A Long View of Banking Industry Disruption (#36)

Structural Shifts with Marc RUBINSTEIN, former hedge fund partner and author of the Net Interest newsletter.

We sit down with Marc Rubinstein, a former analyst and hedge fund manager who currently authors Net Interest — a weekly insight and analysis newsletter on the world of finance. Each note of his newsletter explores a theme currently trending in the sector, whether it’s FinTech or economics, or investment cycles — and today, you are going to hear about a little bit of everything. Marc and Ben Robinson discuss the history of equity research and where it’s at now, whether current regulation is tilted too far against banks, the twofold challenge facing challenger banks, the past and future of embedded banking, the four key differences between investing in private companies versus public, the potential financial services game-changers that could happen this year that people are not talking enough about, and more. 

Full transcript
Structural Shifts with Marc Rubinstein

There’s a lot of overlap between what a very, very good equity analyst does and what an investigative reporter does.

[00:01:26.21] Ben Robinson: So, Marc, thank you so much for agreeing to come on the Structural Shifts podcast. We’re a really, really big fan of Net Interest and so, we feel very, very privileged to have you on the show. If you don’t mind, can we start by you just briefly introducing yourself and giving us a short summary of your career so far, just because I think that will be relevant. I think we can use parts of your career to frame some of this discussion.

Marc Rubinstein: Sure. Well, no, thanks, Ben. It’s great to be on. I’ve been in the realm of financial services for 25 years. I started as an equity research analyst, analyzing banks — I spent 12 years doing that — I spent 10 years investing in banks as a partner of a hedge fund exclusively focused on financial services, stocks globally, publicly-traded, long and short. And then since 2016, I’ve looked at financial services out of sheer interest. It’s something that it’s difficult to shake off. And so that’s basically it in a nutshell.

[00:02:22.07] Ben: Good. Okay, so we’re gonna pick up on different aspects of that. But I wanted to start with the equity research part because one of the newsletters I’ve most enjoyed — I mean, they’re all brilliant, but one of the ones I’ve most enjoyed just because it had personal resonance for me because I was once an equity researcher — was the one where you talked about the history of equity research. If you don’t mind, maybe you can just talk a bit about how sell-side equity research works, because it’s kind of a strange model where, you know, fund managers have access a lot of times to internal research, but yet they source it from a third party; that third party doesn’t charge directly for that research. So it’s kind of a strange model. So if you don’t mind just talking about sell-side equity research, and also how it’s changed, right? Because I think, you know, if I were to put it crudely, it’s gone from a really well-paid, really highly-solicited job to something which is not that anymore, right?

Marc: So, I started out as an equity research analyst in the mid-’90s. And I was not particularly familiar with it as a professional opportunity. It wasn’t something that I, at college, realized that it’s something that I wanted to do. I wanted to go into finance and I participated in a graduate training scheme at a bank — Barclays Bank — it was an investment banking subsidiary of Barclays at the time; and went through various placements across the bank, not dissimilar to the way graduate training programs work today. I do need to say though, any listeners that have watched the series industry, it was nothing like that. But I ended up in equity research and spent, as I said earlier, 12 years there. Now, the way equity research was conducted then was very, very different from the way it was conducted prior to that, and the way it’s conducted today. Equity research emerged in the 1960s, 1970s as an add-on to the core brokerage business that brokers offered their clients. At the time, commissions were very, very heavily regulated and the only way to compete was through ancillary services. And so, brokers offered equity research as one of those ancillary services. They gave it away for free. It was a marketing device in order to attract brokerage business. And that was the case when I entered as well. At around the time — so, we’re going into the ’90s, into the late ’90s and early 2000s — another side of the investment banking business was booming, and that was M&A — an equity underwriting. It’s very topical now to go back 20 years and look at the tech boom of ’99–2000s, given the conditions we’re currently seeing today. But the way it worked back then is that companies would want to IPO and they would choose their investment banks, not dissimilar today. And one of the features that they would look for in selecting their investment bank was the quality of the research that that investment bank produced. And so, rather than exclusively being an ancillary business to the trading business — which was the case, historically — increasingly research became an ancillary business to banking, as well. And as a result of that, equity research attracted a new revenue stream and was, therefore, able to grow. And in the late 1990s, this business of equity research grew, costs increased, a superstar culture emerged.

The markets are not efficient, and Signal and Zoom are great recent examples of that. And to the extent that they’re not efficient, research does have value and those inefficiencies typically emerge the lower down the market cap curve one goes.

Marc: The piece that you referenced, I talked in there about a telco analyst who worked at Smith Barney in New York, called Grubman, and he wrote on telco stocks like AT&T, and he was coerced by his boss, Sandy Weill, who was the Chief Executive at Citigroup, to rethink his view — it’s kind of a euphemism for upgraded to a buy — on one of the stocks under his coverage. The 2000s came along, Eliot Spitzer, who was the Attorney General in New York, took a view that actually there was a massive conflict of interest at play here and he tried to dismantle that construct within equity research. The problem is that the cost base was still there and the cost base didn’t have now a revenue stream to attach to. And so, you had like an orphan kind of wandering around looking for kind of a foster family; this cost base was looking for a new revenue stream. For a short period, it stumbled upon proprietary trading. So, the period between 2001 probably, 2006, 2007, investment banks built very large prop trading businesses, internally, and equity research was a feeder mechanism for some of the ideas that they would put on. And then, the financial crisis happened and that business disappeared as well. Ultimately, that was also dismantled by regulators through Volcker amendment to the Dodd-Frank Act of 2010.

Marc: So, throughout this entire history, you’ve had this kind of valuable resource — inherently, experts looking at companies and issuing investment recommendations through the process of research on those companies. Yet, in and of itself, it was a business that found it very difficult to reflect a model that was able to pay it sufficiently. Which brings us to today and you’ve had another bout of regulation — this was in Europe about three years ago — in 2017, you had MiFID II, which required an unbundling going all the way back to the ’60s, where this process started, where research was ancillary to trading, regulators in Europe came along and said, “Actually, there’s a conflict inherent in this as well.” Certainly in the degree to which it paid for by institutions, and yet again, the business has gone through a kind of an identity crisis. And that’s really where we are today.

[00:08:35.08] Ben: If you like, it’s been sort of hammered by three waves of regulation, right? So, first, Eliot Spitzer, then Volcker, now MiFID II. One of the things that’s changed is you said, I think in your newsletter, you talked about how much Grubman made, right? I think he made like $50 million or something in the space of a few years, which would be unheard of now. So, you know, payback has gone down. But the other thing that’s notable is the amount or the volume of equity research, which has dramatically changed. I mean, you talked about go-to Credit Suisse, an investor meeting, they were like, you know, hundreds of analysts there. I remember, you know, going to SAP investor meetings, there would be 100 plus analysts in the room. And so, clearly, we went from a situation where there was oversupply — do you think we’ve tipped to the opposite situation where there’s a lot of undersupply, particularly of smaller cap stocks?

Marc: For sure there is an idea that there’s an undersupply research out there, that a lot of it is being certainly a shakeout within the industry. Now, it was arguably overpaid, to begin with — and certainly Grubman, did he merit the millions of dollars that he accrued? Probably not, almost certainly not. Possibly not from a compensation perspective, but from a resource allocation perspective to the industry, we may have under shored on the other side. And it’s not dissimilar. Maybe the analogy here is the media, is the press and actually there’s a lot of overlap — and I draw this out in that piece — between what a very, very good equity analyst does and what an investigative reporter does. And there’s a public service here, there’s a public good here. You know, certainly what the research analysts were doing — so Wirecard, very well-known fraud. Interestingly, the credit, rightly so, for uncovering that fraud has gone to a journalist, Dan McCrum from the Financial Times. But there are other cases, and certainly, there were a couple of analysts. Some of them didn’t cover themselves in glory, but there were a couple of analysts who also got that right. And there’s kind of a public service to looking independently, without being influenced by the companies themselves and the management of those companies, nor by other constituencies, for putting out independent research on companies, for doing their job.

[00:10:49.16] Ben: It’s interesting that you call that public good, because it suffers from the same shortcomings of a public good, in the sense that it’s difficult to exclude access to that research once it’s in the public domain. And it doesn’t stop you from consuming. In many ways, it does have the properties of a public good, which means it suffers from the free-rider problem and in general, sort of under-provision.

Marc: Absolutely right. And in addition, it’s difficult before the fact to know if it’s any good or not. Clearly, the analyst report that said that Wirecard was a fraud, after the fact we know was very, very valuable research. The report, which would have arrived on the same day, on the client’s desk which said, you know, Wildcard is a great company and it’s got huge upside — again, after the fact we realized it’s got negative value. But at the time, the decision rests on the recipient to discern between those two. And that’s not easy. And it’s not easy as well, to know ultimately, where the value is, in this. There’s a lot of noise out there.

[00:11:53.12] Ben: I want to come back to Wirecard in the context of, you know, bank regulation, and whether it’s a level playing field. But just on this idea of, you know, perhaps under-provision of research. Do you think that creates arbitrage opportunities? So, for example, do you think it’s now easier to create alpha investing in small-cap stocks? Because there’s a high return on doing that research yourself, whereas before, that was not the case.

Marc: I think, yes. So actually, just recently, there’s two companies called Signal — Elon Musk tweeted quite recently that one should be buying Signal, he was a big proponent of Signal; readers picked up the wrong Signal. Actually, early on in the pandemic, the same thing happened with Zoom, there were two Zoom companies. The point here is, you know, the markets are not efficient, and Signal and Zoom are great recent examples of that. And to the extent that they’re not efficient, research does have value and those inefficiencies typically emerge the lower down the market cap curve one goes.

It’s incredibly difficult for any investor to change their mind.

[00:12:57.18] Ben: There’s a quite high proportion, certainly relative to, in the past, small caps that no longer have any sell-side equity coverage, right?

Marc: Yeah, that is right. And it’s not great, either. Now, the flip side is that some of it has shifted over to the buy-side themselves. That was a trend that was already in place from the institutional perspective. But what we’re now seeing because of the ability to share ideas more freely, through the internet and platforms like Twitter, and also dedicated platforms, like Sub-Zero, and the ability for individual investors or smaller, emerging institutional investors to get access to infrastructure — maybe they can’t afford Bloomberg at $24,000 a year, but they can afford other apps and other facilities — more research has been generated. And you know, actually, this brings us back to the model, it is quite interesting. So, the old research model was ‘we’ll give it away to everyone for free and we’ll attract some revenue dollars through trading commissions’. More recently, post-MiFID II, that translated into, ‘we will just service, say, the top 100 customers who are prepared to pay for it’. There’s a trade-off now between generating thousands of dollars from 100 customers or via the internet, particularly where the market might be individual investors who… And whether this is cyclical or secular or not, at this stage, I don’t know. But certainly, retail engagement in the market is increasing. They’re not going to pay thousands of dollars for institutional research but the quality of what’s available on the internet is very, very high, and maybe they’ll pay $20, $30 a month, and tens or hundreds of thousands of those… You know, there’s a good newsletter writer called — there’s a number of good newsletter writers out there, but a number of them, they offer, in my view, institutional-grade research, particularly in the technology space, and they charge $10, $20 a month for it. But they have hundreds of thousands. And I actually would be interested to see their p&l against a traditional equity sell-side research business, given lower costs and broader reach.

[00:15:21.13] Ben: I was actually gonna highlight this as a second arbitrage opportunity, which is one might be there’s more potential to make money from small caps than there was in the past, but the other one is, I think — you know, I don’t want to suggest that this is the model for Net Interest, but a bit where you can almost crowdsource almost as good or maybe even better, in some cases, research from the internet, which is, you know, the sort of the bottom up, you know, kind of organic production of research to fill the gap. Because, I agree, and you see the same thing also in investigative journalism and other content areas, which is, you know, your choices are either to pay a subscription for the FT or to subscribe to newsletters, right? Because these things are sort of mushrooming. And, you know, I mean, that’s another phenomenon in the way that you’re embodying, which is you publish your newsletter on Substack, and in some ways, you’re kind of contributing to this gap that’s been left as equity research has become or is provided to a lesser extent than it was in the past.

