formerly an

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Speaker 1: advisor to Mark Karney at the BOE. I think he

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Speaker 1: won like a series of research awards at various points

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Speaker 1: in his career, but really one of the smartest guys

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Speaker 1: I know when it comes to banks and financial So, Hugh,

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Speaker 1: thank you so much for coming on.

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Speaker 2: All thoughts. Tracy, thanks so much for having me on.

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Speaker 1: We are very excited. First of all, maybe let's just

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Speaker 1: start with the basic question, because everyone seems to have

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Speaker 1: different opinions, different numbers around this, But how big is

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Speaker 1: private credit at the moment compared to the traditional banking system.

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Speaker 4: Oh, I mean, honestly, it's pretty tiny. So the official

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Speaker 4: stats are about one point seven trillion, and that's by Prequeen,

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Speaker 4: the company that Black Croc bought recently. That number, though

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Speaker 4: doesn't include insurers giving direct mandates to the private credit firms.

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Speaker 4: So I think it's probably closer to two and a

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Speaker 4: half to three trillion, which is a drop in the

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Speaker 4: ocean to what investment grade bond markets nine trillion banking

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Speaker 4: assets in Europe at thirty two trillion. These are really

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Speaker 4: relatively small numbers, and so I think that's why many

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Speaker 4: of these firms think they've got a long run way

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Speaker 4: still to grow.

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Speaker 3: So why do we care. If not because they're so big,

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Speaker 3: it's just because it's growing.

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Speaker 4: Fairst Well, it's also because they're eating away at bank earnings,

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Speaker 4: and I think that's where, you know, if I think

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Speaker 4: about our conversations with bank CEOs and CFOs, and I

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Speaker 4: literally just had one before coming on your show, they're

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Speaker 4: worried about how much of their juice is being gobbled up.

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Speaker 4: And I think that that's you know, that's why in way,

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Speaker 4: Tracy's Right has gone from sort of counterparts to frenemies.

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Speaker 4: And one way to think about it is that we

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Speaker 4: had a very unusual macro period. As you've spoken about

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Speaker 4: many times, during twenty twenty three, private credit players wrote

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Speaker 4: about ninety percent of all leverage loans, and they're also

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Speaker 4: doing really well in direct lending. And so they're looking

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Speaker 4: for the next avenue of growth. And one theme that

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Speaker 4: we come up across a lot is about asset back lending,

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Speaker 4: so in other words, financing i'd know, aviation, or auto

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Speaker 4: loans or even royalties. That's a five and a half

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Speaker 4: trillion dollar market in the States. Private credit probably has

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Speaker 4: less than five percent share, and they're really looking to

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Speaker 4: mine this seam. And so if you're a banker, thinking,

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Speaker 4: these guys are now after durre investment grade assets, not

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Speaker 4: just are the high old assets.

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Speaker 1: So talk to us about how we got to this

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Speaker 1: point because Joe correctly, I think, attributed this to a

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Speaker 1: lot of the post two thousand and eight redesign of

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Speaker 1: the financial system regulation. And I mean, this is what

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Speaker 1: we wanted, right, We wanted the riskiest stuff, the riskiest

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Speaker 1: activity to be pushed away from regulated banks and into

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Speaker 1: I know they have the nefarious name of shadow banks,

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Speaker 1: but you know, mostly we're talking about like a business

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Speaker 1: development company or a direct lender or someone like that.

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Speaker 4: No, look, I think that's right. So looks if you

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Speaker 4: take it since the financial crisis, where we changed the

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Speaker 4: rigs for the banking system, a lot more capital, a

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Speaker 4: lot less shortened the asset liability duration, a mismatch that

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Speaker 4: went beyond an elastic limit into the financial crisis. So

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Speaker 4: these private credit firms have created just over a trillion

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Speaker 4: dollar parallel system to lend to corporate America and parts

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Speaker 4: of corporate Europe. And it's around leverage lending its areas

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Speaker 4: which were either too risky or in some cases where

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Speaker 4: the FED put in limits on how many leverage loans

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Speaker 4: or what was the maximum you know, multiple of level

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Speaker 4: that a bank loan could take on. And so these

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Speaker 4: loans are being pushed outside of the banks. The other area, though,

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Speaker 4: where private credit has been very active to is mid market.

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Speaker 4: So let's say a loan between let's say thirty million

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Speaker 4: dollars to seventy five million dollars. That's a narrow where

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Speaker 4: for the top six banks, this is just too small

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Speaker 4: fry for them to get excited. And therefore so there

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Speaker 4: was us kind of a missing piece, which you know,

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Speaker 4: the private credit firms picked up the crumbs which are

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Speaker 4: left on the table by the bank. So you're right,

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Speaker 4: I think the regulators push this. I think where we're

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Speaker 4: going now is probably to the next level. And so

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Speaker 4: what I the way I think about it is that

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Speaker 4: we are now retranching the banking system where the banks

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Speaker 4: are laying off the junior risk to private credit, and

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Speaker 4: that's allowing them to optimize their capital, but quite frankly

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Speaker 4: also lend more. And so what's really interesting for me

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Speaker 4: is there's, like everything in life, people see things as

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Speaker 4: an opportunity or a threat. The top banks and CEOs

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Speaker 4: that I talk to are now saying, actually, private credit

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Speaker 4: allows me to recycle risk more quickly, I can lend more.

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Speaker 4: And then there's a whole bunch of banks who are

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Speaker 4: just sitting licking their wounds, going I'm not sure how

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Speaker 4: I can do this. So I think there is a

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Speaker 4: little bit symbiosis now between the banks and private credit.

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Speaker 3: SORR explain that a little bit further, at least on

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Speaker 3: the opportunity side, when they can recycle their capital first,

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Speaker 3: just sort of walk us through.

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Speaker 4: The Yeah, so Joe, let's take one of the top

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Speaker 4: top us banks or your banks. So there are three

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Speaker 4: ways they can lay off risk. The first would be

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Speaker 4: to say I will seed the loans that I don't

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Speaker 4: really want to write to give to a third party.

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Speaker 4: So in a way, what you've seen with Apollo and

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Speaker 4: City Group is the is the leverage lending or Brookfield

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Speaker 4: with Lloyd's in Europe. Again, it was around leverage lending.

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Speaker 4: So it's stuff that they didn't really want to do,

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Speaker 4: but they can arrange. They can get all sorts of

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Speaker 4: origination fees, and then they can also keep the relationship.

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Speaker 4: The second is if they let's say, originate a loan,

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Speaker 4: they then pass that up, maybe get one hundred loans

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Speaker 4: on hundred twenty loans, and then do a synthetic risk

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Speaker 4: transfer around this, in other words, start to basically which

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Speaker 4: I think you talked about two months ago on your show.

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Speaker 4: So in that case, think about a bell curve, you're

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Speaker 4: going to ensure the bottom ten percent, you're going to

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Speaker 4: take off the tail, and from a bank capital point

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Speaker 4: of view, that dramatically optimizes your capital at risk. So

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Speaker 4: the FED only permission this is about nine months ago.

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Speaker 4: You've already seen Morman, Sandy, Golden Sacks a number of

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Speaker 4: firms start to do these synthetic risk transfers. That space,

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Speaker 4: I think is going to grow really strongly because for

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Speaker 4: the largest firms it allows them to lend and then

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Speaker 4: do them. And then the third is then the more

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Speaker 4: you know, there's some more complexities as well around you know,

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Speaker 4: how else you can sort of lay off the risk.

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Speaker 1: So one thing I'm always asking on the show is

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Speaker 1: how these conversations begin, because you know, I honestly have

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Speaker 1: no idea. Like, clearly there's a lot of partnering that's

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Speaker 1: happening between banks and private credit at the moment, but

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Speaker 1: when did that start? In your mind, what was the

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Speaker 1: first kind of big, notable instance of a bank teaming

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Speaker 1: up with some sort of private credit entity.

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Speaker 2: Oh, that's a good question.

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Speaker 4: So, as you were sort of hinted earlier on Tracy,

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Speaker 4: often in life a history of these things is far

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Speaker 4: longer than we like to believe. So just like actually

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Speaker 4: nineteen seventy three was the peak of bank lending as

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Speaker 4: a percentage of lending to corporates. I mean, it's what,

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Speaker 4: it's fifty years since that peak. So you're right, some

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Speaker 4: of these partnerships are actually about fifteen years old. One

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Speaker 4: or two of them predate the financial crisis. But if

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Speaker 4: you think about today, in the twelve months to September fourteen,

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Speaker 4: banks tied up deals with private credit, and in the

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Speaker 4: twelve months prior it was only two So it's basically

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Speaker 4: about a year ago. Suddenly it snapped. Now why is that.

