# Credit Chat Getting To Know Your Peers

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[00:00:00.000] Hello. Welcome to another Global Peer Financing Association Peer Connections podcast. I'm Dan
[00:00:07.720] Kiefer, an investment manager with CalPERS, also known as the California Public Employees
[00:00:12.900] Retirement System, and I'm going to be your host today. I'm excited to host this podcast
[00:00:17.780] because it's an area that I started my career in, credit, before moving on to manage a multi-billion
[00:00:24.460] dollar securities lending program over the last 20 years. For those of you who are becoming
[00:00:29.720] familiar with the GPFA, you know much of the group's focus is growing and evolving the market
[00:00:35.540] through education and information sharing in order to better support more peer-to-peer or
[00:00:40.740] beneficial owner-to-beneficial owner transactions in the securities financing industry. One of the
[00:00:46.980] hurdles that we at CalPERS and other beneficial owners have had is we've had to address how to
[00:00:52.240] consider our peers from a credit standpoint. Most of our peers are non-traditional, unlike banks or
[00:00:58.480] broker-dealers, which typically maintain a credit rating. As part of our learning series in the
[00:01:03.300] space, we rediscovered an old friend in the securities lending market. And today, I am
[00:01:08.780] pleased to have Mark Faulkner join me as a guest speaker. Mark is co-founder of Credit Benchmark,
[00:01:15.280] which is a newer company in the credit world that is bringing together internal credit risk views
[00:01:20.760] from leading financial institutions. Before entering the credit market, Mark was a very
[00:01:25.480] familiar face in the securities lending industry, having previously founded Data Explorers,
[00:01:30.880] which is now IHS Market Securities Financing Data Group. And Mark's never shy to share his
[00:01:37.400] thoughts or views. And I'm really looking forward to chatting today. So welcome, Mark.
[00:01:42.460] Thank you very much, Dan. It's a pleasure to be with you.
[00:01:45.300] So why don't you give us a little background on how you started Credit Benchmark, why it came
[00:01:51.480] about? Well, thank you. The genesis of the idea was quite simple. After the credit crunch, every
[00:01:58.900] bank, financial institutions, politician, regulator, policymaker reflected on the way that the world
[00:02:05.880] looked at credit and looked at credit information. I think there was a general consensus, if you'll
[00:02:11.800] excuse the pun, that it could be done better. And that many of the largest banks of the world
[00:02:17.400] became their own and had to set up their own mini internal credit rating agencies. Some of them
[00:02:24.940] with many hundreds, if not thousands of analysts working on it. This data within the banks was to
[00:02:31.120] effectively provide them with their own skin in the game protection against unforeseen credit
[00:02:36.600] events. And one of the challenges under Dodd-Frank, particularly for the American banks, but elsewhere
[00:02:41.740] around the world, was that there was no real basis upon which you could compare that data.
[00:02:46.460] So having built in data explorers at what we call a contributed database business model business, Donald Smith and I decided that we thought we could do something similar in the credit world by bringing together, aggregating and anonymizing this data in different pockets around the financial markets.
[00:03:05.520] Could we create a consensus, a place where people could compare, benchmark, et cetera, and understand and liberate in an appropriate way information on credit?
[00:03:16.520] That was the idea. The basic idea was that there was information from skin in the game providers that was professionally compiled to protect their organizations, overseen by regulators that was going to be useful not only to the banks, but potentially to non-bank financial institutions.
[00:03:34.760] Credit is very, very important, as you know well.
[00:03:39.060] And what credits are you actually pulling in here?
[00:03:41.380] Are these all rated credits?
[00:03:43.120] Are they non-rated credits?
[00:03:45.380] Smaller entities, larger entities?
[00:03:47.160] How many entities have you pulled in?
[00:03:49.400] Okay, so that's a great question.
[00:03:51.060] On a monthly basis, we're taking in around about a million data points from the contributing
[00:03:55.660] banks.
[00:03:56.240] The number of banks contributing now is about 38 and rising.
[00:03:59.400] And what we're looking for is situations where those banks contribute data on the same entities.
[00:04:06.300] And when they do contribute that data on the same entity, when three of them do so, we can publish what we call a credit consensus rating.
[00:04:14.520] Now, not every small organization entity in the world will have three of the largest banks giving them a rating, but many, many do.
[00:04:22.820] So the entities range from sovereigns, munis, governments, corporations, financials, the subsidiaries and trading companies of those organizations, and of particular importance to organizations such as yours and those interested in securities finance, to the funds, the sovereign wealth, the mutual funds and the pension funds as well.
[00:04:42.600] So where a bank has a credit risk to an organization, they are required to model that risk and do the best they can to estimate the risk associated with that counterpart's credit risk.
[00:04:54.620] It's that data that we compile. It covers small, medium and large organizations.
[00:05:00.600] When it's small and medium, very often we're talking about aggregated data at an industry or sector level.
[00:05:06.700] But when it becomes large, we're talking at an entity level.
[00:05:10.840] So then the pension funds would have data provided by the banks.