Marc: Yeah, I think that’s right. And, you know, it comes from just this, I don’t like the word ‘democratization’ that people use, but it certainly plays into our theme. You know, clearly, the advantage that… And I remember when I was an equity research analyst, it was at BZW, which you mentioned, which was a subsidiary of Barclays. And I was looking at Swedish banks in 1996. They kind of emerged from a crisis, they’ve been re-privatized, they’ve been re-IPOed, and there was kind of a recovery theme in a way. And I stumbled upon — it was kind of the early days of the internet, we had access to the internet, but what was on there was difficult to find. There was no search, it’s kind of the days before Google. And I kind of stumbled across a document written from the Central Bank of Sweden, the Rik Bank, which provided very interesting data on kind of banking volumes. It was faxed to me by somebody in Sweden. I literally, I was working at home, it was a Saturday, I was working at home. I couldn’t read it because it was Swedish. Google translate didn’t exist. I ran around to my local bookstore, bought a Swedish-English dictionary, translated this thing, put out a piece of research on this finding that actually loan growth in Sweden, based on this data was greater than anybody anticipated. And it was it. I stumbled across something purely informational. And clearly, the friction to getting that information now is just non-existent. Everybody has all of the information all of the time hence, there’s no arms race in place to get new sources of information. Kind of alternative, dangerous nets. But you know, that’s all done. What’s happening now is the same thing is happening to analysis. Now, people, again, through the ability to meet in the market square via whether it’s Twitter or any other kind of platform, there might be a great analyst who’s based in… I mean, I know there’s a great equity research analyst, who I read called Scuttleblurb, he is based in Portland, Oregon, far from Wall Street, and there are people just all over the world in India, in small towns in England, all over the world, all analyze it. So, they’ve got the base level of information and the degree of analysis they’re doing now is institutional grade, and it’s accessible.

[00:18:50.08] Ben: It was just before the financial crisis that you switched from being a sell-side analyst to working for a hedge fund, if I’m right. Presumably, that was a great time to have the ability to go short on banks. And I just wondered, you know, when you were living through it, how evident was it in advance of the crisis that it was coming. Could you presage that, you know, we were gonna have this big crash, or was it really as sort of sudden and unexpected to you as it was for the people that weren’t as closely following that?

Marc: It’s a really interesting question. It would be easy for me to say yes. I would say the way I would finesse it is yes, we saw elements of it. But it’s important to remember, somebody once said, ‘causes run in packs’. There’s never a single cause. I think it’s lazy analysis. And I see it and often politically motivated for people to say the financial crisis was caused by x — and x typically correlates with one’s political inclination. X could be, you know, greedy bankers, or x could be people borrowing too much or x could be sloppy regulation or x could be too much leverage at the banks or whatever it might be. There’s a whole range of reasons. And ultimately, it was the confluence of lots of those things that happened to create the crisis. Although we — me and my colleagues — identified some strands of it to have predicted the degree to which it all coalesced, you know, in kind of, you know, let’s say, October 2008, I think that was difficult to predict. But that’s never… There’s complex reflexivity to it. It happened, I remember watching, I vividly remember watching the debate in Washington around passing the torpid. It was controversial. And I remember specifically it went down. But because it went down, the market went down, and so, reflexivity because the market went down, then when it came back for another reading because the market had gone down, incentives have shifted. Predicting kind of reflexivity in advance is difficult. Having said that, the worst things we saw. So back in, you know, we were short. I mean, back in 2006, we were short some subprime companies. I went back through my — I’m not a Facebook user anymore but when I canceled Facebook, I downloaded all of my posts, and there was one post in July of 2007, where I cautioned about an impending financial crisis. We were short, Fannie and Freddie, and all the rest of it. Just an observation about investing broadly, and, going into more detail on the crisis, but investing broadly, it’s incredibly difficult for any investor to change their mind. And I think there were a number who were negative; a lot stayed negative beyond March 2009. But the fascinating thing to me is those that there were kind of negative, and then they switch positive. And just taking a step back away from financial services, but generally, investors’ ability — the very, very best investors, their ability to adapt to changing conditions like that, continually, it’s very, very difficult. And I think, you know, history is littered with investors who have got two or three calls right, but to be able to retain an element of persistence, through those changing dynamics, it’s very, very difficult.

[00:22:38.06] Ben: Yeah, I think it could be a rabbit hole but I would argue almost that potentially the greatest of all investors, Warren Buffett, has not been able to adapt this strategy to some extent to the digital age, because he’s still buying sort of, you know, asset-heavy companies with a lot of supply-side, economies of scale, and so on. So I think it even happens to the best when there’s a paradigm shift.

Some of the consternation of bankers right now is that tech companies are getting away with stuff that they just wouldn’t be able to get away with.

Marc: True. And to our conversation earlier about small cap, large cap, I mean, certainly, his performance hasn’t been as good in the recent past, compared to prior periods in his history. And he’s got longevity, very difficult to compare him to any other investor, because I’m not sure there’s any track record out there that’s as long as his. But he made the point recently — he actually made it ’99 — he made the point, there’s a great quote in ’99, where he was talking about if he had a million dollars to invest, you know, he’d crush the market because of his ability to access small cap, but it could be a reflection on your point as well.

[00:23:36.10] Ben: It might be both because you actually wrote another great newsletter about the curse of managing too much money — it becomes harder and harder to achieve a return on much bigger sums.

Marc: Yeah, exactly. That’s another curse — I call it the Zuckerman’s curse. Gregory Zuckerman, who’s a great writer, has written a number of books about — he’s written two, in particular — hedge fund managers. And they’ve been published. Clearly, he’s been attracted to them because of their profile, and their profile is a function of their performance. And therefore, there’s a direct line between them showing good performance and him writing a book. Actually, there’s more nuance to that. It’s not them having good performance, is them having good performance and being big enough for him to notice. One of my favorite investors out there is Hayden Capital. A guy called Fred Liu, based in New York, was up 222% last year, but he’s small, nobody knows of him. And the curse is that over a certain size it’s difficult to sustain that performance on an ongoing basis. Actually, it’s worse than that because typically, after a good year, the money then comes in. And investing isn’t mean reverting but certainly, there’s an element of… It’s only as difficult to sustain very, very good performance across multiple time periods.

[00:25:05.17] Ben: Do you know what Zuckerman’s next book is about? Just so we know in advance.

Marc: That’s a good one. I feel bad because I’ve read them all. I mean, they’re great books. It’s the writing on the wall.

[00:25:20.11] Ben: I’m gonna ask you, a bit like the financial crisis question I’m gonna ask another question, which is gonna be, I think, impossible for you to answer in retrospect, without any sort of cognitive biases, and so on. But you wrote another newsletter, which I really, really liked, which was called “The End of Banking”. How obvious was it now, in retrospect, that post-financial crisis, financial services was just not going to be the same again, right? Because their profitability is not the same. It doesn’t represent anywhere near the same size of, you know, as the composition of the index in which it sits. And so, it just seems like the financial crisis in a way was like, you know, the peak. And you know, maybe as you said, this may be cyclical, it may be that in the future, it becomes as big as it was and as profitable as it was. But certainly, it seems much more structural, for the reasons I think we can talk about now. But when did it become evident to you that the sector becomes structurally less sexy in a way?

Marc: I’ll be honest with you, it took me a long time. My mental model — I mentioned Swedish banks earlier — my mental model was that banks — and this has been true historically, and in my working memory through the Swedish banks, they went through a period of crisis, they’d be recapitalized, they’d come back to the market. Typically, they’d be a lot more conservative and so, underwriting would be tighter. They would then generate huge amounts of capital and then recover. There was a singularity inherent in the industry. They would crash, they’d be recapitalized and then recover. And that was my mental model. I remember at the time being told — we talked about the tech boom from 20 years ago, ’99–2000. We’ve talked about that already. I remember in 2010, 2011, a strategist who’d experienced the tech boom — I mean, I experienced a tech boom as well but I wasn’t directly involved in it — I remember a strategist at a bank saying to me, “The market has to cycle through a generation of investors to forget what happened, to forget the scars of the previous crisis for any kind of return to normality.” And I didn’t believe it. I said, No. You know, so I was sanguine about the extent to which the market recovered. I underestimated a number of things. I underestimated one, how low-interest rates would stay for how long. Two just the… You know, and I often think, actually, for the investment banks, worse than 2000 for them, and their long-term from a strategic perspective, worse than the experience they suffered in 2007–2008m how well they performed in 2009, hurt them longer term from a strategic perspective more, because the backlash was then huge. It was kind of the political disgust, they made so much money in 2009, and that increased the scope of regulation, which muted them for many, many years after that. So, I underestimated regulation, and then we can talk about disruption. It’s difficult. I’m not sure I underestimated that but that was clearly another factor.

[00:28:45.28] Ben: Maybe let’s unpack those things because I think interest rates, I think, you know, we won’t know for a long time if this is a structural or a cyclical factor. But it seems like the re-regulation of the banking is a much more structural thing. As is this one other thing which I don’t know if it’s permanent or not, but you talk about it as governments inserting themselves into the cap table of banks. This idea that they become almost like an arm of government in some ways, right? Because, you know, particularly during the COVID crisis, you know, that we used the direct funding and also, you know, they just don’t have the same control they used to have over capital allocation. So, again, I don’t know if that’s a structural or a temporary phenomenon, but certainly, one of the things that’s been so weighing on bank valuations. But the re-regulation part, I think is probably much more structural. And the question I wanted to ask you about that is, you know, I think we could probably talk about regulation in different buckets. So part was about making banks safer, part was about some introducing more transparency, but the part that I think is now looking a bit kind of controversial in a way is all the regulation is aimed at introducing more competition to banks. You know, so, a PSET, for example, almost seems like that was mistimed because I think what the regulators perhaps hadn’t appreciated because the lag, was that there’s just been so much new competition from non-banking players, right? So I wonder almost in hindsight whether regulators would still introduce some of the regulation they’ve done to introduce more competition into banking because it seems like almost now, not necessary. And potentially unfair. You know in your last newsletter, you talked about that letter from Ana Botín to the FT. And, you know, some of that I thought was quite justified, some of that criticism of recent regulation and the absence of a level playing field. So, it’s a long question, but do you think almost like some of the regulation are tilted or was too far against the banks?

Marc: Yeah, I think it is. I think it’s a truism that regulators typically fight the last battle. And not just regulators. I think it’s a response to, you know, I mentioned earlier, you know, my mental model for the period after the financial crisis was dictated by the last battle, which was the Swedish banking crisis of mid-1990s. So for regulators is the same. They are very, very focused on fighting that battle. And equally, I think it was a truism that whatever the cause of the next financial crisis, it was never going to be the same ingredients to the one in 2007, 2008 to 2009. By the same token, we’re not talking about a financial crisis, here. We’re talking about as you put it out, a playing field. But certainly, the combination of low-interest rates, and a playing field that’s not level was very, very negative for the banks. And there was a degree to which maybe regulators understood that, maybe they didn’t. If they understood it, certainly there was no political motivation to circumvent it, because there was this culture about wanting to punish the banks. But you’re right, you know, this point about they insert themselves, the role of any chief executive of any company, pretty much exclusively is capital allocation. And from an investor’s perspective looking at banks, if they don’t have the capability to manage their own capital allocation because regulators can come in… I listened to a debate recently, between some sell-side analysts, and market participants, and representatives from the Bank of England. And the view of the Bank of England — and I don’t think they’re unique here. I think it’s a view of many regulators that prevented their banks from paying out capital, in March of 2020 was only temporary. But you’ve spoken about scars and the degree to which scars can be left, and from now on, any investor that is investing in a bank understands that at any point, particularly given the capital framework that was put in place to protect banks from unknown. I mean, clearly, a pandemic was an unknown, but that’s what capital is there for. It is there to protect against the unknown. It is not there to protect unknowns, except for a pandemic, or unknowns except… All unknowns, whatever they might be. And so, even with that in place, for them to come in and say, “Actually, we’re going to take charge here of capital allocation” that sends out a very negative signal.

One could have made an argument 10 years ago that banks have got more data, more valuable data. I guess Amazon has got shopping data, Google has got search data, Facebook has got social data, and some overlap between them. Banks have got financial data, and what data is more valuable than financial data? And yet, they’ve been restricted, rightly, from their ability to monetize that.

[00:33:26.20] Ben: Plus, they’d already introduced regulations to ensure that there were more buffers, that you had to protect against losses earlier in the cycle. And so, to some extent, it was almost like a double hit on their ability to allocate capital, right?

Marc: Exactly. Exactly. So we’ll see the extent to which… There’s a view out there that we haven’t seen the worst, that maybe over 2021, when things begin to recover, small businesses will see unemployment. And there’s a view out there. The other thing is, again, a competitive point of reflexivity. Back in March, the regulators didn’t anticipate — to be somewhat fair to them — the degree to which monetary policy would come in, and fiscal policy would come in, but once they had come in, there was a degree of caution that was maybe unwarranted. And again, they might argue, who cares. We’re hurting some bank investors here, but who cares? But ultimately, from the perspective of a bank investor, there’s some long-term issues here. And actually the ultimate bank stop, and it worked in 2009 is that investors, the private sector bails out the banks, the private sector puts more money in because it knows that actually, at this point in time, we can draw a line and that future returns for that bank look positive. It would have been difficult actually, for that to have taken place in 2020, given what had gone on before it and given the things we’ve discussed about regulatory intervention. I think it would be very difficult. The banks have raised capital in the private markets, and that would have been very negative.