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Speaker 4: I think it's because the private credit firms, particularly the

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Speaker 4: top ten, felt that started to max out of you know,

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Speaker 4: leverage lending or direct lending. But I think the subplot

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Speaker 4: is much more Shakespeare. I think there's a really interesting subplot,

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Speaker 4: which which is more of the top ten private credit

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Speaker 4: firms and now getting the assets from insurers. So take

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Speaker 4: yesterday we had the Blackstone results. Half their assets now

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Speaker 4: now come from insurance companies. Insurers can only invest investment grade.

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Speaker 4: So if you think about it from the point of

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Speaker 4: view the private credit player, they are structurally lowering their

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Speaker 4: cost of capital, which means that they can then go

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Speaker 4: after investment grade assets on the bank's balance sheets.

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Speaker 2: And so that's subplot.

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Speaker 4: I mean, I think now of the top ten firms

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Speaker 4: on my numbers, about forty percent of the assets come

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Speaker 4: from insurers. They become much more relevant to compete for

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Speaker 4: the investment grade pieces on the banks. And I think

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Speaker 4: that's what you know, whether it's the Barclays deal with Blackstone,

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Speaker 4: whether it's oak Tree with the SoC Gen, whether it's

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Speaker 4: City with Apollo. In a way, the private credit is

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Speaker 4: able to nibble away at more assets than they could

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Speaker 4: in the past.

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Speaker 3: I'm going to back up and ask the dumb question.

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Speaker 3: I'm like, oh, I think maybe listeners are a lot

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Speaker 3: of clarification, but it's actually just me. What's the difference

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Speaker 3: between leverage lending and direct lending?

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Speaker 1: Again?

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Speaker 4: Oh, look, the this is one of these where the

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Speaker 4: MAENDC clature is pretty poor. Okay, I mean it's really poor,

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Speaker 4: and it's pretty blurry. I think the way they think

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Speaker 4: most of them would think about its direct lending is

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Speaker 4: I'm lending to a mid market company, you know, thirty

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Speaker 4: million dollars to one hundred million dollars, the kind of

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Speaker 4: a mid market finance, whereas lever lending is going to

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Speaker 4: be acquisition related finance.

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Speaker 2: But okay, got it? Yeah, yeah, Joe, it's a it's

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Speaker 2: a it's a blurry fandomic.

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Speaker 3: No, the acquisition related finance. That makes a lot of

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Speaker 3: sense to me.

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Speaker 1: So I'm going to go back to that conversation point

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Speaker 1: and ask, Okay, so you know, a bank approaches a

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Speaker 1: private credit lender, or a private credit lender approaches a

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Speaker 1: bank and says, hey, we need to do something in

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Speaker 1: this environment. You know, there's a lot of demand. I've

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Speaker 1: got a bunch of insurance companies that are interested whatever,

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Speaker 1: how do they go about identifying what exactly they're going

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Speaker 1: to do and which particular assets are loans might be

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Speaker 1: realistic for this kind of partnership.

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Speaker 4: Oh, it's a that's a great question, Tracy. So and

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Speaker 4: it cuts look some of these relations These firms are

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Speaker 4: being counterparts of the banks for many years, so there's

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Speaker 4: a degree of relationship, even if maybe historically being a

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Speaker 4: little bit antagonistic, and certainly one of the leaving product

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Speaker 4: credit firms you know, has a swear box for every

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Speaker 4: time they talk about a counterpart rather than a partner

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Speaker 4: these days, how.

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Speaker 1: Much do you have to put in? Is it like

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Speaker 1: five thousand bucks or five dollars?

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Speaker 4: Well, it probably should be five thousand, but I think

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Speaker 4: it's actually got it's the charity. It's a charity pot.

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Speaker 4: And so if we think about those deals, half have

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Speaker 4: been around asset back lending and half around the you know,

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Speaker 4: the overflow, the leverage finance or the more levered stuff.

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Speaker 4: So I think that what the private credit companies are

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Speaker 4: doing is very shrewdly going through that. They're almost doing

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Speaker 4: like my old job being a banks on list. They're

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Speaker 4: looking at a bank and saying where is capital constrained

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Speaker 4: at the end of the day, particularly in Europe, but

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Speaker 4: even in the state and even particularly think about US

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Speaker 4: regional banks or some of the European banks. They've become optimizers.

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Speaker 4: They're optimizing for a cost efficiency, capital efficiency and revenues.

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Speaker 4: And in that mindset of optimization, they're always looking to

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Speaker 4: try and lay off risk. And so the private credit

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Speaker 4: company often says, well, look, I see your capital constrained.

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Speaker 2: You need to grow.

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Speaker 4: An example would be let's say Blackstone with Barclays, they

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Speaker 4: want to grow their credit card business, but you know,

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Speaker 4: but equally want to keep lots of money in there

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Speaker 4: in their vestment bank. By partnering up, they can now

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Speaker 4: fuel the growth of the credit card business in a

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Speaker 4: way they couldn't do before, or did at least didn't

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Speaker 4: believe they could before. So I think this is, you know,

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Speaker 4: in a very constrained world. In fact, I was an

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Speaker 4: event last week with a bunch of investors and private

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Speaker 4: credit firms, and one of the investors said, well, look,

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Speaker 4: there is not enough capital for any bank to put

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Speaker 4: capital behind an acronym. You know that that's just space

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Speaker 4: is gone. You need to find partners and so I

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Speaker 4: think they're forensic. They're hiring people to do bank's analysts

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Speaker 4: for them, and then I look in to try and

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Speaker 4: create a solution because I said, the top ten firms

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Speaker 4: feel their origination constraint and so they need the access

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Speaker 4: to more assets.

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Speaker 3: So in a situation like a credit card deal, what

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Speaker 3: the bank wants and the bank still has, and the

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Speaker 3: bank will probably still have, is that brand, that relationship,

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Speaker 3: that retail distribution network and so forth, and then the

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Speaker 3: private credit entity just allows them to keep growing these

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Speaker 3: lines and that they're presumably other lines without impairing their

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Speaker 3: balance sheet.

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Speaker 2: Exactly.

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Speaker 4: It's about optimizing the castle as well, because you know

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Speaker 4: it's at the end of the day, if you take

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Speaker 4: off the riskiest piece, will take off the entire slice.

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Speaker 4: Then you can just grow much faster.

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Speaker 3: That's very helpful. Can you go further in talking about

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Speaker 3: the role of insurance and all this, because one of

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Speaker 3: the things that I still find actually completely strange is

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Speaker 3: that we have these gigantic financial entities called insurance companies

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Speaker 3: their bad meth and they're important all kinds of areas,

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Speaker 3: and no one ever talks about insurance companies Either's like

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Speaker 3: that you don't really see them in the media the

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Speaker 3: same way you see banks.

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Speaker 1: It's very stress that and accounting. Yea, like the two

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Speaker 1: missing like major ingredients of financial.

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Speaker 3: Financial of the financial ecosystem that seemed to like punch

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Speaker 3: and maybe they like it and punch it like you know,

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Speaker 3: ten percent of their weight in terms of our understanding

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Speaker 3: of their role. But talk a little bit more about

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Speaker 3: their role of insurance capital on a.

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Speaker 4: This Oh, this is a great plot. And actually it's

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Speaker 4: very different in Europe versus the US. But let's say,

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Speaker 4: if you go back to Tracy point, the railways were

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Speaker 4: funded mostly by insurance companies. I mean large, large capital

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Speaker 4: projects were mostly funded by the insurers. So because they

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Speaker 4: had long dated funding and you still had wildcat you

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Speaker 4: know runs in the States as you as you may

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Speaker 4: not remember, but I was there. So so thet you know,

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Speaker 4: everyone's looked at Apollo and their arrangement with a theme

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Speaker 4: and seeing that they've created a very they've got a

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Speaker 4: very stable source of funding through their annuity business, much

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Speaker 4: like you know you might have seen through the last

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Speaker 4: actually before even before the financial crisis, hedge funds wanted

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Speaker 4: permanent capital vehicles, right, everyone wants ee wants to be

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Speaker 4: aligned to long data capital, and so I think, you know,

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Speaker 4: most of the top ten now have an insurance business.