[00:05:15.480] Yes, to the extent that the banks have a relationship with a pension plan and have credit risk to those particular funds,
[00:05:22.420] they would calculate a probability of default, the likelihood of that counterpart not being there in a year,
[00:05:28.540] which is relatively remote, I imagine, from a pension funds perspective, but varies fund to fund.
[00:05:34.380] So when you take a look at the credit rating of pension funds or asset owners and compare them to the bank,
[00:05:39.500] how does that credit stack up? It would depend. And you might imagine that I would say that no
[00:05:44.780] banks are created equal, no pension plans are created equal. But I think it's fair to say that
[00:05:50.700] the breadth of credit for the GSIBs is reasonably narrow and high quality. But the key thing is that
[00:05:57.720] you often don't deal with the name over the door. You're often dealing with the subsidiary
[00:06:01.960] underneath the name. So it's one thing to ask about a banking credit. It's another thing to
[00:06:07.460] be talking about the subsidiaries and the broker-dealers and the different specialist
[00:06:11.060] entities that those banks have. Pension plans, typically, reasonably high quality. Not always
[00:06:16.880] the case, but a very broad trend would be to say, you know, good quality.
[00:06:21.260] Double A, triple A quality.
[00:06:23.380] Yeah, very few triple A's given out anywhere. It's a little bit like sort of scores time. Not
[00:06:28.440] many people get the 10 out of 10 or the 20 out of 20, but triple A's are out there. Very, very,
[00:06:33.760] very few in the banking world, slightly more, but not very many in the pension plan world,
[00:06:38.980] typically the sovereign wealths. And how would you compare this data to CDS or even credit rating
[00:06:46.180] agencies? Okay, I'd split those two things in two. CDS first. CDSs are having a tough time.
[00:06:53.320] There aren't so many of them that are liquid and really viable at the moment. I think that they
[00:06:58.480] would also be saying the data is not necessarily a pure reflection of creditworthiness. It is a
[00:07:04.120] reflection of trading activity, hedging activity, etc. And there are directional moves at play that
[00:07:09.860] may not necessarily just be purely credit. So I would view CDSs as less liquid than perhaps
[00:07:16.380] they would be. The number of them available is not so high. Maybe there's three or four thousand
[00:07:21.360] names out there. There might be 1,500 that would be liquid. We're gathering and producing data on
[00:07:27.100] 55,000 entities at the moment. So better breadth, better liquidity, and better coverage.
[00:07:34.480] The rating agencies, it's a very different story. The rating agencies are typically paid by the
[00:07:40.920] companies that they rate. So the issuer pays, and that's fraught with challenges, which I'm sure you
[00:07:46.660] and colleagues are familiar with. We've all seen and read the big short. I think the point I would
[00:07:51.860] make with regard to the rating agencies is that their propensity to try and be accurate is tempered
[00:07:59.900] by the fact that they are paid by the issuers. So sometimes you'll find that they struggle to
[00:08:05.320] downgrade at the pace that those with skin in the game say the banks might downgrade. So there's a
[00:08:11.680] different behavioral dynamic there. There's a trade-off between stability and accuracy. And I
[00:08:18.980] think credit benchmark plays quite well, as you might imagine, I would say that, but it plays
[00:08:22.840] quite well in juxtaposition to those two alternative sources of data. I think it's a complementarity
[00:08:29.140] that the three sources are helpful and useful, and the comparison between them is constructive.
[00:08:36.280] I wouldn't necessarily say that one thing should be seen in isolation as a solution,
[00:08:40.580] as a nirvana for everything, but they are different. So you're saying from kind of a
[00:08:45.860] credit mosaic, this is a critical piece to kind of put in your credit tool bag.
[00:08:50.760] I think so. I think what we're doing is filling in a gap for the unrated. There are some strong
[00:08:55.880] dynamic qualities to the data that we gather. We're gathering data on a fortnightly basis at
[00:09:01.980] the moment. Recent turn stresses in the market have encouraged our banks to focus upon sending
[00:09:07.920] in and wanting to see data collected more frequently. We were previous to COVID collecting
[00:09:13.180] on a monthly basis. We're now doing it every fortnight, which is a big testament to the team
[00:09:18.140] at Credit Benchmark. So that's quite quick in the context of the credit markets. I think that so
[00:09:23.600] speed is interesting, diamond Amazon is amazing, but the coverage is huge. There are very few
[00:09:28.340] sources of data on the subsidiaries of the major banks because they don't issue paper
[00:09:33.700] of the funds that people do business with. The buy side is very curious for a whole host of
[00:09:41.440] different reasons about what the street makes of their credit worthiness, because it impacts the
[00:09:46.100] liquidity that they can access, the pricing that they get, the scale of the transactions they can
[00:09:51.780] do. And I think all too often, it's not just about a fund looking or a fund manager looking
[00:09:56.920] out at the world of counterpart risk. This is almost like a two-way mirror where you can see
[00:10:01.440] a reflection of what the street thinks of you. And I think that's very useful, particularly with
[00:10:05.460] Basel IV coming up, because unrated funds, unrated buy-side accounts in Europe will attract a 100%
[00:10:12.560] risk weighting, which will make dealing with them very expensive in the future.