[00:35:19.18] Ben: Do you think maybe things might change from here? This is where I wanted to bring in Wirecard because the banks are so heavily regulated now and so closely scrutinized that a lot of the scandals and fraud and impropriety is happening outside of the banking sector in tech companies or shadow banking or areas of shadow banking. Do you think at some point that the regulator is now going to change the direction of, or at least move its focus to all of those companies that are doing banking, but aren’t banks?

Marc: Whether it’s going to happen or not, I don’t know. And actually shadow banking, I mean, I said earlier, I’m going to contradict myself now talking about fighting the last battle. But some of the ingredients of that last battle were in the non-banking sector, were in the shadow bank. Subprime companies weren’t regulated and in the US, different regulatory requirements for thrifts, such as Washington Mutual, who played a game of regulatory arbitrage, choosing to be regulated by one regulator rather than a broad financial services regulator. The investment banks weren’t regulated as banks. Lehman Brothers was regulated separately from… And as a result of the crisis, Goldman and Morgan Stanley became bank holding companies and became regulated as a bank. So shadow banks, this kind of regulatory arbitrage was going on anyway. But you’re right, is going on now. And these payments companies, to all intents and purposes, what a payments company does is not dissimilar to what a bank does. And we saw that with Wildcard, actually. And hence, you know, you mentioned Ana Botín’s FT piece. Some of the consternation of bankers right now is that tech companies are getting away with stuff that they just wouldn’t be able to get away with.

Nobody wants a mortgage, they want a home.

[00:37:23.05] Ben: in every sense, right? In the sense of the same scrutiny, but also, you know, they don’t even have the same level of capital, for example, to do the same business. It’s not just more scrutiny, it’s not just the supervisory level blame for this; it’s actually an operating level blame for this as well.

Marc: Yeah, that’s right. That’s right. That’s right. And the issue here is not about financial stability, per se. It’s about the specific issue that Santander has, and Unicredit has mentioned it, and Jamie Dimon at JP Morgan has hinted at it as well, which is about data. And one could have made an argument 10 years ago that banks have got more data, more valuable data. I guess Amazon has got shopping data, Google has got search data, Facebook has got social data, and some overlap between them. Banks have got financial data, and what data is more valuable than financial data? And yet, they’ve been restricted, rightly, from their ability to monetize that. And I think the issue now is we’re seeing this convergence of data and this degree of consternation about the degree to which the playing field is not leveled.

[00:38:43.00] Ben: And the PSDs bit as well. It’s not just that they have to share data if the customer says that’s okay, is that they’re sharing data with companies that already have, in some ways, an advantage because they’re already more embedded in our lives, right? So, you’ve made the point many times in your newsletters, if you control distribution in the digital age, you know, you’re in a much better position to create network effects and to reduce the cost of customer acquisition and so on, than if you’re a balance sheet provider. And so it’s almost like, it’s a double whammy of sort of thinking you need to introduce more competition and forcing banks to share a really valuable asset with those people that are already better positioned to capitalize on data and distribution anyway.

that combination of Goldman Sachs’ back office, banking as a service infrastructure, with Apple’s consumer-facing distribution and brand value, could be a bigger competitor to JP Morgan than Chime or any kind of startup, FinTech, challenger bank.

Marc: Yeah, that’s right. And banks, certainly some of the starter bank, some of the challenger banks are trying to exploit that idea about distribution. But they don’t have the distribution right now, and that’s obviously an issue for them.

[00:39:41.03] Ben: I’m really pleased you mentioned challenger banks because one of the things I wanted to ask you is, you know how people are talking about this COVID economy is k shaped, right? And the idea that everything digital is booming and everything analog is suffering or faring really badly. And to some extent, you’ve seen that in the world of financial services and FinTech. You know, you talked about Square — which we’ll come back to in a second — as a company that’s really shot up and really found more customers and been able to benefit from the crisis. But challenger banks notably haven’t. What do you put that down to?

Marc: Well, some of them have, actually. So you’re right. I did write. Some of them have. Chime in the US has done very well through this period. But others haven’t. I think the biggest challenge, singularly, that these challenger banks face is their ability to acquire customers cheaply — and the right customers. There’s some question mark as to the quality of those customers, let’s say. And actually, to be fair to the company, the company has provided disclosure in the past as to what the unit economics are, on a customer that pays its salary account into its Monzo account, as distinct from a regular customer that maybe saw their friend has got Monzo, downloaded the app, and maybe actually isn’t even an active user. I guess a problem — maybe is why it’s different from other digital industries — is that there’s a life cycle perspective, whereby the customer becomes more profitable when he’s a little bit older. And yet, digital adoption tends to take place when they’re younger. So the challenge for the challenger banks is twofold. One is, as I’ve mentioned, it’s the ability to acquire customers cheaply. But the second, linked to the ability to capture revenue from them, is can they turn a millennial into — can they extract profitability, which is equivalent to what a typical bank customer profitability might be? Or do they have to wait until that customer gets a bit older, and kind of hits that profitability level, which would be typical in a lifecycle process.

[00:42:11.04] Ben: Let’s talk about customer acquisition cost, because I agree with you, the unit economics are really hard to manage, if you’ve paid loads and loads of money to acquire the customer. It costs a lot to acquire the customer. And then, the lifetime value is somewhat held up — in my view, at least — which is, you know, the ability to sort of upsell and cross-sell customers is hard in banking because we don’t actually spend very much time on the banking apps. And so, we still have this thesis that it’s gonna become much easier to embed banking and other channels than it is to build a really, really profitable banking business going forward. Because, you know, if you consider social channels, for example, or e-commerce channels, we spend a lot of time on those channels. And if you can introduce banking at the point of sale, or if you can introduce banking in a social way, then, you know, first of all, we have a low or even negative cost of customer acquisition, but then you also have the ability to generate very high lifetime value, because you have the customer spending a lot of time on the app, and therefore, you have a lot of surface area in which you drop-sell and cross-sell. Where do you stand on that whole embedded banking discussion?

Marc: Yeah, I think that’s right. I think that is right. I think one of the reasons why payment has been the most successfully penetrated area within financial services by startups and digital propositions is exactly this point that the frequency of payments is infinitely higher than the frequency of mortgage application. So that is right. And I’ve thought about this in the context of insurance, as well as banking, but in both cases, nobody wants a mortgage — there’s no tangible benefit, there’s no tangible value in the mortgage itself. Nobody wants a mortgage, they want a home. And secondary to that is the financing of it. And equally, nobody wants a checking account. Ultimately it is a payments mechanism and they want some facility to serve multiple jobs. One is to preserve their payments. One is as a store of liquidity. One is maybe as a conduit into savings — longer-term savings. But the tangible value of the thing itself is low. And, as you say, therefore, the appeal of embedded finance is very, very high. Now there are issues around regulation, and from a business perspective, the ability to scale, but from a consumer perspective, it makes perfect sense.

[00:44:51.15] Ben: And do you think this is, therefore, the biggest threat to banks over the long term which is, you know, it becomes easier to embed finance in channels that have engagement, than trying to create engagement in banking channels, and therefore, as you’ve talked about this sort of split between what we might call distribution financial services and the, I guess we could call it the manufacturing financial services becomes even more pronounced, and therefore, you know, profits go one way and the other becomes more and more of a utility over time.

Marc: It depends. So, one of the features of banking is that each market is distinct. There’s a path dependence because we’re going back hundreds of hundreds of years, banking has evolved very, very differently across different markets. You know, a mortgage in Switzerland is very, very different from a mortgage in the UK, for example. So Russia is an interesting case study. Sberbank, the biggest bank in Russia, has brand value that banks across countries in Europe and in the US would envy. They have phenomenal brand value. Sberbank itself has launched a marketplace where… Everything we were discussing earlier, it knows it’s got the data and it’s got the brand value. So it’s got the data and the brand value. So, it’s offering a marketplace to its customers via its app. So that’s one approach. Everything we’re nervous about big tech companies in the US and countries in Western Europe, everything we’re nervous about them achieving, Sberbank itself might be achieving that and is in competition to the tech companies in Russia because it’s forging its own path there. So that’s one market. It’s a bit different. But you’d be right elsewhere. You know, I often think about that. I’ve written this in one of the newsletters that Goldman Sachs plus Apple is probably the biggest competitor — that combination of Goldman Sachs’ back office, banking as a service infrastructure, with Apple’s consumer-facing distribution and brand value, that combination of both of those could be a bigger competitor to JPMorgan than Chime or any kind of startup, FinTech, challenger bank.

[00:47:18.01] Ben: Listening to you, it seems there’s a tendency to conflate retail banking with banking in general, because, you know, trust is so important. And as you say, once we move into wealth management, then you just don’t see the same level of tech or FinTech disruption. Once you move into wholesale banking, you know, you don’t see the same level of tech and FinTech disruption. So I wonder, you know, are we guilty sometimes for talking about retail banking, as if it’s whole banking? And then the second point would be because you’re such a student of financial services, I wonder, do we also fall into the trap of thinking that these things which look so disruptive, have actually played out many times before in different guises? Because I was reading your newsletter about Visa before and it’s almost in a way that, was Visa not embedded banking in a way? So I wonder, are we also guilty of thinking these are bigger trends than they really are and they happen quite regularly over the course of history, in cycles?

Marc: Yeah, it’s such a good point. I think 100% I agree with that. And there’s nothing new under the sun. A lot of what we’re seeing now we’ve seen before in various guises. So you’re right, I did a deep dive on Visa, recently. It’s a fascinating story. The founder of Visa, Dee Hock was so far ahead of his time in thinking about payments and the way in which payments simply reflect — just to give some context, we’re talking about the 1960s, where, you know, computers were the size of buildings, and he was thinking about payments. And most of the payments at the time were done on paper that was shuttled between banks. And he foresaw this system whereby payments were — he didn’t use the phrase ones and zeros, but he talked about alphanumeric data — simply alphanumeric data. He has written about all of this. So, Dee Hock, the founder of Visa, is 92 years old today. He founded Visa in the late ’60s, let’s call it 1970. He was CEO until 1984. And he wrote a book in ’99 that was re-issued in 2005. And he questions the need for banks. He says, “If it’s just alphanumeric data, why do we need banks, and the payments?” And he, at the time, knew nothing about crypto, knew nothing about digital currencies. But presently, he talks about a global currency, he talks about payments just taking place directly between consumer and merchant, much of the functionality that Bitcoin potentially offers — or crypto more broadly potentially offers today. And he was talking about this in the ’60s and ‘70s.

Marc: Just to come back to your question, similarly, equally, he allowed JCPenney, which went bankrupt last year, it kind of came out, it went through a bankruptcy process in 2020, has come through with that now. But back in 1979, it was one of the three biggest retail merchants in the United States. It was so big, he said, “Well, let’s introduce embedded finance, let’s bring it straight into the Visa ecosystem”. But even before that, interestingly, it was companies like JCPenney, that actually invented the credit card in the way now that… So now we think about kind of Shopify, and everything that Shopify is doing with Stripe to embed finance at the point of sale in merchants. This was a big merchant’s… I guess, what’s changed is that you don’t have to be big anymore, that because of these providers, because the cost of everything has gone down — the cost of storage, the cost of underwriting, the cost of everything has gone down — it’s become more accessible for smaller companies to offer these things that the big companies have been offering since the 1950s and 1960s. So yes, there’s nothing new under the sun. The same with challenger banks. AG was a challenger bank that merged in the UK with a not dissimilar model to the model of many challenger banks today, 20 plus years ago, 25 years ago. A lot of these models have appeared before and one of the things that I try and do in that interest is look back through history — as you said — as a student of financial services, to learn from them and apply them to the situations we find ourselves in today.

[00:51:54.04] Ben: Having said that, there is nothing new under the sun, I just want to get you on digital currencies, because actually, it does seem like something which is more transformational. If you don’t mind, can you briefly just describe what digital currency is because, you know, one of the things that, you know, when we talk about digital currencies, people get, I suppose, a bit confused about is, you know, if I were to pay you some money now, and I would just transfer it to you, that’s in a way digital money. So what’s the difference between just an electronic transfer of Sterling versus digital Sterling?

Marc: There’s three types of digital money broadly. One is crypto. So, basically, it’s got its own infrastructure and its own coin. So, like Bitcoin. Two is we can talk about stable coins, which have their own infrastructure. So Facebook looks like it will launch any week now, actually, its own stable coin. It’s got its own infrastructure, but it’s stable in the sense that it’s not its own coin, it’s a US dollar or some other currency. And then the third type is a Central Bank Digital Currency, which is, the central bank maintains the infrastructure. It is also an existing currency — call it the US dollar. So these are the three types. And the difference is… So, if we’re talking about your question referred to Central Bank Digital Currency, the difference is, you know, if I give you a 20£ note, it will have a serial number on it. So, when I’m talking about a digital currency when I’m paying you online, it won’t have that serial number on it. So basically, I’m digitizing that 20£ note. I’m digitizing that 20£ note such that if I was to pay you 20£, it would have a serial number attached to it, such that the regulators, the central banks could then audit the trail of that currency the way they do with cash right now through a digital system.