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Speaker 4: So I mean I was just you know, I listened

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Speaker 4: to the Blackstone call yesterday, two one hundred and twenty

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Speaker 4: one billion other assets and now from insurers out of

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Speaker 4: four hundred and thirty two, so over half of their

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Speaker 4: credit assets come from insurers. Now, obviously that's great because

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Speaker 4: these are investors with long duration liabilities who need long

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Speaker 4: dated assets. So they're the right kind of people to

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Speaker 4: fund data centers, infrastructure assets, you know, the long dated

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Speaker 4: and stuff we need to fund our growth. But the

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Speaker 4: interesting thing for private credit is, rather than having to

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Speaker 4: go for i know, ten to thirteen percent return, if

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Speaker 4: they can just simply get one hundred to two hundred

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Speaker 4: basis points more than the insurer could have got through

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Speaker 4: the public markets, there actually quits in. And so certainly

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Speaker 4: the expectation for the CIOs of insurers I speak to

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Speaker 4: is if they can get one hundred and fifty one

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Speaker 4: hundred and seventy five basis points pick up on a

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Speaker 4: single a bond by buying a private bond rather the

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Speaker 4: public bond, if you compound that over ten years, that's

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Speaker 4: huge for the insurance sector. And so as I said,

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Speaker 4: I think about forty percent of the ass sets now

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Speaker 4: of the majors come from insurance. I think for the

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Speaker 4: industry as always between near thirty and so that's fueling

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Speaker 4: the growth and it's changing the nature of where private

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Speaker 4: credit can invest.

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Speaker 3: By the way, Tracy, I didn't know that insurance companies

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Speaker 3: funded the railways, but I will say early on in

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Speaker 3: my career I do remember, and I sort of pat

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Speaker 3: myself on the back for this, I do remember having

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Speaker 3: the realization that sort of like clicked how similar banks

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Speaker 3: and insurance companies are. Because with the bank, you know,

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Speaker 3: you make a deposit of one thousand dollars and over

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Speaker 3: the lifetime the bank will probably give you back your

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Speaker 3: one thousand dollars in the form of you take it out.

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Speaker 3: Insurance companies basically the same. You buy a collect premium

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Speaker 3: and you get the you know on no but like

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Speaker 3: on average right for the industry, they pay out roughly

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Speaker 3: what they get in and they hope to like make

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Speaker 3: it on the float kind of. And so in the end,

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Speaker 3: like the models, in the ideal sense, it's just a

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Speaker 3: matter of timing of when the cash goat comes back.

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Speaker 1: Out but in aggregate, right, aggregate, but not my individual experience.

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Speaker 3: Some people get screwed and some people get way more

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Speaker 3: than they put in and then on average anyway, Yeah,

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Speaker 3: sort it sort of clicked to me one time in

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Speaker 3: my own.

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Speaker 1: There's there's a lot of overlap here, for sure. Okay,

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Speaker 1: So I want to go back to the financial risk

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Speaker 1: slash regulation points. So we've established a number of times

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Speaker 1: that to some extent, this is exactly what regulators wanted

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Speaker 1: to see happen. But I think there's always a concern

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Speaker 1: that maybe this will come back to bite them and

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Speaker 1: the overall financial system in some unexpected way, and that

401
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Speaker 1: maybe there are avenues that some of this risk is

402
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Speaker 1: still entangled with the banking system, especially as we see

403
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Speaker 1: these new partnerships develop. What are the avenues for private

404
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Speaker 1: credit risk to I guess, re enter the banking system

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Speaker 1: and potentially cause problems.

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Speaker 4: Look, I think it's a great question. I think I've

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Speaker 4: had almost every regulator post this question. This is this

408
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Speaker 4: is one of the very hot buttons for the issue

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Speaker 4: for them. So look, my take is that for sector

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Speaker 4: which is very low on leverage, doesn't have the big

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Speaker 4: asset liability mismatches is not systemically interconnected, and to be honest,

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Speaker 4: is still relatively small, less than three trillion. It's on

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Speaker 4: the whole, not a source of systemic risk. But the

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Speaker 4: question you gather therefore, well, are there pockets of leverage

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Speaker 4: that we can't see? So, for instance, the Bank of

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Speaker 4: England's got an investigation to think about where is the

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Speaker 4: hidden leverage because obviously having had the LDI problems under trust,

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Speaker 4: they're worried about hidden leverage. So nav finance is an

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Speaker 4: area which the regulators are pouring over and are getting

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Speaker 4: trying to get the data from firms just to see

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Speaker 4: as their leverage on leverage and the system that may

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Speaker 4: be you know, may trip them up. I think second

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Speaker 4: would be on the whole. If you've got ten plus two,

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Speaker 4: if the funds are ten year in duration, or even

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Speaker 4: even if they're six years and you're lending to five

426
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Speaker 4: year loans, there isn't a big asset liberty in mismatch.

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Speaker 4: But to the extent that private credit may potentially be

428
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Speaker 4: put into retail vehicles or even or even to ETFs,

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Speaker 4: is there going to be an asset liability mismatch. And

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Speaker 4: certainly the more that private credit looks to raise money

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Speaker 4: from retail, the more there's going to be a questions

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Speaker 4: around the structure. And we can come out to that

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Speaker 4: because there's a Cambrian explosion of interest of traditional asset

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Speaker 4: managers and private credit players teaming up to create commingle vehicles.

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Speaker 4: And then the third though, but tracy to your point,

436
00:21:17,640 --> 00:21:20,240
Speaker 4: is how do the tentacles overlap. So there is a

437
00:21:20,240 --> 00:21:22,159
Speaker 4: good piece by liberty streets. So like the Fed in

438
00:21:22,200 --> 00:21:25,200
Speaker 4: New York that twenty seven percent of bank loans are

439
00:21:25,280 --> 00:21:29,280
Speaker 4: now too non bank financial institutions, so hedge funds, private credit,

440
00:21:29,359 --> 00:21:31,680
Speaker 4: you know, a private equity and the like, and it's

441
00:21:31,680 --> 00:21:33,920
Speaker 4: been growing like a weed. And so they're war and

442
00:21:34,000 --> 00:21:36,760
Speaker 4: doing you know, you know, at one level they're very

443
00:21:36,800 --> 00:21:39,320
Speaker 4: happy that firms are laying off.

444
00:21:39,200 --> 00:21:40,800
Speaker 2: Risk to private credit.

445
00:21:41,160 --> 00:21:43,080
Speaker 4: But they but the question in fact, one of the

446
00:21:43,119 --> 00:21:46,240
Speaker 4: big central banks is asking the banks to try and

447
00:21:46,280 --> 00:21:50,040
Speaker 4: tos up every loan to private credit firm, every loan

448
00:21:50,119 --> 00:21:52,320
Speaker 4: to private credit firm, be in reality they're not making

449
00:21:52,320 --> 00:21:53,760
Speaker 4: the loan to the firm. They're doing it to the

450
00:21:53,800 --> 00:21:57,240
Speaker 4: underlying asset. But they won't have a consolidated tape because

451
00:21:57,480 --> 00:21:59,040
Speaker 4: you know what you don't know, you scares you, And

452
00:21:59,080 --> 00:22:01,240
Speaker 4: so they're trying to get a much better transparency on

453
00:22:01,280 --> 00:22:01,639
Speaker 4: this space.

454
00:22:02,160 --> 00:22:04,160
Speaker 1: This kind of reminds me of the conversation we had

455
00:22:04,160 --> 00:22:07,879
Speaker 1: with Mickey Shemy about synthetic risk transfers, where you know,

456
00:22:07,920 --> 00:22:10,000
Speaker 1: it's sort of the same idea. The bank is like

457
00:22:10,040 --> 00:22:12,760
Speaker 1: selling off part of the risk of a lone portfolio

458
00:22:12,840 --> 00:22:16,000
Speaker 1: totally to another entity. And that sounds fine, except sometimes

459
00:22:16,080 --> 00:22:18,399
Speaker 1: those other entities who tend to be hedge funds or

460
00:22:18,440 --> 00:22:22,000
Speaker 1: someone like that, are borrowing from banks in order to

461
00:22:22,080 --> 00:22:24,600
Speaker 1: apply leverage to boost the yield.

462
00:22:24,960 --> 00:22:27,840
Speaker 3: You say more about the twenty seven percent, I have

463
00:22:27,920 --> 00:22:30,760
Speaker 3: to go read the Liberty Street Economics report. But what

464
00:22:30,960 --> 00:22:34,840
Speaker 3: is from the bank's perspective that specific type of lending?

465
00:22:35,200 --> 00:22:36,680
Speaker 3: What are the risk characteristics?

466
00:22:36,680 --> 00:22:37,080
Speaker 2: What are the.

467
00:22:37,040 --> 00:22:41,360
Speaker 3: Capital impairment the capital cost characteristics of this kind of activity?

468
00:22:42,200 --> 00:22:44,040
Speaker 4: Well, look, so by the way, I'll send you THEA.