[00:10:17.020] And your data set, how long have you been capturing this data?
[00:10:20.800] It's just over five years. We started the company in 2013, and we started publishing our first data
[00:10:27.820] set with five banks contributing data in May 2015. So we're just cracking into our sixth year now.
[00:10:35.460] statistically important. That five years of back history is very important.
[00:10:40.000] I know one of the challenges that we had when we took a look at our peers was coming up with a
[00:10:45.720] process to rate unrated counterparties. And part of the Global Peer Financing Association,
[00:10:52.940] we've embraced credit benchmarks as one piece of the puzzle to bring some of these non-rated
[00:10:59.580] counterparties in to know your client. And it's been received well internally from both
[00:11:05.260] our organization and our peers. Do you have any newer clients or any large sovereigns that have
[00:11:11.420] been onboarded or using your product? We're making progress on a global basis from New Zealand to
[00:11:17.700] Australia, from Europe to America. But if I may, and I tell you this to your face, whether it's on
[00:11:24.800] Zoom or in your face and you and to Rob and to Jerry and to Chris, I think what you've done to
[00:11:30.740] create the Global Peer Financing Association is an amazing achievement. I know it's taken you
[00:11:35.640] ages. I know it's been an enormous labor of love. And I know it's maybe taken six years to get where
[00:11:40.820] you are. But I think it's a testament to what a damn good idea it is that you doubled in size in
[00:11:45.940] six weeks. Well done. Because this is something that has arrived at the right time for a whole
[00:11:54.240] host of reasons. I don't think it's something that the banking and brokerage community see as much of
[00:12:00.640] as a threat as they used to. I think they were terrified of disintermediation. But I think there's
[00:12:05.860] some transactions that you and your colleagues can do, your peers can do, that don't suit the
[00:12:11.620] old way of doing business. So we're really pleased. We're very proud to be associated with the
[00:12:17.200] association and stand ready to provide as much information as we can to fuel transactions,
[00:12:24.720] different types of collateralization. There's a whole host of different things that we can do.
[00:12:29.020] The client base for our business is not just the funds, but the broker dealers and the agents, because they want to understand and manage and communicate and electronically automate the credit worthiness of the funds with whom they're doing business with.
[00:12:45.980] The funds are not just unrated for you, Dan, and your colleagues in the peer community.
[00:12:50.200] They are unrated.
[00:12:51.640] And actually shedding a light on that unrated universe is something that we're very proud of and we're continuing to do.
[00:12:57.900] Our biggest single drive at the moment is to gather more data, more data on the funds
[00:13:02.520] so we can make sure that it is truly the wisdom of the crowd rather than a small coverage
[00:13:07.420] for a name, building up that depth, building up that credibility.
[00:13:11.780] The biggest thing that's happened to us as an organization in recent times would be the
[00:13:16.840] Bank of England and the Majesty's Treasury contacting Donald Smith, my business partner,
[00:13:21.020] one Friday afternoon and saying, a couple of the UK banks have suggested we give you
[00:13:25.780] a call.
[00:13:26.140] we think they might be able to help us in the COVID crisis, which is an astonishing thing for
[00:13:31.540] a small young business to get a call like that. We were able to provide data to help the Bank of
[00:13:38.380] England and Her Majesty's Treasury manage the COVID commercial funding facility. Previously,
[00:13:44.120] there'd been 350 UK companies with a credit rating in the public domain of the kind that you'd be
[00:13:50.580] familiar with. We were able to provide 3,500 commercial credit consensus ratings, and we were
[00:13:58.140] able to send across 140,000 rows of data on UK companies that helped inform the decision making
[00:14:05.600] on the liquidity, solvency, and survival of those companies. So to answer your question, how does it
[00:14:10.800] range and what are we doing? That's something that we're very proud of too. Well, it seems like a real
[00:14:16.060] evolutionary step in the whole credit business. Yeah. I mean, even my mother thinks it's a good
[00:14:21.680] idea. I don't mean even my mother. I mean, she can understand that information helps empower
[00:14:28.760] decision-making. Well, and as you said, skin in the game helps empower decision-making. And I
[00:14:34.520] think that's one of the key elements of this is it's not a pay-for-play model. It's skin in the
[00:14:40.180] game model. And the data set has been around for five years. So hats off to you. Well, thank you
[00:14:46.180] very much, Dan. Thank you for joining another episode of Peer Connections by the Global Peer
[00:14:51.500] Financing Association. We hope you have a better understanding of the various factors, including
[00:14:57.380] credit to consider when expanding your program or counterparties to include non-traditional players.
[00:15:03.240] And if you have any suggestions or topics you'd be interested in hearing about for future episodes,
[00:15:07.580] please reach out to us via LinkedIn or our website, globalpeerfinancingassociation.org.
[00:15:15.040] To stay up to date on the GPFA Peer Connections, you can also subscribe and you can listen to all
[00:15:20.200] these podcasts. We really thank you for listening and thank you, Mark, for joining us today.
[00:15:26.720] You're welcome. Thank you very much.