[00:53:56.06] Ben: But isn’t that the most important point for Central Bank Digital Currencies, which is about that ledger? And therefore, it really goes into the question the extent to which you need banks to intermediate. Because if you can have your wallet directly with the central bank, if the central bank can disperse money to you directly, does it to some extent take away that role of banks as creating money supply? Because I suppose, to the earlier question about, you know, if we are going to see an increased split between the distribution of manufacturing financial services, and the central banks kind of rising up to take a bigger share of the manufacturing — or I don’t want to call it manufacturing, but if the balance sheet aspect of financial service because more will just sit directly on their ledger. Does that again squeeze the traditional banking sector?

Dee Hock, the founder of Visa wrote a book in ’99 where he questions the need for banks. He says, “If it’s just alphanumeric data, why do we need banks, and the payments?” And he, at the time, knew nothing about crypto, knew nothing about digital currencies. But presently, he talks about a global currency, he talks about payments just taking place directly between consumer and merchant, much of the functionality that Bitcoin potentially offers — or crypto more broadly potentially offers today. And he was talking about this in the ’60s and ‘70s.

Marc: Yeah, absolutely. And one of the reasons why the central banks are being so cautious in rolling out Central Bank Digital Currencies — everybody’s looking at China — China is trialing Central Bank Digital Currencies right now. They’ve suggested that those trials will continue up until Beijing Winter Olympics in 2022. So, we’re not going to see anything launched until at least then. And that’s in China. And similarly, Europe and various other central banks have said that they’re still studying it. And one of the things they’re studying is exactly that, is that what would differentiate between retail central bank digital currency, and wholesale. And one extreme would be retail, which is the picture you paint, which is that you and I have an account with a central bank, the same way that UBS has an account with the central bank, or Barclays has an account with a central bank. We have an account with a central bank and are therefore able to conduct ourselves without the need for banks.

[00:55:48.18] Ben: Because I can just send you money through my wallet to your wallet, right?

Marc: Exactly. And it’s insured. The way bank deposits are currently insured. All they do at wholesale, and actually, they maintain the role of banks. And again, it goes back to this idea of path dependence. It is quite interesting. Dee Hock, when he thinks about Visa, he’s got this framework for looking at the world. He says, you know, “To understand anything, you have to think about the way it was, you have to think about the way it is, you have to think about the way it might be. And you have to think about the way it ought to be.” And when he was thinking about Visa back in the early ’70s, and say today, actually, he’s made this very clear in his book, that Visa had been created through his kind of organizational principles. It’s not a panacea, and he lists in his book, and I quote him in my recent piece, some of the issues, some of the drawbacks some of the flaws in the Visa model. And to come back to what we were talking about, the point applies here as well, is that there’s a path dependency that, you know, maybe on a blank sheet of paper, we can devise this phenomenal new financial system. And they did that in China. You know, China didn’t have credit cards, they went straight from cash. So they didn’t need credit cards. They went straight from cash to a digital wallet, and you cut out the middleman. That’s very, very difficult when you’ve got vested interests that are cultural, political, data, that when people are used to a certain way of doing things as they are in Europe, in the US, you might be right, from a blank sheet of paper, if we could devise a financial system, we do it like this. But that’s not, to use Dee Hock’s framing, that may be the way it ought to be but we can’t neglect the way it has been and the way it is. And therefore, it probably won’t pan out like that.

[00:57:49.16] Ben: I was going to ask you this question at the end, but I feel I need to sort of preempt it now. Which is, you talked about Libra. And I just wonder, you know, if you look ahead at 2021, what’s the most potentially game-changing thing that’s going to happen in financial services that people aren’t talking enough about? It feels like that might be Libra, because, in a way, they’re going to roughshod over all those vested interests and introduce something that’s going to potentially have the adoption of every Facebook user, which is I don’t know how many billion people and it’s kind of outside a single country jurisdiction and it just seems massive. I’m wondering, you know, are you going to write a newsletter on Libra? Because it just seems such a big phenomenon?

Marc: Yes, I agree. I think it will be a big story for 2021. Riding roughshod. Interestingly, they already watered it down. So initially, they put together a consortium, which included financial service companies, there was a backlash from regulators. And so, they watered it down and the result today is something a little bit different. But I agree with you 100%. I think it’s gonna be a big story of 2021.

[00:58:54.13] Ben: But it’s still a currency that might be used to intermediate peer to peer and other transactions. You know, and even all the vendors that sell through Facebook, right? Within the Facebook network, you might have a currency that sits independently of any fair company, or is that not?

Marc: So again, I mean, anything I would say, the regulators do still have the capability to insert themselves. And we saw that too in Brazil, WhatsApp, which is part of Facebook launched a payments mechanism. And they spent a lot of time preparing it, launched it, presumably at launch they’d had the approval of the central bank because they’d spent a lot of time preparing it, but nevertheless, the central bank once it saw it, changed their minds and shut it down. So, regulators still do have this power, which is, I guess, classic disruption. Bitcoin has been operating at the margin and interestingly it never really became a payment coin. So, Coinbase, which is going to IPO this year, started out as a payments system for Bitcoin. And there’s a book that was released in December, called Kings of Crypto, about the story of Coinbase. And in it, they talk about hiring somebody in order to acquire merchants that will accept Bitcoin. And they did a great job, he got all these merchants, he got multiple billion-dollar revenue companies, lots of merchants, all lined up to accept Bitcoin. But consumers didn’t want to spend their Bitcoin. And so, they pivoted to a broker and Bitcoin became less of a payment mechanism, and more of an asset class, more of a commodity. But clearly, that can change. But as I said, it’s tangential, classic disruption. So they operate margin, and it can become mainstream.

[01:00:58.23] Ben: Yeah, if I understand what you’re saying, Libra, first of all, you know, whatever way in which it’s envisaged that it will be used might change because the use case is different from the one that was a bit like Bitcoin. I want to move on to a different topic now, which is private versus public investing. Because, you know, to get to the latter part of your career, I think one of the things that you’re doing now is you’re doing some angel and private investing. I just wonder if you have any interesting observations about the difference between investing in public markets versus investing in private companies? And I suppose we’ll come back to it as well. But you know, I think it’s relevant because companies seem to be staying private for so much longer than in the past. And it’s almost like being an expert in private investing is a more important skill set than it was historically, we could potentially argue. So I wonder if you’ve got observations around that.

Marc: Yeah. So, interestingly, three months ago, I might have agreed with your point about companies staying private for longer. I think what we’ve seen recently through the rise and the emergence of SPACs…

Ben: You’ve preempted that because I was gonna ask if the SPAC is the vehicle to get companies from private into public markets faster?

Marc: Yeah. I think yes, they are.

Ben: Let’s break this down into three sections, if you don’t mind. So, first of all, maybe everything we’ve got on the data shows it’s changed yet, but why weren’t companies staying private for longer? Because it must have been because it was difficult to realize the value in public markets. And how do SPACs do that? Why would a SPAC or a company taken to market through a SPAC, have a higher valuation than a company that would have gone through an IPO process?

Marc: Yeah. In the short term, maybe that’s an inefficiency in the market. Long term, it’s not clear that the mechanism through which one comes to market has a bearing on one’s long-term valuation. But having said that, there are some structural differences in the process. The key one here being that when, through the IPO process, management is not allowed through SEC guidelines to provide any projections on the future. And coming back to what we were talking about earlier, in terms of equity research, one of the roles, one of the jobs that equity research analysts used to fulfill was to provide equity research at the time of the IPO. Now, it wasn’t always independent, which is one of the issues why it was shut down. But there was a service provided nevertheless. Now, that’s not allowed. So now, what will happen is the company will provide its own filing and the institutional investor will have to peruse that filing, do their own due diligence, do their own work in order to take a view, but they’re given no steer as to what the projections are.

[01:04:08.23] Ben: Do you mind, just because I’m not sure everybody knows what a SPAC is. I mean, I love the phrase that you put in your newsletter, you said, “The SPAC is a bit like the wardrobe, is the portal to Narnia, complete with unicorns on the other side.” So what did you mean by that? If you don’t mind just spending a minute on what it is because it’s such a new phenomenon. Maybe many people don’t know what it is.

Marc: Sure, that’s fine. So a SPAC is a Special Purpose Acquisition Company. And what it is, it’s a pool of money that is raised by a sponsor. Typically, a well-known sponsor will raise several hundred million dollars in cash. And the purpose of the cash and the role of the company that the cash sits in is to do an acquisition with a private company, to find a private company — hence the analogy of Narnia. So public investors clearly are restricted to investing only in public companies. But if they were to buy a share in a SPAC, it’s just a pool of cash. If they were to kind of hand some cash to the sponsor, the sponsor will then go through the wardrobe, into the land of the private companies and find a private company to merge with, bring it back out. And then, all of a sudden, you now, through the merger process, have got a share in a private company.

[01:05:31.21] Ben: That is a great analogy, by the way. That’s superb to describe what a SPAC is.

Marc: And just to finish off what I was talking about earlier, the difference is — and it’s slightly arcane, it’s kind of regulatory — but the merger process enables the company to provide projections. So the guy on our side of the wardrobe, when the sponsor comes back out with his private company, can say, “Well actually, in 2022, ’23, ’24, these are our projections. What do you think?” And at that stage, he can either kind of roll with it, or he can sell because maybe it wasn’t what he wanted as a public market investor, so he can sell but he’s kind of got that right.

[01:06:14.17] Ben: So you think this sort of recent last 20-year phenomenon, with more companies staying private, is maybe addressing this fact? Because it does two things, essentially, if I understand rightly. Firstly, it reduces a lot of the friction and the cost of going public because I can’t remember how much an IPO costs, but it’s a lot, right? You pay your fees, I think it’s like four or 5% that you pay to the investment bank?

Marc: It could be even higher, actually. Yeah.

[01:06:35.04] Ben: So yeah. So there isn’t that big cost, there isn’t the sort of, you know, I don’t know how many months it takes to IPO. But it reduces the friction, the cost, the time to go public plus also through being able to share projections with the market. Arguably, and I think this is the bit you’re talking about, there isn’t the data, but arguably, it enables you to achieve a higher valuation. Because I guess there are two reasons why people stay public for longer, right? One, they didn’t feel they could achieve the valuation that they deemed appropriate in the public market, or they were just put off by the time and the cost and the friction.

Marc: Yeah, that’s right. And also the third reason is the private market was rich with capital. So, why would they…

Ben: But that bit hasn’t changed, has it?

Marc: That hasn’t changed. But you’re seeing even in the public… You know, interestingly, recently… So Lemonade is a FinTech, it’s an insurance company that was founded on a kind of a digital platform. And it was SoftBank. So the SoftBank vision fund is one of the biggest venture capital backers out there, it was an investor in Lemonade. It went public in July of 2020. Actually, recently, already in 2021, it’s raised fresh capital in the public markets, at a valuation much, much higher than when it went public in the summer. Typically, normally — and that’s an unusual occurrence — in the public markets, normally, that would take place in the private markets to be a funding round, even six months after the last one. It’s more unusual in the public market. There was kind of a convergence between… I mean, maybe it’s cyclical just because of where valuations are. But it feels as it was kind of convergence between some of the behaviors that were typically the case in private markets and in public markets.

[01:08:23.10] Ben: I suppose you could argue companies like Tesla wouldn’t achieve a richer evaluation on the private market than they could in the public market. But do you think also, there’s some of the stuff that couldn’t IPO because it didn’t come under the same level of scrutiny would do so through a SPAC?

Marc: Yeah, I think that’s right. I mean, some pushback about SPAC is people have talked about as being a SPAC bubble. And, you know, inevitably, there’ll be a lot of poor companies that are coming through that wardrobe, sneaking through that when the, you know, as Buffett says, when the tide goes out. Yeah, we’ll say who’s swimming naked.

[01:08:59.29] Ben: I think probably we’ve run out of time to talk about Robinhood, and that whole phenomenon of the gamification of the stock market investing. But I just wonder if you had any other observations just from your practice as a public and a private investor. You know, the kinds of things you look for in companies that you didn’t historically, or whether it’s very similar.