469
00:22:44,119 --> 00:22:46,440
Speaker 4: I think it's about where do banks end and let

470
00:22:46,480 --> 00:22:49,479
Speaker 4: and private markets begin? All banks begin is the title,

471
00:22:49,760 --> 00:22:52,639
Speaker 4: So I think, look, it's very heterogeneous at the moment.

472
00:22:52,840 --> 00:22:55,479
Speaker 4: So it's hedge funds, and I know that my good

473
00:22:55,520 --> 00:22:57,640
Speaker 4: friend Tosin Slocke said that's to a new high it's

474
00:22:57,640 --> 00:22:58,960
Speaker 4: to private equity firms.

475
00:22:59,240 --> 00:23:01,840
Speaker 2: So it's all over the place. But so I'm just

476
00:23:01,840 --> 00:23:02,680
Speaker 2: like what, Sorry.

477
00:23:02,520 --> 00:23:06,640
Speaker 3: Jake, my question wasn't particularly sorry. I know, my particular

478
00:23:06,720 --> 00:23:08,199
Speaker 3: question wasn't particularly cogent.

479
00:23:08,480 --> 00:23:10,160
Speaker 2: Here, it was worse.

480
00:23:10,280 --> 00:23:12,280
Speaker 3: It's like, no, no, no, it's fine, it's fine. I'm just

481
00:23:12,280 --> 00:23:15,760
Speaker 3: trying to understand, Like, right, Okay, So banks are optimization vehicles,

482
00:23:15,800 --> 00:23:19,120
Speaker 3: they're capital optimization vehicles and so forth, and they want

483
00:23:19,160 --> 00:23:22,480
Speaker 3: to like, they want to do the lending that creates

484
00:23:22,560 --> 00:23:25,240
Speaker 3: the fewest constraints on the size for regulators and all

485
00:23:25,320 --> 00:23:29,240
Speaker 3: that stuff. So where does lending to financial institutions fit

486
00:23:29,359 --> 00:23:32,080
Speaker 3: into this sort of like madetrix of costs and benefits?

487
00:23:32,320 --> 00:23:36,359
Speaker 4: Okay, So the way bank REGs work these days is

488
00:23:36,440 --> 00:23:40,040
Speaker 4: to encourage the banks to do senior or high quality

489
00:23:40,119 --> 00:23:43,400
Speaker 4: lending and to try and limit the amount of riskier lending,

490
00:23:43,560 --> 00:23:45,959
Speaker 4: either to try and originate and distribute it very quickly

491
00:23:46,320 --> 00:23:48,359
Speaker 4: or to lay it off through you know, derivatives of

492
00:23:48,400 --> 00:23:51,399
Speaker 4: some sort. Yeah, and so let's say it's it's lending

493
00:23:51,400 --> 00:23:53,359
Speaker 4: to hedge funds. Now, you know, because you've you've spoken

494
00:23:53,440 --> 00:23:55,399
Speaker 4: out before with archaegos, they got that wrong. But the

495
00:23:55,440 --> 00:23:57,840
Speaker 4: idea was up until then hedge fund lending. I mean

496
00:23:58,000 --> 00:23:58,800
Speaker 4: very low risk.

497
00:23:58,640 --> 00:24:00,760
Speaker 3: For el So this is kind of what hedge fund

498
00:24:00,840 --> 00:24:02,720
Speaker 3: lending is considered to be low risk.

499
00:24:03,359 --> 00:24:06,520
Speaker 1: Absolutely, although but I think that is changing, like there's

500
00:24:06,560 --> 00:24:08,479
Speaker 1: more scrutiny of the prime brokerage business.

501
00:24:08,960 --> 00:24:12,040
Speaker 4: Yeah, exactly, Well, because they had a fifteen e run

502
00:24:12,080 --> 00:24:15,720
Speaker 4: with almost no credit losses, and obviously they had good

503
00:24:15,760 --> 00:24:18,720
Speaker 4: collateral with haircuts and if and this was the big

504
00:24:18,800 --> 00:24:20,800
Speaker 4: question with the firms who got the wrong way around

505
00:24:20,800 --> 00:24:23,119
Speaker 4: to our chaegoss was if they got the right haircuts,

506
00:24:23,359 --> 00:24:25,280
Speaker 4: then there was secured lending. So on the whole, they

507
00:24:25,359 --> 00:24:27,960
Speaker 4: you know, they could seize the assets and sell them off.

508
00:24:28,080 --> 00:24:30,439
Speaker 4: What they got wrong was this was such a concentrated

509
00:24:30,480 --> 00:24:33,360
Speaker 4: pool and they were all stampeding. So so I think

510
00:24:33,359 --> 00:24:35,679
Speaker 4: actually the risk here is not so much the belly,

511
00:24:35,960 --> 00:24:36,800
Speaker 4: it's actually the tail.

512
00:24:37,160 --> 00:24:39,520
Speaker 2: So is there a scenario where there's.

513
00:24:39,359 --> 00:24:42,359
Speaker 4: A major credit event and that many companies go bust

514
00:24:42,400 --> 00:24:44,560
Speaker 4: and then that works its way through and that's where

515
00:24:44,560 --> 00:24:47,919
Speaker 4: the regulators are trying to piece it through. But my

516
00:24:48,000 --> 00:24:50,359
Speaker 4: take is on the whole, the banks are trying to

517
00:24:50,440 --> 00:24:54,040
Speaker 4: retranch keep the senior risk and I cheekily put in

518
00:24:54,119 --> 00:24:56,639
Speaker 4: and the zen pick of the system because this was

519
00:24:56,680 --> 00:24:59,520
Speaker 4: a wave for them to lay off risk local.

520
00:24:59,320 --> 00:25:01,640
Speaker 3: Directory very hard. Well, because in my mind, just sort

521
00:25:01,680 --> 00:25:05,320
Speaker 3: of very naively, I don't necessarily think of lending to

522
00:25:05,400 --> 00:25:07,920
Speaker 3: hedge funds is really safe lending, because aren't they taking

523
00:25:07,960 --> 00:25:09,920
Speaker 3: all kinds of crazy risks and doing all kinds of

524
00:25:09,920 --> 00:25:13,920
Speaker 3: stuff that may go wrong. But to your point, obviously

525
00:25:13,960 --> 00:25:16,800
Speaker 3: beyond just the run, the fact that there it's backed

526
00:25:16,800 --> 00:25:20,959
Speaker 3: by actual assets typically or typically the bank knows what

527
00:25:21,000 --> 00:25:24,479
Speaker 3: the assets are, I can understand more conceptually why lending

528
00:25:24,520 --> 00:25:28,080
Speaker 3: to financial institutions is more frequently perceived as safe. So

529
00:25:28,119 --> 00:25:29,200
Speaker 3: thank you for that, Joe.

530
00:25:29,240 --> 00:25:31,960
Speaker 1: I think we should do a prime brokerage episode of

531
00:25:32,000 --> 00:25:34,920
Speaker 1: All Lots where all we do is a dramatic reading

532
00:25:35,160 --> 00:25:37,840
Speaker 1: of the report on credit Swiss and our ke ghost.

533
00:25:37,880 --> 00:25:38,560
Speaker 2: Yeah, let's do it.

534
00:25:38,760 --> 00:25:41,040
Speaker 1: We just do that, because that was amazing and actually,

535
00:25:42,840 --> 00:25:45,680
Speaker 1: well actually a really good insight into how it all works,

536
00:25:45,920 --> 00:25:48,800
Speaker 1: or you know, a bad example of how it should

537
00:25:48,840 --> 00:25:51,480
Speaker 1: not work. Anyway, Hugh, I'm going to ask a really

538
00:25:51,520 --> 00:25:55,920
Speaker 1: basic question, but I find, you know, the changing answers

539
00:25:56,000 --> 00:25:59,159
Speaker 1: to this one always really interesting. But how are banks

540
00:25:59,200 --> 00:25:59,840
Speaker 1: making money?

541
00:25:59,840 --> 00:26:04,359
Speaker 4: Now? Oh? It's that Look, that's that's a really good question.