Marc: It’s really very different. It’s very, very different investing in private companies, from investing in public companies. For one, probably four key differences. One is the level of transparency, which is much higher on the private side than on the public side. Two is — and this is an interesting point — two is volatility. So, a lot of people inherently don’t like volatility. And I think one of the attractions of private market investing is that they only get revalued when there’s a funding round. And so, kind of right now it’s not an issue, because, in the markets, we’re only seeing upward volatility with everything getting up. But I think there was a kind of a degree of, think back to March, April of 2020, when there was a lot of not such good volatility in the markets. I think there was a degree of comfort around private holdings, which, you know, whether it is Robinhood app, or whatever broker one is using, one’s not seeing kind of the daily volatility of valuations in private holdings than they are in public. That’s a big behavioral difference. The third difference is the structure. It’s very, very important as a private investor to be comfortable with the structure of the holding. You know, when you buy a share in Apple, it’s a share in Apple. It’s pari-passu with all the other shares in Apple. That’s not necessarily the case with private companies. Well, they are different classes of shares so it’s something that as an ex-public investor gone private, I suddenly have to learn about. And the final point is just that you’re in the room. I mean, I can write a newsletter about Jamie Dimon at JP Morgan, he may or may not read it, he may or may not do anything about it…

Ben: I think he subscribes to that, doesn’t he?

Marc: Probably he won’t do either. But it’s just a great experience being involved in a private company.

[01:11:31.05] Ben: Yeah, I think is that last point, which is, you know, you have the ability to make your own weather in a way, right? Because I always thought, for me, that’s the key advantage of angel investing, which is, you don’t just sort of invest the money and hope for the best. You can actually get involved and materially affect the return on that investment that you make.

Marc: Yeah, exactly. Exactly. Exactly.

[01:11:51.10] Ben: One question I wanted to ask you, which is the big downside, obviously, of private investing, is liquidity. And it just amazes me that we haven’t seen more people enter the space for the secondary market for private investing. Why do you think that is?

Marc: There are some crowdfunding platforms in the UK — it was one crowdfunding platform in particular in the UK — was Seedrs, which offers secondary trading of its companies that is crowd equity, crowdfunded for. But probably is difficult, actually, because of the fact that, coming back to the point about structure, different classes of shares. You know, I did another newsletter on fixed income markets — electronic trading and fixed income markets — which is much less developed than electronic trading in equity markets. The reason being is there are multiple fixed income instruments out there. Whereas there’s only one equity for most companies, there’s only one equity. And it’s the same here with private, there’s two different classes of shares with too many different terms. But there’s no standardization.

[01:12:53.08] Ben: So, I have two quick follow-on questions for you. One is, what’s getting you really excited beyond Libra looking into 2021?

Marc: I think what’s happening in embedded finance is fascinating. I think what’s happening broadly, just the acceleration we saw in 2020 around digital, I think what’s happening broadly, through payments mechanisms, and beyond payments, not payment as the hub. It used to be that the checking account was the anchor product for most banks, or potentially, the mortgage actually, increasingly is becoming payments. And that I think has all sorts of implications, whether it’s around crypto or Libra, or embedded finance. It’s basically the common theme across all of those things.

[01:13:40.06] Ben: And then the last question I wanted to ask you, which I think is gonna be difficult, you may have to come back to us, which is, what’s the best book that’s ever been written about the financial services sector?

Marc: Liar’s Poker.

Ben: Yeah, that would have been my pick. Yeah. Okay, good. So if anybody hasn’t read Liar’s Poker, you really, really should. Great. Marc, thank you so much for coming on the podcast. It was a great discussion, and I really appreciate you taking the time and keep up the good work with Net Interest which is awesome. And if you didn’t subscribe to Net Interest, you really should. One fantastic deep dive into an aspect of financial services every Friday. So subscribe. Marc, if people want to subscribe, where do they find it?

Marc: Yeah. So netinterest.email is the page.

Ben: Thanks so much again.

Marc: Thanks, Ben. Great to be on. Thank you.

Sequencing the World’s Regulatory Information (#35)

Structural Shifts with Manos SCHIZAS, Lead in Regulation and RegTech at Cambridge Center for Alternative Finance

Our guest is Manos Schizas — Lead in Regulation and RegTech at Cambridge Center for Alternative Finance at the University of Cambridge. We discuss how regulatory change is accelerating so fast that people alone can’t deal with it and how does the technological solution addressing the problem looks like. Can technology solve this problem at scale? How much innovation are we seeing thanks to machine learning? And we also discuss about the Regulatory Genome Project, a recently launched long-term project that aims to sequence the world’s (financial) regulation, allowing developers and firms to build own applications on top of the platform. Before joining the Cambridge Center for Alternative Finance, Manos also served as a regulator with the UK’s FCA.

 

It costs something in the order of 4% of turnover for a major financial institution to comply with regulation.

Ben: Manos, thank you very much for coming on the Structural Shifts podcast.

Manos: Thanks for having me on the show, Ben.

[00:01:22.05] Ben: Maybe let’s start by you talking about your background because I think it’s useful for our listeners to know that you’ve seen this interplay of finance, tech, and regulation from many different angles. So, if you don’t mind, Manos, just tell us kind of, you know, how you started off in this world?

Manos: Sure. So, I first got involved with writing and reading about regulation back in 2008. At the time I was a very, very junior lobbyist at an association for accountants — the ACCA. And because I had their access to finance brief, inevitably, around that time, I had to feed into the discussion around Basel III, and the implications for financing of small businesses. But before long, I was talking and writing primarily about FinTech and regulation. At some point, I made the jump over to, I guess what I thought at the time was about the dark side. So, I joined the FCA — the UK regulator — I spent some time there leading their work, at the working level, on things like crowdfunding or their approach to small businesses, surprisingly, political and fraught topics. And then, I moved on to a London-based RegTech startup, where I was their Head of Regulatory Content Operations and also had the product brief for a short period of time. And then, of course, the rest is history. I joined the Cambridge Center for Alternative Finance, where I lead their thought leadership practices, as well as their applied research program on RegTech and machine-readable regulation.

The pace of change and the volume of data has really long outstripped the ability of firms to just throw humans at the problem — human brains and human bodies.

[00:02:53.10] Ben: We’re going to come back to the Regulatory Genome — the project that you’re working on — but before we get there, I think we should zoom out and talk a bit about the whole terrain of regulatory compliance and why it faces so many challenges? So maybe let’s start from the point of view of a regulated financial institution. Why is it so time-consuming and expensive for banks and other financial institutions to comply with regulations?

Manos: Well, alright, let’s start from the top line if you will. It costs something in the order of 4% of turnover for a major financial institution to comply with regulation. Again, that’s turnover. That’s not, you know, breaking margins, that’s not profit. It’s colossal amounts of money on a global scale. And why does it cost so much? Well, I guess, there hasn’t been a time in very recent memory when financial services weren’t heavily regulated. But since the financial crisis, in particular, there’s been an explosion in regulation, that has seen the amount of regulatory notifications rise, I think about seven or eightfold between 2008 and 2018. So, I guess the key point is, the cost is driven primarily by how demanding the regulatory framework is and the pace of change. Now, it’s not the same for every part of the regulated sector. So, a tier-one bank will probably recognize the pace of change as I describe it, whereas let’s say, you know, a smaller asset manager might not, but by and large, there’s been an explosion in regulatory requirements. At the same time, there’s also been an explosion in the sheer amount of data that firms hold, not just the ones that they have to hold for regulatory purposes, but the ones they hold for commercial purposes. You know, only recently — I think it was HSBC — one of the major banks was creating a data lake that was in size exactly the same size as the entire internet had been four years earlier. It gives you a sense of perspective of what we’re talking about. The pace of change and the volume of data has really long outstripped the ability of firms to just throw humans at the problem — human brains and human bodies.

Manos: There’s also other elements related to the way you manage institutions like that. So, you know, many of these major firms are matrix organizations where it’s actually, in the time of change, quite easy to lose visibility as a senior manager of why you’re complying the way you’re complying, what exactly the outcomes you’re achieving are, and so on and so forth. And at the same time, regulators are hardening their stance on the personal responsibility of senior managers. You know, you’ve got senior managers regimes in the UK, in Singapore, in Australia, in Hong Kong, and in an increasing number of jurisdictions. So you’re in this kind of the opposite of a sweet spot, if you will, or the sweet spot for vendors, where the key decision-makers are facing increasing scrutiny on a personal level, and at the same time, are losing visibility. So if you’re a vendor, this is a good time to come in and try to sell them technology.

[00:06:12.08] Ben: What about if we look at it from the point of view of regulators because it sounds a bit like, you know, listening to you, the regulators are really driving the agenda here — which I guess is true to an extent — but the regulator doesn’t control the pace of technology change, which is driving innovation; and the regulator also only can really affect its jurisdiction. And I think one of the things that’s become more apparent over recent years is there’s a lot of competition between jurisdictions to attract new financial institutions and also new FinTech companies. And so, does the regulator also see the need to do things differently in this space?

Manos: Sure, I guess there’s two types of regulations depending on where they come from. So, there are rules that are fundamentally quite harmonized across the globe. AML, for example, prudential requirements — at least in banking and insurance. And for those, the rules come down from Mount Olympus, from the G20. They cascade through the standard-setting bodies and then finally into national regulators. Now, if you are a regulator working in that kind of subject matter area, then your key concern is, am I fundamentally compliant with international standards? And have I found the most efficient way to comply with them? AML is the usual example here because if you’re not compliant, that’s a big problem. The whole country can get graylisted or blacklisted, and you just don’t want to be there as a regulator. But you know, even when the stakes aren’t that high, regulators want to know that they are compliant with international standards. Then there are other areas of regulation which are closer to the matter of technological change that you mentioned earlier, where good practices are bubbling up from the bottom up. So areas like, I don’t know, cybersecurity, data protection — you know, there is no single unifying force or no single cascade of standards from the top. But everyone wants to know how they compare to the jurisdictions that they see as competitors. So, if you’re in Malaysia, you’re the Securities Commission, you will look at what MAS is doing in Singapore. If you are in the UK, you’ll be looking at what the Europeans are doing post-Brexit. Pre-Brexit, obviously, you just have to comply. So this process of regulatory benchmarking is actually one of the factors driving regulatory change internationally. When at the CCF, we surveyed regulators from 111 jurisdictions around the world. They told us that nearly every exercise of review of regulation in relation to FinTech had involved some benchmarking exercise. And, in more than half of these circumstances, it was the benchmarking exercise that had prompted regulators to change how they do things.

if anything, regulators are under more pressure. So when we say something like, you know, the pace of regulatory change has increased sevenfold since the financial crisis — well, you know, firms’ compliance budgets have not increased sevenfold. But regulators’ budgets have not increased at all, not in real terms anyway.

[00:09:11.06] Ben: What about COVID? Has that had much of an impact on the pace of regulatory change?

Manos: Well, that’s what our research tells us. So, we have just come out of a significant project to basically carry out a rapid impact assessment of COVID on the FinTech and RegTech industries, as well as the regulators responsible for them. And obviously, what you hear from regulators is that COVID fundamentally changed the way they approach some areas of their work — not just their rulemaking, but also their hands-on supervision. But I guess what regulators tend to see here is some megatrends that have accelerated — so trends towards you know, more or less material financial services, more online banking, more app-based financial services and so on and so forth, but also greater demand on their resources, so that they can do more with fewer touchpoints with industry. And then, of course, COVID also came with some of its own, if you will, pathologies. So, regulators told us, for instance, that they were much more aware and worried about fraud in a COVID environment where a lot of things have had to be put on the cloud or have had to be done remotely at relatively short notice, or where firms have had to deal with stuff that previously were very closely held in-house on a remote basis. So, of course, the focus of regulators has had to change.

[00:10:48.17] Ben: So, Manos, if we were to try to summarize what you’ve told me, you’re saying that the pace of regulatory change is accelerating to the point where financial institutions can no longer just throw, you know, human resources at this problem because it’s an exponentially changing situation so it requires a technology solution to it. But would you also argue that the regulators need to be putting more technology at play here? Because presumably, they also want to know how regulations are changing and being implemented, and they want to make use of the data to make sure that they’ll keep up with the potential rates of innovation, put that to good use in terms of financial inclusion and everything else. So would you say that the need for new technology applies to both the regulated and the regulators?

Manos: Yeah. I mean, if anything, regulators are under more pressure. So when we say something like, you know, the pace of regulatory change has increased sevenfold since the financial crisis — well, you know, firms’ compliance budgets have not increased sevenfold. But regulators’ budgets have not increased at all, not in real terms anyway. And so, regulators find themselves in these very interesting challenges wherever there’s this use of data involved. Like, to give you a simple example, the first touchpoint with technology around regulation and compliance for most regulators is reporting. And if you talk to an emerging market regulator — not the poorest countries in the world, necessarily; just, you know, significant emerging markets — they will say, “You know, firms report data to us and by the time we’ve validated the data and made sure it’s not garbage, it’s three months old.” Now, let’s go back to that COVID discussion we just had. If you had three-month-old data on the robustness, the financial stability of firms, as a regulator, it would be useless. It’s a snapshot from a completely different world. So you can see how COVID can really create an issue for regulators there and waken some of them to the challenges. But even if you think of more normal times, you know, the FinTech revolution has created a very big fringe of very small, very marginal firms that fly sometimes under the radar of regulators, and sometimes just above. And so, for instance, when the FCA took over payments, for instance, the population of firms that they were supposed to supervise more than doubled overnight. Now, their resources did not increase at all. So, what exactly do you do when faced with a situation like that? You have to find some way of prioritizing your human resources. And the only way, really, to get to a point where you can do that is to invest in technology that allows you to prioritize better by getting insights more cheaply, more efficiently, where the risks are proportionately smaller.

in the AML space, every year there’s a new estimate of what percentage of the illegal flows of funds are actually intercepted by AML controls. And it’s usually always in the low single digits. So, you know, you have to keep wondering, like, is this really the best we can do?

[00:13:49.05] Ben: That’s happening, is that not? So, we are getting thousands of new entrants into this space, new technology companies, new RegTech companies are entering this space to solve these challenges that regulated companies have, and regulators have. I was reading before this podcast that I think collectively, over $10 billion of new venture capital has gone into this space in the last 10 years. So, are we solving this problem at scale?

Manos: Well, it’s interesting. I mean, obviously, throwing more firms at the problem doesn’t necessarily solve anything. It is a good indicator of how valuable the prize is, I guess, for whoever wins the race. Just to be clear, just the number of RegTechs really depends on how you define this sector. So, you know, you will hear estimates from 800 all the way to the 2000 number that you quoted, but the amount raised is almost always estimated the same way because most of the fundraising is concentrated in a handful of large firms. So, this is one of the first things I think we need to keep in mind in the context of this discussion. You will hear about RegTech growing very fast as a sector, and all of the success stories, but the typical firm in the RegTech sector — we did our own research on this — has raised somewhere in the order of $1.5 million. Now, it sounds like a lot of money if you give it to me to buy a car or a house even. But how much runway does it buy a technology company? Like, less than a year. And to put it into further context, how long does it take from the moment, let’s say someone at the bank shakes your hand and says — well, they can’t shake your hand anymore, but you know, looks you in the eye virtually, and says “I love your product, we will definitely buy it” and the moment when you first see any money from them? Usually about 18 months. So, you have to put these two numbers together, like, how much runway do they have versus how long it takes for them to actually convert prospects to paying customers. So, most of this sector isn’t particularly successful financially. And so, the sector is kind of ripe for consolidation. Quite a few of these people are competing in very, very crowded segments. Also, of course, in our own research, what we’ve seen is that there was a golden era of new market entry between let’s say, 2013 and 2017. And the pace of market entry has slowed since then, quite significantly. So, this sector is now growing more from the center than from the margins — so, big firms getting bigger, as opposed to new firms joining.

I’m skeptical about the pace at which we can move towards machine-readable and machine-executable regulation, where we treat regulation as code.

Manos: Now, to your question, though, the actual question was, you know, are they solving this problem? I think the first thing to bear in mind is that the sector has been around for like 20, 30 years, depending on how you define it. So, you know, you had regulatory intelligence applications 20 years ago, you had BPM and GIC applications 20 years ago; they’ve evolved since then, yes, but the fundamental kind of offerings were already being imagined at the time. What firms are now much better able to do, I would say, is, first of all, they can scale a lot faster and deal with smaller institutions because their services can be delivered through the cloud and by APIs. It’s much easier for them to work together, so, hooking up different applications via APIs is now much more realistic than it used to be. And so, what that means is that ideally — and we’ll have to come back to this point — you know, no one firm has to build everything, end to end your entire kind of compliance factory. So, that obviously helps. But there are areas where RegTech has yet to make a significant impact. If you try to map where most of the effort has gone — AML, reporting, risk particularly on the prudential side — between those three areas you’ve probably captured 80–90% of the activity that we’ve seen; probably a lot more if you count it by funds raised. And then there are other areas, notably on conduct, for instance, that are kind of less tangible and quantitative areas of compliance, where, you know, you don’t see the same level of success. And, of course, even where the RegTech sector is making inroads — good on them — you still have to ask yourself, how much success do we have to show for it? So, in the AML space, every year there’s a new estimate of what percentage of the illegal flows of funds are actually intercepted by AML controls. And it’s usually always in the low single digits. So, you know, you have to keep wondering, like, is this really the best we can do?

[00:19:02.21] Ben: And listening to you, it sounds a bit like, you know, even though lots of money has gone into this space, and accepting that, you know, most of it has flown to a few big firms, rather than the long tail of smaller suppliers, it sounds like there’s still a lot of duplication of activities in this space, and also potentially, like, there’s not complete coverage of the regulatory space, i.e. people keep shooting, I guess, for the areas with the largest addressable market. So, would you say that they’re two of the challenges that still persist, that the RegTech community is still duplicating a lot of its own efforts, as well as, you know, perhaps don’t have complete coverage yet of all the areas of regulatory compliance?

Manos: Absolutely. And I’m not sure that any one firm has a particularly good overview of its entire competitive environment, just because so many people are trying this and many of them are still under the radar unless they’ve done two or three funding rounds and you start seeing kind of headlines about them. But I think it’s also important to say that compliance, in general, involves a colossal duplication of effort. If you think about it, the regulations are the regulations. They are what they are. But there’s thousands of financial services firms, each developing their own mapping of rules, you know, against their own internal systems. And you think, “Well, how much of that is duplicating effort? And is there really a business reason to duplicate this for each firm to do it on its own?” Because compliance in itself does not confer a competitive advantage. Being able to manage risk better does. Being able to understand customers better does, of course, so there are some things that firms will always want to keep close to their chest. But compliance in itself does not. So the duplication is quite substantial and not very rational.

[00:20:54.08] Ben: In terms of technology change, you mentioned cloud, you mentioned APIs? What about AI? Because it seems to me that one big area of potential improvement here is to train models… You know, you can imagine this particularly in the case of financial crime, for example, where, you know, many actors contribute information about financial crime and one provider can train the best models and can give the best predictive analysis about where financial crime might arrive, or stop financial growth based on patterns seen in the past. So, are we seeing much innovation and headway being made thanks to AI in this space?

Manos: We are. And I guess we’d better because the amount of processing power we can leverage these days is colossal. So, you know, in the first AI spring, in the ’50s and ’60s — I’m not reminiscing, I wasn’t there — back then it would take about seven minutes for a computer to parse one sentence or one paragraph worth of text. And now we can do, like, billions of them in the same amount of time. You know, obviously, that helps. Having said this, applications of AI mostly end up with a trade-off. So, think of it a little bit like an industrial process, where, because at the end of the day, most of the applications of AI that you’ll see in compliance come down to statistical models. You’ve got error rates, you’ve got false positives, you’ve got false negatives. And the whole kind of quality assurance process is around saying, “Well, how many false positives and false negatives can we tolerate?” And particularly, like, “How many false negatives can we tolerate?” Because that’s where you get fined or put in jail. And so, usually, what happens is firms, certainly in compliance, are very, very reluctant to accept that there will be a consistent level of errors in a compliance process, particularly around things like AML. And so, you know, many will seek a level of certainty that is just not possible. Some of them will tolerate redundancies and duplication, just to make sure that they are covered. And particularly in the larger firms, often you will have a duplication internally. If you’re a tier-one bank, there is actually a decent chance that you’ve licensed software that duplicates things you’ve built in-house, that you have licensed software from two different people that overlap. So, the strategy around incorporating AI in this area is still not fully fleshed out.

to get from the messy regulatory language to something that humans can work with, you have to have some kind of mental map of what regulations are out there, a kind of taxonomy of regulatory obligations and concepts. That’s one side. And you have to have a corresponding mental map of what the firm looks like — what matters to the firm. So a firm doesn’t see itself as a collection of compliance obligations. It sees itself as a collection of products and functions and locations, and yes, even processes and controls and policies, and so on, and so forth. So, you have to have both of those maps, and then get them to talk to each other — so create linkages between the two sides of the equation.

[00:23:41.02] Ben: What about this whole area of machine-executable regulation? So, you know, certainly, I’ve been reading about a lot of companies that are working on, you know, basically turning regulation into code, which can then be executed by the machine. And this seems, you know, at least prima facie, like, this is the most elegant solution to this problem, right? Because if regulators can put out very precise regulations, and they can be turned into code, not only can that code then be executed immediately, but it will be executed exactly as the regulator intended to be executed. So that seems like the holy grail here, would you agree? And do you believe that this is realistic and that we’re making progress in this direction?

Manos: I mean, it is the holy grail. And it’s interesting because it’s one area where software developers and lawyers kind of lead in the middle. Both sides think like machines. They want very precise and consistently worded inputs and outputs. But in reality, most regulation doesn’t work that way. So, the hype around machine-readable, machine-executable regulation is what it is because some of the earliest use cases for RegTech and SubTech are around reporting. And reporting use cases involve heavily standardized data — I say heavily standardized, but if you see them upfront in their raw form, they’re not always that good but they involve much more standardized and much more quantitative data, more structured data as well than most other RegTech use cases. So, if you’re only really interested in reporting and adjacent use cases, actually machine-readable and machine-executable regulation will happen. You know, it’s already happening in some domains, and it will happen in most others. Enormous amounts of money, enormous amounts of attention, and standard setting effort has gone into those. But then there is a lot of regulation where this level of standardization, of quantification and of structure just doesn’t exist, partly because that’s not how it’s been designed and it’s very expensive to redesign it from scratch, but partly because regulators want it that way, or legislators want it that way.

Manos: So, to give you an example that’s close to my experience: let’s say consumer credit regulations in the UK do not include any indication of what criteria somebody should meet in order to get a loan. Not because they couldn’t come up with, you know, a good sense of what credit worthiness looks like, but because legislators and regulators want firms to have the flexibility to come up with their own answer to the question. In other cases, the point isn’t flexibility, but responsibility. So, very often, what the regulator wants is for the onus to be firmly on the firm to find a way to reassure the regulator that the outcomes are as the regulator expects. And so, you can imagine a situation at the limit of this road towards machine-readable, machine-executable regulation where the regulator just releases their code and they say, “Okay, plug this in, connect it to your data lakes, and out will come compliant outcomes.” If something goes wrong, who’s to blame? The only person left to blame now is the regulator. That’s not a very comfortable place to be, certainly not if you’re an independent regulator. Like, if you become a sandwich between industry and government, that’s the sort of thing that would end up with the regulator being crushed. So, there will be a natural resistance in some areas of regulation against this level of mechanization. But even in reporting where this is supposed to work well, you know, if you hear the noises coming out of some of the kind of leading regulators in the world — not least the FCA here in the UK — what you will hear is that there’s enormous amounts of data standardization that needs to be done before the promise of even that use case — which is the most promising RegTech use case of all — can be fulfilled. So I’m skeptical about the pace at which we can move towards machine-readable and machine-executable regulation, where we treat regulation as code.

Treating regulation-as-content where we say the regulatory language is what it is and the job of RegTech isn’t really to turn it into push-button executable code, but rather to turn it into workflows and business rules.

Manos: Now the opposite, which does work, but is more human in the way that it does work, is treating regulation as content where we say the regulatory language is what it is and the job of RegTech isn’t really to turn it into push-button executable code, but rather to turn it into workflows and business rules. And so, the idea is that to get from the messy regulatory language to something that humans can work with, you have to have some kind of mental map of what regulations are out there, a kind of taxonomy of regulatory obligations and concepts. That’s one side. And you have to have a corresponding mental map of what the firm looks like — what matters to the firm. So a firm doesn’t see itself as a collection of compliance obligations. It sees itself as a collection of products and functions and locations, and yes, even processes and controls and policies, and so on, and so forth. So, you have to have both of those maps, and then get them to talk to each other — so create linkages between the two sides of the equation. If you’ve done that, then effectively you can get either one application or multiple applications talking to each other by APIs to do this interesting kind of relay of regulatory content. So regulatory content comes in, it gets labeled according to where it has to go, what it’s related to, and then it’s passed on to the appropriate application, to the appropriate subject matter owner with an instruction that implies what kind of workflow is expected afterward. So, that’s messier, it’s more human, but for the same reasons, it’s bulletproof. Eventually, someone will make sure that the system works. Whereas end-to-end machine-readable and machine-executable regulation will usually break down.

[00:30:19.28] Ben: You know, if we think about the idea of machine-executable regulation as being… You know, if we were to be on the Gartner Hype Cycle, it would probably say machine-executable regulation in brackets for reporting, right? And then it would be somewhere quite early in the hype cycle, because, you know, this is probably being hyped, and we’re going to go to the trough of disillusionment. Where are we with the alternative approach, which is, you know, using, I guess, AI and classifiers, and so on, to be able to classify regulatory text at scale, and to serve it up, as you said, into workflows. So this seems like the more promising approach and where are we in the hype cycle with that kind of bridge?