542
00:26:04,480 --> 00:26:07,800
Speaker 4: So after fifteen years of zero or negative rates, We've

543
00:26:07,800 --> 00:26:11,040
Speaker 4: had a wonderful couple of years where spread income, so

544
00:26:11,119 --> 00:26:14,280
Speaker 4: the spread between the assets liability once again became profitable,

545
00:26:14,560 --> 00:26:17,040
Speaker 4: and that really hurt the banks. But you know, if

546
00:26:17,240 --> 00:26:21,000
Speaker 4: the majority of my conversations, both Stateside and Europe and

547
00:26:21,040 --> 00:26:22,720
Speaker 4: even in Asia, as the banks want to make more

548
00:26:22,760 --> 00:26:25,480
Speaker 4: fee income, so that could be asset management, could be

549
00:26:25,520 --> 00:26:29,160
Speaker 4: private banking, could be originate to distribute. They want to

550
00:26:29,200 --> 00:26:31,760
Speaker 4: continue to shift more and more of their earnings towards

551
00:26:31,760 --> 00:26:35,760
Speaker 4: fees and less from just common and garden banking. And

552
00:26:35,760 --> 00:26:38,920
Speaker 4: that's partly cyclical. As interest rates are being cut now,

553
00:26:39,200 --> 00:26:41,960
Speaker 4: the anticipation is the kind of the spread is going

554
00:26:41,960 --> 00:26:43,879
Speaker 4: to be under a little bit of pressure, but you know,

555
00:26:44,080 --> 00:26:46,320
Speaker 4: as we've seen actually it's continued to be very good.

556
00:26:46,520 --> 00:26:48,680
Speaker 4: But it's more and more fees that's where that's really

557
00:26:48,680 --> 00:26:52,040
Speaker 4: where the banks are focused, and then within the loan income.

558
00:26:52,680 --> 00:26:55,720
Speaker 4: My sort of take is that the kind of winner takes.

559
00:26:55,760 --> 00:26:58,240
Speaker 4: Most dynamics we've seen in tech are starting to come

560
00:26:58,280 --> 00:27:01,000
Speaker 4: to banking. The more of a bank cost base, which

561
00:27:01,080 --> 00:27:05,000
Speaker 4: is the systems, the cloud data, you know, it's more

562
00:27:05,040 --> 00:27:07,520
Speaker 4: and more the cost space is tech well quite frankly,

563
00:27:07,560 --> 00:27:09,680
Speaker 4: it's very scalable, and so what you're starting to see

564
00:27:09,720 --> 00:27:11,600
Speaker 4: if you look at the roe. I actually did a

565
00:27:11,600 --> 00:27:13,199
Speaker 4: piece the other day where I looked at the ros

566
00:27:13,280 --> 00:27:16,040
Speaker 4: for the banks in every country by the number one player,

567
00:27:16,080 --> 00:27:18,399
Speaker 4: number two, number three, umber five. The top three players

568
00:27:18,440 --> 00:27:21,119
Speaker 4: in each market are doing so much better than the

569
00:27:21,160 --> 00:27:23,040
Speaker 4: tails than they wear a decade ago. And I think

570
00:27:23,040 --> 00:27:26,120
Speaker 4: it's that winner takes most win, it takes more behavior.

571
00:27:26,280 --> 00:27:28,880
Speaker 3: That's really interesting. Actually, let's talk a little bit more

572
00:27:28,880 --> 00:27:31,520
Speaker 3: about this, because I have also from time to time

573
00:27:31,640 --> 00:27:34,679
Speaker 3: pull up the chart of JP Morgan and compare it

574
00:27:34,760 --> 00:27:37,280
Speaker 3: to other banks, and it is it looks kind of

575
00:27:37,320 --> 00:27:39,480
Speaker 3: like a tech dog. I mean, it's not really quiet

576
00:27:39,520 --> 00:27:41,639
Speaker 3: as good because it's not a techt Doug, but it

577
00:27:41,800 --> 00:27:45,520
Speaker 3: sort of seems to exhibit And I wondered about this,

578
00:27:45,800 --> 00:27:48,240
Speaker 3: this sort of winner take on this of the market

579
00:27:48,400 --> 00:27:51,000
Speaker 3: and whether there is a similar dynamic. And I hadn't

580
00:27:51,040 --> 00:27:53,480
Speaker 3: thought about it quite so literally in terms of the

581
00:27:53,600 --> 00:27:56,320
Speaker 3: actual tech stack of the bank, and I was wondering

582
00:27:56,359 --> 00:27:58,800
Speaker 3: if it was more sort of like a network effects.

583
00:27:58,800 --> 00:28:01,679
Speaker 3: And of course in finance work effects are important just

584
00:28:01,680 --> 00:28:03,840
Speaker 3: like they are in software. But talk to us a

585
00:28:03,880 --> 00:28:07,560
Speaker 3: little bit about the dynamics that you think are contributing

586
00:28:07,600 --> 00:28:10,639
Speaker 3: to this. Winner take on this in any country banking system.

587
00:28:11,200 --> 00:28:13,720
Speaker 4: So look, I think obviously there's part of it is

588
00:28:13,800 --> 00:28:15,920
Speaker 4: the is the tech stack. Of course, you know, if

589
00:28:15,920 --> 00:28:18,520
Speaker 4: they're trading assets, there's always going to be some network effects.

590
00:28:18,520 --> 00:28:21,000
Speaker 4: If you can take you know, thirteen fourteen, fifteen percent

591
00:28:21,000 --> 00:28:23,840
Speaker 4: for market, you just see more, you can price better,

592
00:28:24,000 --> 00:28:26,160
Speaker 4: you've got better source of flow. So I think in

593
00:28:26,160 --> 00:28:29,359
Speaker 4: investment banking markets and in sort of wealth markets, there's

594
00:28:29,400 --> 00:28:34,080
Speaker 4: definitely some network effects, but also just there's scale in origination.

595
00:28:34,200 --> 00:28:37,560
Speaker 4: I mean, you just need loan officers, loan processors. I

596
00:28:37,560 --> 00:28:41,000
Speaker 4: mean it's very typically, it's very manual, and in fact,

597
00:28:41,120 --> 00:28:42,920
Speaker 4: one of the not for me, but some of our

598
00:28:43,000 --> 00:28:45,680
Speaker 4: colleagues are doing a lot of work actually using AI

599
00:28:45,800 --> 00:28:48,440
Speaker 4: to automate loan procedures for banks because that's an area

600
00:28:48,480 --> 00:28:52,240
Speaker 4: which is very physical, historically been very labor intensive. And

601
00:28:52,320 --> 00:28:54,560
Speaker 4: actually one of the reasons why private credit is trying

602
00:28:54,600 --> 00:28:56,760
Speaker 4: to team up with the banks is because they don't

603
00:28:56,800 --> 00:28:58,560
Speaker 4: have enough people to originate, so they're trying to lean

604
00:28:58,600 --> 00:29:02,000
Speaker 4: on someone else's origination STAF. Now, look, but just there's

605
00:29:02,120 --> 00:29:05,040
Speaker 4: one nuance here for every power, there's an equal and

606
00:29:05,080 --> 00:29:07,760
Speaker 4: opposite power. So in the States, let's say for regional

607
00:29:07,840 --> 00:29:10,760
Speaker 4: back for the smallest banks, they're all basically sitting on

608
00:29:10,760 --> 00:29:14,360
Speaker 4: one of three players like five serv So they're enjoying scale,

609
00:29:14,400 --> 00:29:16,920
Speaker 4: but they're just outsourcing it. And then the other thing is,

610
00:29:17,000 --> 00:29:20,600
Speaker 4: of course, you know the Google's alphabets and as you're

611
00:29:20,640 --> 00:29:24,240
Speaker 4: within Microsoft are getting a winning handover fist because if

612
00:29:24,280 --> 00:29:26,840
Speaker 4: you're a MidCap bank, the way you try to capture

613
00:29:26,840 --> 00:29:29,480
Speaker 4: scale is by outsourcing to one of the superscalers.

614
00:29:29,920 --> 00:29:32,680
Speaker 1: I think you anticipated my next question perhaps when you

615
00:29:32,680 --> 00:29:35,560
Speaker 1: brought up AI. But one thing I often think about

616
00:29:35,680 --> 00:29:39,280
Speaker 1: the financial sector, so banks and insurance companies, is if

617
00:29:39,320 --> 00:29:42,560
Speaker 1: AI is Obviously it's about the technology, but it's about

618
00:29:42,560 --> 00:29:45,760
Speaker 1: the data too. Who has the most data? It's got

619
00:29:45,760 --> 00:29:49,120
Speaker 1: to be insurers and banks, right, they just have noodles

620
00:29:49,120 --> 00:29:52,760
Speaker 1: and oodles of it. How excited are banks in particular

621
00:29:52,920 --> 00:29:56,760
Speaker 1: getting about, you know, the actual data component of their

622
00:29:56,800 --> 00:29:57,400
Speaker 1: business here?

623
00:29:58,440 --> 00:30:00,600
Speaker 2: Well, hopefully Bloomberg's got one of the best data staffs.

624
00:30:00,720 --> 00:30:01,280
Speaker 2: Well us too.