Manos: Just before we move on from machine-executable regulation, I think the key moments in the hype cycle for that, you know, probably, the key moments would have been the FCA and bank of England’s digital regulatory reporting pilot. So that was definitely a hype point in the hype cycle. And if you’ve read all of their lessons-learned reports, you actually feel yourself sliding down the hype cycle. It’s hard to read those and think, “Oh, this was this was a slam dunk.” But then you look at things like, you know, ISDA’s Common Domain Model that basically gives you a way of making both machine-readable and machine-executable a lot of the contract terms around derivatives. And you think, “Well, that’s quiet there. But actually, that seems to be working reasonably well.” And the whole kind of cause of machine-readable and executable regulation has been given a new lease of life with the Saudi-led G20 sandbox, which really is focused on these types of applications. So, you know, I think we’ve still got some time of hype left in the machine-executable side of things.

Manos: But as you said, I think there’s a lot more to be said for regulation as content and the other side or the less ambitious kind of side of RegTech. And there, I guess, the level of maturity is very good. So, when we looked at the market last — you can probably name something in the order of 25 to 30 platforms or tools that are in the regulatory intelligence space, that are really making significant headway in organizing regulation, according to their themes and topics and using things like natural language processing and machine learning to automate that so that they can read rule books at scale. Now, where you want to go eventually is that there’s one kind of virtual front end to every rule book in the world. We’re not there yet. But equally, I think, as long as you’re thinking of private standards only, we’re not that far either. I mean, there’s very significant work done and you can already name three or four firms that are way out ahead of anyone else — I won’t name them here. Now, what you don’t have, though, is some way of reconciling all these proprietary standards into one language of regulation. And that’s quite hard for someone on the purchasing side because what it means is, if you’ve done a lot of work to onboard one of these suppliers and mapped all of your internal systems and controls and processes to their dictionaries and their map of compliance, what then happens if you want to change the supplier? You know, or what has to happen if you want to onboard some other compliance application that needs to talk to that first one, but just doesn’t know the language? That’s the bit that we don’t yet have a very good answer for and there’s no clear kind of commercial incentive for firms to create that.

[00:34:18.08] Ben: Which is the segway into the Regulatory Genome Project, because that is at least partly a public good, right? And it’s aimed at solving exactly this problem of creating common standards and interoperability, right? At the level below commercial applications.

Manos: That’s correct. So let’s start with a little bit of background on the Regulatory Genome Project. So, at the CCF, we were approached in 2017 by what is now Flourish Ventures and was then part of the Omidyar Network with a very specific use case. So these guys were impact investors, they invested in FinTechs mostly in emerging and frontier markets, that were kind of mission-driven to improve financial inclusion. And what they said was, “Look, our portfolio is doing quite well. But one of the things that usually get in the way of growth and manifests itself in the kind of growth plateau at a time that is not really helpful for our firms is that if you want to grow beyond a certain point, then you have to expand at least on a regional basis.” So let’s say you start off in Kenya and you want to cover all of East Africa. Very reasonable. So, when the firms reach that stage in their development, it’s actually quite hard for them to grow because different markets, even within the same region, even if there’s a certain level of integration, have different rules. And so, a lot of time and money, and lawyers fees have to go into making sure that you get market entry just right from a compliance basis. And there’s no obligation for regulators to be consistent with each other or to make life easy for you.

Manos: So, they came to us with that question, saying, “You know, you have access to resources at the university, you know, cutting-edge research on NLP, you know, machine learning engineers — isn’t there something that you could build, that would pass regulation across jurisdictions and make it comparable?” And we thought at the time, well, look, this is a nice applied research program. Of course, we would be interested in looking into this. But what we found as we went along and created a pilot application and tested it, and saw they worked reasonably well, we thought, well, we’ve only covered one domain in this area. We came up with an AML model. We’ve only covered one domain and anyone we tried to take this to as a potential user would say, “Well, what about this other area of application?” So they might say, “Okay, AML good. What about cyber? Or payments, great. But what about insurance?” And it seemed to us that we were going down this rabbit hole of mapping out all the regulations in the world in order to create this one product.

Manos: Obviously, there was also a kind of existential question — you know, the university isn’t really a RegTech vendor, we didn’t want to be permanently in the business of building applications. And it’s a busy space out there, right? Other people have done this longer, and they know this better. So, we thought, what is it that we feel is really needed? Is there a public good that our research can produce? Now, that is consistent with the mission of the university. And so, we thought of an analogy to, I guess, the life sciences. And, at the time, because we were dealing with people who had been involved in the Human Genome Project, it kind of triggered this thinking of, is what we’re trying to build really kind of parallel to the Human Genome Project? And is this pilot application we built, something analogous to an application like 23andMe? And then, from that kind of thinking became the genesis of what we now call the Regulatory Genome Project.

even if you’ve already gone quite a way and had a lot of success in implementing RegTech within the organization, the appeal of interoperable applications and open standards, I think, should be quite significant.

Manos: So, we basically thought we need to find a way to fund and resource and guide a long-term project that maps all regulation. And then, to make sure that it’s available to people truly as a public good, we have to not only make the marked-up rules, I should say — the classified rules — as open data or as near as open as we can make it, but also, we need to find a way to release some of the pent-up innovation out there, by allowing developers and firms to work on this map of regulation, this global map of regulation, and build their own applications. And that way, we don’t have to be, you know, the guys who build everything. We can tap into the creativity and technical skills out there.

Manos: I think what’s really important also, just to bear in mind is the skill sets on the two ends of this journey are just very different. So, building a map of regulation requires a certain amount of technical expertise in the areas of regulation, it requires very strong ties with regulators — which the university has. Whereas, building applications on what we call ‘the right-hand side’ of this journey requires very different skills and a deeper understanding of how the institutions work internally as organizations. So, what does it mean to keep the machine kind of running? And so, to expect somebody to cover all of that is actually quite hard. That means that most people who have innovative ideas in RegTech, either coming from one end or the other end, can’t really deliver the whole thing. So, I guess this is a long way of saying that the key principles behind the Genome Project are, first of all, regulations should be available in machine-readable form as a public good. This is stuff that firms are required to know, by law. They’re made with public money. There is no reason for it to not be open data in a machine-readable format. That’s principle number one. Principle number two is, all of this information must be available to developers in such a way that people can build applications around it. And finally — and this is a key point — both the representation of regulation and the resulting application need to be interoperable. You need to have one common language of regulation. It’s true, different jurisdictions regulate in different ways so, you’ll never get to the point where you say, “Well, this requirement in Brazil is exactly equivalent to that requirement in Mongolia.” But what you do have in the middle is a kind of regulatory Rosetta Stone that can map regulations from any given country against a common framework. Think about, I don’t know, the Dewey Decimal System, right? If you go into a library and you’re a librarian from anywhere in the world, of course, the books are going to be different, but you know that nonfiction is going to be there and you know that life sciences are going to be there. So, that’s the level of interoperability we want to get to.

[00:41:27.18] Ben: And how do you get there? How do you sequence the genome of regulatory information?

Manos: So, let’s get as practical as we can. So, it starts with a paper exercise — I mean Excel exercise — whereby you create almost a hierarchical list of regulatory concepts and obligations. You usually do it by domain. So, you might say, “Here’s my taxonomy of AML concepts and obligations, here’s my taxonomy of cybersecurity, and so on and so forth.” And you know, some of these taxonomies are what you might call horizontal — they cut across the entire financial services industry, so the two examples I gave just now — some of these are vertical. So you might have payments, for instance, insurance, crowdfunding, which was one of the areas of the Center’s particular attention and expertise. And what you do is you create these hierarchical lists of obligations. So for instance, you might say, I don’t know, let’s say you’re dealing with investments, right? You might have client categorization and within that, the definition of an accredited or professional counterparty. You know, perhaps not the best example, but the point is that you always move from a higher level, more general obligations or families of obligations, to more specific ones. Now, at the end of each of these branches, if you will, you will have an end node. You will have the most detailed level of classification of regulations that the genome can manage.

Manos: Now, in theory, there is no limit. You can keep making them more specific, and more specific, and more specific. But remember, the genome as a public good is about making regulations comparable across jurisdictions. So there is a natural stopping rule. You want to stop at the point where the regulatory requirements at the end node are still comparable internationally. So, for instance, client categorization, yes, that’s comparable. You know, the distinction between professional slash accredited investors and more ordinary retail investors, yes, that’s comparable. But if you go all the way to saying, you know, ‘treatment of local authorities for the purposes of client categorization’, you are getting now so fine to the weeds that you’re going to draw blanks for most jurisdictions. And then for everyone who’s subject to MiFID, you will just have this note that says, actually, in most cases, these people are retail clients. So you can guess what the stopping rule is. You go as many levels down as you can until you reach a point where international comparability is compromised. So, that’s how you build that.

Manos: Up to this point, you’re still kind of in the paper world. You can still be doing that in Excel. But then, once you’re happy with the structure you have created, then you can start using machine learning. And machine learning relies basically on collecting large amounts of data from a diverse sample and teaching the machine that a specific example corresponds to a specific node. So, for instance, let’s say you have rules around credit worthiness assessments of consumer borrowers in different jurisdictions. You basically say to the machine, “This is a credit worthiness assessment-related obligation. This is as well. This is as well. This isn’t.” You repeat that over and over and over again until you can train basically a statistical model — which lives as code and we call ‘a classifier’ — so that model can now take in unfamiliar text, and take a stab at what category it fits into. So the next time around you feed regulatory text that you’ve never seen before to the same classifier, and it can say what the probability is that it is about credit worthiness, and you set yourself a cutoff and you say, “Well, if it’s above, let’s say, 70%, 80%, we’ll mark that as a one.” And so, what that does is, if you try to imagine now the machine-readable version of the same regulatory document, that paragraph or that piece of text now carries a tag, an electronic tag that says, “This corresponds to this type of obligation.” And any other application that knows the universe of tags that you’re working with — your taxonomy — can now read this and say, “Oh, okay. I know that this paragraph now is about this.” And that’s how you might be able, for instance, to run queries via an API; you might say, “Can you bring me all the text that’s tagged as credit worthiness assessment?”

[00:46:13.17] Ben: How difficult is the tech there? It sounds almost like, you know, provided you train the classifiers with enough data, then the results will get better and better and better. So, would you say it’s more of a challenge to get the data than it is to get the tech, or am I oversimplifying?

Manos: It’s a good question. I mean, I don’t want to downplay how difficult it is to get the tech. Like, the colleagues who we have working on this are obviously at the top of their game. Having said that, the technology comes with its own significant challenges. What do I mean by that? You know, there isn’t an enormous amount of regulatory tax out there. Now, this may sound really funny bearing in mind what I said earlier.

Ben: Yeah, the sevenfold increase you mentioned earlier. Yeah.

Manos: That’s true. But, you know, from a machine learning point of view, if you look at what kind of corpora people are working with to train machine learning models, they will usually use, you know, all of Twitter for the last three years, or, you know, the entire text of Wikipedia, or the entire internet if it comes to that. So, you know, in comparison to things like that, the amount of regulatory text out there is not enormous. And so, a lot of the challenge is around making sure you have enough samples to actually build good models. The other thing I guess, which people need to appreciate is that the returns to just having more samples start to diminish reasonably early. So, you know, the models don’t get exponentially better as you double or triple the amount of data you have access to.

Manos: Where this becomes really challenging, is, first of all, when you look at really new or niche areas. So, let’s say tomorrow, you know, one of our regulators came up with a very, very specific type of obligation in relation to making, let’s say, AI auditable. So it says, “If you implement any AI applications as a firm, you have to make sure that they are auditable by a regulator — whatever that means. You know, in the early days, only one regulator will have any references to that. So your sample is going to be tiny, right? That is a problem because it means your model runs the risk of having blind spots and you have to find ways of bootstrapping the small sample that you do have, in order to make sure that the classifiers work. I’m not saying that’s not possible, and obviously, my colleagues are working on things like that, but it is challenging. And it’s also challenging when you look at non-English techs because if you create a classifier for AML obligations written in English, that’s going to be completely useless if you’re reading documents in Spanish. But the problem is, if you want to replicate that process in Spanish, your corpus of documents now becomes a lot smaller. And Spanish is, you know, a major global language. Try doing that in Japanese, try doing that in less widely-used languages, that are not the language of business for many people. That is another major issue in that area. But I guess the final issue will always be with these things — and I’ve already mentioned it once already — is that, at the end of the day, there will be errors. And there’s a question of, you know, how much liability should the parties accept for these errors, and who does it sit with?