625
00:30:03,160 --> 00:30:05,920
Speaker 4: Oh no, this is so there's a huge amount of

626
00:30:05,960 --> 00:30:08,720
Speaker 4: automation going on. I mean, look, let's take one step back.

627
00:30:08,880 --> 00:30:10,720
Speaker 4: The banks need to make sure that their data is

628
00:30:10,880 --> 00:30:12,840
Speaker 4: organized in a lake or in a way it can

629
00:30:12,880 --> 00:30:15,360
Speaker 4: be used effectively. And then number two, you need to train.

630
00:30:15,440 --> 00:30:18,000
Speaker 4: So actually one of the largest banks now has every

631
00:30:18,040 --> 00:30:20,920
Speaker 4: new graduate doing AI prompt training as part of their

632
00:30:20,920 --> 00:30:23,120
Speaker 4: core curriculum as they join. So one of my son's

633
00:30:23,360 --> 00:30:26,120
Speaker 4: flatmates just done eight weeks of AI training. It's extraordinary.

634
00:30:26,360 --> 00:30:29,160
Speaker 4: So the way, of course, if the more data you've got,

635
00:30:29,240 --> 00:30:32,080
Speaker 4: the better. But Tracy, there's some really subtle things in here,

636
00:30:32,120 --> 00:30:34,680
Speaker 4: because the regulators want to know how you made the

637
00:30:34,800 --> 00:30:38,200
Speaker 4: decisions right, and is the a optimizing just on past

638
00:30:38,320 --> 00:30:39,920
Speaker 4: experience or is it right?

639
00:30:40,000 --> 00:30:43,840
Speaker 1: This is the black box alg point, right, where like

640
00:30:43,920 --> 00:30:45,560
Speaker 1: if you have all this data going into a black

641
00:30:45,600 --> 00:30:48,280
Speaker 1: box and algorithm and it's spitting out an answer, you

642
00:30:48,320 --> 00:30:51,000
Speaker 1: actually have to know whether that answer is valid, like

643
00:30:51,040 --> 00:30:56,160
Speaker 1: whether it might violate regulations on biased lending based on

644
00:30:56,240 --> 00:30:59,280
Speaker 1: like racial or age characteristics or gender or something like that.

645
00:31:00,960 --> 00:31:03,120
Speaker 4: Absolutely, and so there's all sorts of bites. So at

646
00:31:03,120 --> 00:31:05,440
Speaker 4: the moment, most of it is for co piloting, but

647
00:31:05,520 --> 00:31:08,400
Speaker 4: you know, some of the use cases, Tracy, are fascinating.

648
00:31:08,440 --> 00:31:10,360
Speaker 4: It's like one of the big banks I was talking with,

649
00:31:10,440 --> 00:31:13,040
Speaker 4: they're actually using AI now in the right sharp departments

650
00:31:13,040 --> 00:31:15,320
Speaker 4: to just basically automate and a thing. If they want

651
00:31:15,320 --> 00:31:17,120
Speaker 4: to fask someone, they just they click a big thing

652
00:31:17,360 --> 00:31:18,520
Speaker 4: less and then try and work it out.

653
00:31:18,560 --> 00:31:20,280
Speaker 1: Oh wow, dystopia is here.

654
00:31:20,480 --> 00:31:23,000
Speaker 3: Yeah, it really is. There's some great articles, by the way,

655
00:31:23,000 --> 00:31:25,760
Speaker 3: done by Bloomberg, not related to banks or anything, but

656
00:31:25,800 --> 00:31:29,240
Speaker 3: like on the sort of like Amazon auto hiring and firing,

657
00:31:29,360 --> 00:31:31,920
Speaker 3: and just this idea of all that being the assumption

658
00:31:32,000 --> 00:31:34,960
Speaker 3: of liquid labor markets and the you know, it's okay

659
00:31:35,000 --> 00:31:37,600
Speaker 3: to make mistakes if there's just an endless supply of

660
00:31:37,600 --> 00:31:40,120
Speaker 3: people who want to work at your company. Anyway, that's

661
00:31:40,120 --> 00:31:43,880
Speaker 3: its own digression, you know, just on this point. So obviously, okay,

662
00:31:43,920 --> 00:31:46,640
Speaker 3: the big banks have their gigantic text ex. I had

663
00:31:46,680 --> 00:31:50,280
Speaker 3: a conversation recently with someone who worked for a very

664
00:31:50,320 --> 00:31:53,520
Speaker 3: small bank. Actually it was just like someone over coffee

665
00:31:53,880 --> 00:31:55,680
Speaker 3: and I'm curious and so it's like kind of like

666
00:31:55,760 --> 00:31:58,040
Speaker 3: doing an odd lots except over coffee with no microphone,

667
00:31:58,080 --> 00:32:00,640
Speaker 3: like how it all works and how a region or

668
00:32:00,680 --> 00:32:04,000
Speaker 3: a small local bank actually has a business. And it

669
00:32:04,040 --> 00:32:07,720
Speaker 3: was really striking in the conversation how many of the

670
00:32:07,760 --> 00:32:12,080
Speaker 3: specific things that came up were literally about third party

671
00:32:12,120 --> 00:32:15,520
Speaker 3: modeling or software packages and stuff like that, and how

672
00:32:15,600 --> 00:32:19,240
Speaker 3: much the sort of all kinds of risk management, et cetera.

673
00:32:19,880 --> 00:32:23,800
Speaker 3: It was really a job of plugging their numbers in

674
00:32:23,840 --> 00:32:26,360
Speaker 3: to various packages that they buy. And I have to

675
00:32:26,400 --> 00:32:29,840
Speaker 3: imagine that the companies that sell these modeling services or

676
00:32:29,840 --> 00:32:32,880
Speaker 3: software services or data services to any of the banks

677
00:32:32,920 --> 00:32:35,280
Speaker 3: that aren't like JP Morgan and a few others must

678
00:32:35,280 --> 00:32:36,320
Speaker 3: be making a mint.

679
00:32:36,880 --> 00:32:40,560
Speaker 4: Oh absolutely, Look, I mean that's not my area. No,

680
00:32:40,920 --> 00:32:43,200
Speaker 4: But just in the same way, the MSCI has made

681
00:32:43,240 --> 00:32:45,120
Speaker 4: an absolute fortune by being the day you know, the

682
00:32:45,160 --> 00:32:46,840
Speaker 4: premier data company for markets.

683
00:32:47,160 --> 00:32:47,800
Speaker 2: See.

684
00:32:47,880 --> 00:32:49,600
Speaker 4: One way to frame it is in this of fifteen

685
00:32:49,680 --> 00:32:52,520
Speaker 4: years post financial crisis, the banks you know, in many

686
00:32:52,640 --> 00:32:54,400
Speaker 4: at least certainly for the first seven or eight, which

687
00:32:54,440 --> 00:32:58,280
Speaker 4: is focused on capital repair, improving process, improving risk management,

688
00:32:58,600 --> 00:33:01,160
Speaker 4: that the amount of discretionary that they had to invest

689
00:33:01,200 --> 00:33:03,959
Speaker 4: in new textacs was really quite low, and so a

690
00:33:04,000 --> 00:33:06,960
Speaker 4: lot of the innovation was happening outside banks and being

691
00:33:07,000 --> 00:33:09,360
Speaker 4: sold back in. Now, as you say, look from maybe

692
00:33:09,400 --> 00:33:12,000
Speaker 4: twenty sixteen onwards, the US banks got back on the

693
00:33:12,000 --> 00:33:15,360
Speaker 4: front foot, and the leading ones are investing disproportionately in

694
00:33:15,400 --> 00:33:18,040
Speaker 4: tech and their own solutions but there's an enormous amount

695
00:33:18,040 --> 00:33:20,520
Speaker 4: which is brought in and again that's sort of that's

696
00:33:20,520 --> 00:33:22,080
Speaker 4: why if you go back to private credit, one of

697
00:33:22,080 --> 00:33:25,440
Speaker 4: the areas they hope is is that they certainly the

698
00:33:25,520 --> 00:33:28,680
Speaker 4: leading firms are also investing in tech stacks because they

699
00:33:28,680 --> 00:33:30,920
Speaker 4: want to make sure they have an information edge. And

700
00:33:30,960 --> 00:33:33,240
Speaker 4: so also you're starting to see, i want to say,

701
00:33:33,240 --> 00:33:35,479
Speaker 4: hollowing out of the middle in private credit. But definitely

702
00:33:35,480 --> 00:33:39,440
Speaker 4: the larger firms are investing very significantly in treasury management

703
00:33:39,480 --> 00:33:41,640
Speaker 4: data and underlike.

704
00:33:41,680 --> 00:33:44,160
Speaker 3: By the way, Tracy, check out look over a chart

705
00:33:44,200 --> 00:33:47,000
Speaker 3: of five five serves duck, which does a bunch of

706
00:33:47,480 --> 00:33:51,440
Speaker 3: various payment things for smaller and credit union banks and

707
00:33:51,480 --> 00:33:53,960
Speaker 3: stuff like that, and check out the Oh geez, yeah,

708
00:33:54,080 --> 00:33:55,880
Speaker 3: check out their stuck I just pulled it up.

709
00:33:55,960 --> 00:33:57,200
Speaker 1: I have to compare it to in video.

710
00:33:57,400 --> 00:34:00,640
Speaker 3: Yeah right, I know it looks like it hasn't it.

711
00:34:00,640 --> 00:34:02,240
Speaker 3: It's probably well anyway.

712
00:34:02,040 --> 00:34:20,880
Speaker 1: Yeah, that's amazing. Why don't we get back to private credit.

713
00:34:21,160 --> 00:34:23,960
Speaker 1: We're in danger of just making this an AI episode.

714
00:34:24,120 --> 00:34:27,520
Speaker 1: But Hugh, you know, we've seen growth in private credit,

715
00:34:27,560 --> 00:34:30,600
Speaker 1: although to your point, it's still relatively small, so you know,

716
00:34:30,680 --> 00:34:33,399
Speaker 1: it's coming up from a low base. We've seen more

717
00:34:33,480 --> 00:34:38,400
Speaker 1: partnerships between banks and private credit entities. What's next in

718
00:34:38,480 --> 00:34:41,000
Speaker 1: terms of this dynamic? What are you watching out for

719
00:34:41,120 --> 00:34:42,319
Speaker 1: as the next big thing?

720
00:34:43,280 --> 00:34:46,840
Speaker 4: So for me, private credit's next act is around asset

721
00:34:46,880 --> 00:34:50,480
Speaker 4: back lending and secondly commercial real estate, and I think

722
00:34:50,520 --> 00:34:54,160
Speaker 4: they're the two big asset classes which the private credit

723
00:34:54,200 --> 00:34:58,200
Speaker 4: firms are really trying to either gain origination or do

724
00:34:58,320 --> 00:35:01,080
Speaker 4: partnerships or get into and just go back to it. So,

725
00:35:01,120 --> 00:35:04,800
Speaker 4: if specialty finances are five and a half trillion market,

726
00:35:05,040 --> 00:35:07,680
Speaker 4: private credit has about a five point share. If you

727
00:35:07,760 --> 00:35:10,839
Speaker 4: then include consumer mortgages and commercial real estate, it gets

728
00:35:10,840 --> 00:35:13,319
Speaker 4: to about twenty five trillion in the States, of which

729
00:35:13,360 --> 00:35:15,680
Speaker 4: private credit has probably about a two percent share. So

730
00:35:15,800 --> 00:35:17,919
Speaker 4: this is an area where they are really trying to say,

731
00:35:18,239 --> 00:35:21,320
Speaker 4: with insurance led assets, with also some of the assets

732
00:35:21,360 --> 00:35:24,000
Speaker 4: for the wealthy. This is where they're really gunning for

733
00:35:24,040 --> 00:35:26,840
Speaker 4: it now. At the moment, commercial real estate is probably

734
00:35:26,880 --> 00:35:29,560
Speaker 4: less picked over because the areas where the banks are

735
00:35:29,600 --> 00:35:32,239
Speaker 4: shedding is more the distressed or stressed, particularly if it's

736
00:35:32,239 --> 00:35:34,920
Speaker 4: a US regional bank. But the asset back lending piece

737
00:35:35,080 --> 00:35:38,400
Speaker 4: is they're going absolutely gung ho, and that's the area

738
00:35:38,400 --> 00:35:41,120
Speaker 4: which I think is probably one of the most interesting

739
00:35:41,160 --> 00:35:44,120
Speaker 4: areas to spend time on. And then the second bit

740
00:35:44,160 --> 00:35:46,839
Speaker 4: trace is that's obviously on origination on the where they're

741
00:35:46,840 --> 00:35:50,280
Speaker 4: getting the assets from. As we've spoken about many times before,

742
00:35:50,440 --> 00:35:53,080
Speaker 4: Let's be honest, the endowments and pension funds are still

743
00:35:53,080 --> 00:35:54,480
Speaker 4: a lot of bit of have got a bit of

744
00:35:54,520 --> 00:35:59,400
Speaker 4: indigestion to private equity and venture capital. Now that indigestion's passing,

745
00:35:59,719 --> 00:36:02,640
Speaker 4: but most endowments I speak to still say they're abroout

746
00:36:02,719 --> 00:36:05,799
Speaker 4: five points over allocated to VC. Would love to put

747
00:36:05,800 --> 00:36:07,680
Speaker 4: that to private credit, but they just don't want to

748
00:36:07,760 --> 00:36:10,880
Speaker 4: do more I liquid assets today. So the private credit

749
00:36:10,920 --> 00:36:14,080
Speaker 4: firms are doing three things. Going international, so going to

750
00:36:14,080 --> 00:36:16,040
Speaker 4: the Middle East in particular, where there is just a

751
00:36:16,080 --> 00:36:19,640
Speaker 4: ton of new money, and actually that those clients really

752
00:36:19,719 --> 00:36:23,160
Speaker 4: like fixed income. They're going to insurers, and I still

753
00:36:23,160 --> 00:36:25,600
Speaker 4: think there's a good runway to raise money from insurers

754
00:36:25,640 --> 00:36:28,360
Speaker 4: we could discuss. And third and finally, it's the wealthy.

755
00:36:28,880 --> 00:36:30,279
Speaker 4: And I think at the moment there's a little bit

756
00:36:30,280 --> 00:36:33,680
Speaker 4: of misnomer. When they say wealthy, they mean seriously wealthy.

757
00:36:33,760 --> 00:36:36,120
Speaker 4: I mean they talk about you know, family offices, people

758
00:36:36,160 --> 00:36:39,280
Speaker 4: with fifty million dollars plus there's about a nine trillion

759
00:36:39,360 --> 00:36:42,800
Speaker 4: dollar market of family offices globally. That's their sweet spot,

760
00:36:43,000 --> 00:36:47,360
Speaker 4: but they're going to look increasingly towards the decently wealthy,

761
00:36:47,840 --> 00:36:50,279
Speaker 4: and I think their product innovation around, you know, whether

762
00:36:50,320 --> 00:36:53,360
Speaker 4: it's Capital International with KKR, where it's Black Crop with

763
00:36:53,400 --> 00:36:56,479
Speaker 4: Partners Group, whether it's Apollo with State Street Global, there's

764
00:36:56,480 --> 00:36:59,960
Speaker 4: some really interesting innovation about how you slice up private

765
00:37:00,040 --> 00:37:03,200
Speaker 4: credit to some of like affluent clients, and that's something again,

766
00:37:03,200 --> 00:37:04,560
Speaker 4: you could do a whole episode.

767
00:37:04,160 --> 00:37:07,200
Speaker 3: On Tracy we've never done. I don't think a family

768
00:37:07,239 --> 00:37:09,000
Speaker 3: office episode. No, we should.

769
00:37:09,160 --> 00:37:11,839
Speaker 1: Yeah, we should go to Singapore, Yeah and do it

770
00:37:11,880 --> 00:37:15,680
Speaker 1: from there. Yes, because I want to go back to Asia. Okay, Well, Hugh,

771
00:37:16,000 --> 00:37:18,280
Speaker 1: thank you so much for coming back on this show.

772
00:37:18,400 --> 00:37:20,920
Speaker 1: It was lovely to catch up with you as always,

773
00:37:20,960 --> 00:37:22,960
Speaker 1: and you walked us through that perfectly. So thank you

774
00:37:23,000 --> 00:37:23,399
Speaker 1: so much.

775
00:37:23,920 --> 00:37:24,600
Speaker 2: Thanks for having me.

776
00:37:24,680 --> 00:37:25,920
Speaker 3: Thanks you. That was fantastic.

777
00:37:38,400 --> 00:37:40,920
Speaker 1: Joe, that was great. I love catching up with you again.

778
00:37:41,000 --> 00:37:42,600
Speaker 1: Like I've known him for a long time and he's

779
00:37:42,640 --> 00:37:45,920
Speaker 1: always had really interesting thoughts on the financial sector and

780
00:37:45,960 --> 00:37:48,719
Speaker 1: a great way of kind of explaining them. I do

781
00:37:48,800 --> 00:37:52,160
Speaker 1: think his idea of retranching of risk in the financial

782
00:37:52,200 --> 00:37:57,600
Speaker 1: system is definitely like the way to think about what's happening.

783
00:37:57,719 --> 00:38:00,839
Speaker 1: It seems like, in some respects seeing more and more

784
00:38:00,920 --> 00:38:03,920
Speaker 1: specialization in the system where maybe it used to be,

785
00:38:04,040 --> 00:38:07,080
Speaker 1: you know, back in nineteen seventy when bank lending was

786
00:38:07,120 --> 00:38:09,120
Speaker 1: at its height, the bank would do all sorts of

787
00:38:09,160 --> 00:38:11,600
Speaker 1: things right, But now it kind of breaks up all

788
00:38:11,640 --> 00:38:15,160
Speaker 1: those different businesses into different pieces and has different partners

789
00:38:15,160 --> 00:38:15,960
Speaker 1: for each one of those.

790
00:38:16,360 --> 00:38:18,160
Speaker 3: No, I think that makes a lot of sense. Look,

791
00:38:18,200 --> 00:38:21,560
Speaker 3: I would still say, and you know famous last words

792
00:38:21,600 --> 00:38:23,640
Speaker 3: that someone will make fun of me, but I would

793
00:38:23,719 --> 00:38:27,080
Speaker 3: still say that by and large, I am of the

794
00:38:27,160 --> 00:38:31,319
Speaker 3: view that the post grade financial crisis evolution of the

795
00:38:31,320 --> 00:38:35,200
Speaker 3: financial system has probably been a net good in terms

796
00:38:35,239 --> 00:38:39,400
Speaker 3: of overall financial stability risk. That to your point about

797
00:38:39,440 --> 00:38:43,000
Speaker 3: the tranching, that the financial system has gotten better about

798
00:38:43,000 --> 00:38:46,280
Speaker 3: putting the right form of risk in the right hands.

799
00:38:47,000 --> 00:38:49,960
Speaker 3: There's never total delinkage or anything. But even hearing them

800
00:38:50,000 --> 00:38:55,120
Speaker 3: explain why financial lending to financial institutions is a safer

801
00:38:55,200 --> 00:38:57,319
Speaker 3: form of lending that is really helpful, and so why

802
00:38:57,360 --> 00:38:59,880
Speaker 3: that's grown like why you know, this emergence of his

803
00:39:00,000 --> 00:39:02,840
Speaker 3: specific mid market type lending and the right entities for that.

804
00:39:03,760 --> 00:39:04,120
Speaker 2: I don't know.

805
00:39:04,200 --> 00:39:06,680
Speaker 3: I'm still of the view that probably things are better.

806
00:39:07,200 --> 00:39:09,080
Speaker 1: It's one of those things where there could always be

807
00:39:09,160 --> 00:39:11,160
Speaker 1: something that we're missing, yeah, of course, right, and that

808
00:39:11,239 --> 00:39:14,319
Speaker 1: regulators are missing. I do think at the moment we

809
00:39:14,400 --> 00:39:16,359
Speaker 1: seem to be in a sweet spot where a lot

810
00:39:16,440 --> 00:39:19,319
Speaker 1: of this is happening. So risk is getting divided up

811
00:39:19,360 --> 00:39:22,840
Speaker 1: and you know, distributed differently in the financial system to

812
00:39:22,880 --> 00:39:25,560
Speaker 1: where it was in two thousand and eight. But it's

813
00:39:25,560 --> 00:39:28,399
Speaker 1: still relatively small, like he was saying, despite all those

814
00:39:28,440 --> 00:39:31,279
Speaker 1: headlines about private credit, like, we're still talking about a

815
00:39:31,320 --> 00:39:34,399
Speaker 1: relatively small market. It could be that as it gets

816
00:39:34,440 --> 00:39:38,320
Speaker 1: bigger and bigger, it becomes more problematic in various ways.

817
00:39:38,520 --> 00:39:40,320
Speaker 1: But the other thing that I think is interesting about

818
00:39:40,320 --> 00:39:42,400
Speaker 1: private credit, and I think we've talked about it a

819
00:39:42,480 --> 00:39:44,680
Speaker 1: couple of times at this point, is the idea that

820
00:39:44,840 --> 00:39:48,399
Speaker 1: it kind of has acted as an additional cushion of

821
00:39:48,480 --> 00:39:51,120
Speaker 1: financing during the past couple of years where we had

822
00:39:51,200 --> 00:39:55,320
Speaker 1: really high rates and banks were still relatively capital constrained,

823
00:39:55,760 --> 00:39:57,799
Speaker 1: so you could still get a lot of you know,

824
00:39:57,840 --> 00:40:02,120
Speaker 1: middle market businesses have this addition layer of financing or

825
00:40:02,160 --> 00:40:04,680
Speaker 1: funding that they could still tap even if the banks

826
00:40:04,680 --> 00:40:05,920
Speaker 1: weren't necessarily doing it.

827
00:40:06,600 --> 00:40:08,120
Speaker 3: The winner take all in this of banks.

828
00:40:08,160 --> 00:40:09,240
Speaker 2: Oh yeah, really interesting.

829
00:40:09,320 --> 00:40:11,640
Speaker 3: I think it's one of those things that you can

830
00:40:11,719 --> 00:40:13,920
Speaker 3: see and you can look at the comparison of large

831
00:40:13,920 --> 00:40:16,319
Speaker 3: caps for small caps or whatever, or JP Morgan versus

832
00:40:16,360 --> 00:40:18,640
Speaker 3: literally everyone else in you guys. But it is like

833
00:40:18,760 --> 00:40:22,399
Speaker 3: still sort of a little bit under discussed and underdiscussed

834
00:40:22,400 --> 00:40:24,120
Speaker 3: why And I get the point about the tech stack,

835
00:40:24,160 --> 00:40:26,680
Speaker 3: and I get the point about you know, capital markets.

836
00:40:26,719 --> 00:40:29,839
Speaker 3: There's a natural network effects, but it's still interesting. We

837
00:40:29,880 --> 00:40:32,919
Speaker 3: live in this network effects world and in almost any

838
00:40:32,960 --> 00:40:35,520
Speaker 3: industry this seems to be a phenomenon. And why that

839
00:40:35,680 --> 00:40:38,120
Speaker 3: is across so many different areas where you have a

840
00:40:38,200 --> 00:40:40,799
Speaker 3: number of companies in any industry that look like tech

841
00:40:40,840 --> 00:40:44,000
Speaker 3: stocks is an interesting under discussed phenomenon.

842
00:40:44,160 --> 00:40:46,240
Speaker 1: Joe's theory of network effect.

843
00:40:46,400 --> 00:40:51,840
Speaker 3: You know what I said, all companies are banks except banks.

844
00:40:52,160 --> 00:40:53,360
Speaker 3: Banks are media company.

845
00:40:53,719 --> 00:40:56,640
Speaker 1: That's perfect. Yeah, I love that. Okay, let's leave it

846
00:40:56,680 --> 00:40:58,719
Speaker 1: there on a high note. Yeah, all right, this has

847
00:40:58,719 --> 00:41:02,600
Speaker 1: been another episode the Authoughts podcast. I'm Tracy Alloway. You

848
00:41:02,600 --> 00:41:04,759
Speaker 1: can follow me at Tracy Alloway, and.

849
00:41:04,719 --> 00:41:07,600
Speaker 3: I'm Joe Wisenthal. You can follow me at the Stalwart,

850
00:41:07,680 --> 00:41:10,879
Speaker 3: follow our guest Hugh von steinas He's at Hugh Steinas,

851
00:41:11,000 --> 00:41:14,640
Speaker 3: Follow our producers Carmen Rodriguez at Carman Ermann Dashel Bennett

852
00:41:14,640 --> 00:41:17,719
Speaker 3: at dashbod In Kelbrooks at Keilbrooks. And thank you to

853
00:41:17,760 --> 00:41:20,759
Speaker 3: our producer Moses Ondam and from our Oddlots content. Go

854
00:41:20,800 --> 00:41:23,560
Speaker 3: to Bloomberg dot com slash odd lots, where you have transcripts,

855
00:41:23,560 --> 00:41:26,520
Speaker 3: a blog and a daily newsletter and you can chat

856
00:41:26,560 --> 00:41:28,680
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857
00:41:28,800 --> 00:41:32,000
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858
00:41:32,400 --> 00:41:34,919
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859
00:41:34,960 --> 00:41:38,680
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860
00:41:38,680 --> 00:41:42,200
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861
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865
00:41:54,719 --> 00:41:58,080
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866
00:41:58,320 --> 00:42:18,520
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