[00:49:45.07] Ben: If we move beyond the tech and the data — although I think this is a bit related to the data — to this idea of the chicken and egg problem because it’s not difficult to foresee a time when the genome exists and therefore if you’re a RegTech provider, you would build any new RegTech application on the genome because you then don’t need to do all of the mapping of taxonomies yourself. You can just query the public good, right? But between now and then, you’ve basically got to convince software providers to build on the genome, you’ve got to convince regulators to work with you, you’ve got to convince commercial users to use it. So, how do you go about building that ecosystem around the genome to make it successful in the first place? Or, in other words, how do you solve that chicken and egg problem?

Manos: So it’s a fair question. I mean, there is a place you can start, obviously, and it depends on where your relative strengths are. So if you look at other initiatives that have tried to kind of force some level of convergence within industry, they would usually have some strength in one area or the other. Now, if you’re talking about the university’s areas of expertise, obviously, because of our work in capacity building with financial regulators, that for us is the obvious place to start. So we’ve got very strong links to financial regulators around the world and we also know that they have a very strong use case around regulatory benchmarking. So, remember what we said earlier in this podcast that regulators are always checking their homework against the guy who sits next to them. And so, these benchmarking exercises are big painstaking things — expensive, very slow. I remember one regulator saying, “You know, if I had a tool that could do this, I would have nine months of my life back on just the last project.” Which was quite intense but I sympathize with that.

Manos: So the first people to reach out to are regulators. But regulators being involved gives confidence to financial services firms. And not just confidence in the quality of the taxonomies and the classifiers because frankly, regulators will never pull out a big rubber stamp and saying, “I approve of this.” But what a firm can see is that if this is good enough for the regulator to use for their own use cases, then, you know, maybe this is good enough for us as well. I think — you know, as far as industry is concerned — this standard-setting process is also an opportunity to influence in the direction of the common good, in the sense that, of course, you know, no regulator is going to go to a consortium of firms and say, how should I write my AML rules? But giving them the tools to compare against their peers, will usually give you, as a result, better regulation, because people will now have an evidence base on which to say, what is common practice? What is good practice? How do different things correlate with market outcomes or consumer outcomes? So, from an industry perspective, even though you can’t just lobby these people in a crude way, they have been given tools whereby, internally, they can come up with better outcomes for things that you care about. So that’s another reason why industry really, you know, ought to care about creating something like this.

Manos: And then, once you’ve got a few major banks, a few major fund managers, a few major insurers on board, as well as a developer platform through which you can access these assets, then, as a developer, it becomes quite reassuring to know that you can build on this standard because you’ve got the sense that whatever else happens, there are some people who are already on board, and will use applications or will build applications against that standard. So, your investment, your one-off investment in mapping all of your internal systems to this common denominator set will not be wasted. And, as a developer, that can be quite attractive, because the alternative is that every time you onboard a new major client, you have to do all sorts of ad-hoc fixes, so that your systems talk to theirs, which is, you know, expensive work that you’re not always going to get paid for because the client, as far as they’re concerned, it pays for the actual result not for the path you have to walk in order to make sure you can service them.

[00:54:16.15] Ben: So you’ve just launched the Genome Project, and you just started to try to recruit new members, new consortium members — the private sector, the regulated users of the genome. First of all, how is that going? And secondly, if I were a large financial institution, and I had, you know, significant resources to invest in RegTech, and as you say, already had many, many existing RegTech applications and suppliers, what would be the case you would make to join the consortium?

Manos: It is true. We have been in conversation with a number of major financial institutions starting with some of the larger ones, as you might imagine, for obvious reasons, which are now starting to yield results in the form of potential collaborations. Now, that activity is not going to end anytime soon, because, at the end of the day, you want as much of the industry onboard the consortium as possible. But once the first step of recruiting firms is significantly under way, then the work begins to build out the rest of the genome, and also to recruit developers and make sure that you raise awareness of the benefits of your platform and to build the kind of tools that will help developers build applications against the genome. So, there’s a significant kind of technology roadmap, there’s a significant business development roadmap, as well as, of course, the semantic roadmap whereby we’re actually creating the genome itself. So this is just the beginning. But we’re already seeing some of the first successes. Similarly, on the regulatory engagement side. So, you know, we’ve had our first few workshops with individuals from the regulatory community who are willing to dedicate their time to review and make suggestions to improve the various taxonomies. And so, you know, I’m quite confident that if we’re speaking again this time, next year, a significant percentage of financial regulation will have been mapped — and come 2022 we’ll be in a position where people can actually start building applications.

[00:56:31.28] Ben: If I’m a bank and I want to make this case internally — because I presume there’s a price point to join the consortium — how would you convince me, practically, that it makes sense?

Manos: Yeah. I guess it’s always a very different conversation when you’re dealing with a major financial institution that actually has done a fair amount of work in the RegTech space — and pretty much all of them do. If you speak to tier one bank, they have been bombarded with proposals from RegTechs, and even from potential consortiums as well. And so, I guess the way people will usually respond this — you know, why do I really need this sort of thing? I’ve already got fairly mature solutions in-house that I’m reasonably happy with. So where is the real kind of long-term strategic value?” And I guess there’s three layers to this. The first one has to do with how procurement works effectively. It’s great that you’ve got the supplier that you’re happy with. That’s amazing. However, what it also does is it locks you in because you’ve invested a significant amount adjusting your internal systems to fit with theirs, and particularly adjusting at the semantic level — so, making sure that all of your other applications speak the same language as the vendor and can map to the same taxonomies. Now, that’s usually a significant sunk cost. And so, a firm that wants to move away from a supplier relationship doesn’t actually have a lot of good options, because they’ll have to take on the cost of doing this all over again if they onboard somebody new. And it’s very unlikely that they’ll be able to get a startup, for instance, to do that work because the startup just doesn’t have the cash and the runway with which to do it. So you end up in a situation where you’ve got a significant supplier lock-in. And it shouldn’t really be the way that a major financial institution runs compliance technology. So, that’s one part of the answer.

Manos: The other part of the answer is that usually, even when you do have really good applications, they tend to be limited in scope. So they will either be limited to a few domains that they were originally built on. So let’s say, you know, anywhere in Europe or anywhere in firms that deal with Europe in any way, people will have built ad-hoc systems to deal with MiFID compliance, for instance. You can’t then repurpose that to deal with some new type of securities law that comes in 10 years down the line. If you’re lucky, maybe you have architected that way but most people will not have. So the benefit is that dealing with a kind of de facto standard, like the genome, as and when it becomes available, builds some longevity into the applications that you do build. And obviously, it’s not just scalability across domains. It’s also, are you able to serve jurisdictions that are not in the magic circle of jurisdictions that suppliers usually target? So if you think about what most applications can deal with, they can deal with EU, UK, US and Canada, Australia, Hong Kong, Singapore — that’s your magic circle. Beyond that, you know, here be dragons in many cases. So being able to have that same level of scalability and functionality beyond those core jurisdictions is a huge benefit.

Manos: And then finally — and I think this is the more where interoperability really comes into its own — is when you deal with suppliers or partners to whom you have the cascade regulatory obligations, or with which you are tied together in a compliance pipeline. So I’m thinking of things like, for instance, product governance, where the producer of a financial product and the distributor of a financial product are tied together in a set of obligations around, for instance, identifying what the target market of a product is, identifying any applicable risks, understanding what kind of uses the clients are supposed to have for these products, reporting on whether it is sold and distributed in the way that was envisaged. Now, all of that requires that information flows between two very different firms — you know, the distributor might be a huge bank or it might be an IFA; the producer will usually be a very substantial financial institution — but they can be very different is what I’m saying. Similar things happen, for instance, when you cascade obligations in the area of cybersecurity or cyber resilience, where the two organizations — the supplier, the vendor, and the buyer — are actually very different organizations. So, if you need their systems to talk to each other, you need some common denominator to map them against each other. Otherwise, you risk, again, that kind of lock-in that we talked about earlier with regards to suppliers. So, I think the bottom line here is even if you’ve already gone quite a way and had a lot of success in implementing RegTech within the organization, the appeal of interoperable applications and open standards, I think, should be quite significant.

[01:02:03.05] Ben: Let’s assume that you build this, it gets wide usage, you overcome the chicken and egg problem, then we can imagine the network effects — the flywheel of network effects — will really start to kick in. And you know, then you’ll be able to level the playing field between regulators, regulators will get better feedback to make better regulations, there’ll be fewer barriers to entry for new vector companies. And so, you’ll see this unleashing of new RegTech innovation. Firms will be able to comply with regulation more cost-effectively, more quickly. Would you describe that as the end state, the kind of collective good that will be created, or is there anything I’ve missed?

Manos: So, no, I think you’re mostly there. I mean, what I would expect to see if this whole thing works properly, is that in the end, there is a marketplace where firms can engage developers to work on the genome — you know, they don’t need to involve any of us in any way. But also, regulators can start writing regulation that is as machine-readable as possible. So, for instance, right now, there are standards like a common torso for writing machine-readable documents at the document level. You know, you can do a lot better than that if you have a common standard for what is in an AML document or what might be in a cybersecurity document. At some point, once you’ve reached critical mass, you’ll start to penetrate a lot more deeply into how regulators do their work, and also a lot more deeply into how people build applications. And that, to me, is what success will really look like — that people start considering your standards at the outset of building their tools and applications.

Ben: Manos, thank you so much for coming on the show. It’s been great!

Manos: Thanks for having me! A real pleasure!

Hard Truths about Digital Banking (#32)

Structural Shifts with Leda GLYPTIS, Chief Client Officer at 10x Technologies

We’re discussing with Leda Glyptis, a self-described recovering banker and lapsed academic, who’s worked in technology implementations for the last 20 years. Leda is one of the leading voices in banking and FinTech today, she has served as Chief Innovation Officer at QNB group, she was Director of EMEA Innovation at BNY Mellon, and most recently she was Chief of Staff at 11:FS. In this episode, Leda and Ben discuss what a Chief Innovation Officer actually does, whether innovation can come out of innovation departments, what most companies miss when they talk about culture, why emotions are holding back traditional and challenger banks from making money, why selling banking services like supermarket offers doesn’t work and what banks should be doing instead. For more information on Leda, look up the hashtag #LedaWrites on Twitter. She publishes an article every Thursday.

Leda recommends

 

  1. One book: “To end all wars: A Story of Loyalty and Rebellion, 1914–1918” by Adam Rothschild
  2. One influencer: if you don’t follow Bradley Leimer already, I don’t know what you’ve been doing and you don’t know what you’ve been missing
  3. Best recent article: ‘Can empathy be the cure’, by Theodora Lau
  4. Favourite brand: Converse All Star
  5. Productivity hack: I have ‘writing spaces’ — windows of time in spaces away from my desk where I write with no interruptions, no internet access and usually with a specific time box imposed by a friend arriving to join me in a café or park at a given time or by virtue of doing it on a flight or train ride.

One should only build the technology that is tied to their differentiator and partner or buy the rest

[00:01:28.12] Ben: Thank you so much for coming on the Structural Shifts podcast! I wanted to start off by asking you how one goes from studying social and political science to becoming a banker?

Leda: First of all, thank you very much for having me. The answer to that is ‘by accident’. I have always found it extremely impressive and confusing when I hear people talk about their careers and say, “You know, when I was 17, I decided I want to do this, then I had a plan, and I did it.” I don’t know who these people are. This was not me. Mine was entirely accidental. As I was finishing my Ph.D. with a series of deaths in the family which knocked me for six, I found myself sort of delayed and frustrated, ended up getting a job in, actually, private security of all things, and was my first taste of corporate life and working with technology investments — because the company was investing in non-weapons defense technologies at the time. And I found myself quite far away from academia, in a place that was interesting but didn’t make that much sense. And I chatted with a friend one day saying, “There are parts of my job I really like, parts of my job I don’t like, but I really don’t know what to do now, where to go next.” My friend said, “Well, we’ve built some software. We want to sell it into banks, but we don’t like people and things. You like people and things. Why don’t you join us?” It was an absolute audacity of your mid-20s. I thought, how hard can this be? And it turns out, it was quite hard, but it was also quite incredibly interesting. And I fell sideways into banking IT, and I haven’t looked back since, to be honest.

[00:03:02.08] Ben: I wanted to ask you: so, your last job in banking was at QNB and you were Chief Innovation Officer. What does a Chief Innovation Officer do? So, for example, is that a role that holds a budget, or is it one where you sort of seek to influence the rest of the organization and guide them towards some sort of digital future?

Leda: It absolutely varies. In some organizations, the Chief Innovation Officer is part of a marketing effort and they’re there to drive organizational learning in and organizational positioning out. In those cases, the job doesn’t have much of a budget, and it tends to be all about teaching the organization what they should know and helping the organization tell a story to the market about how they’re thinking about the future. Then, there’s another type of Chief Innovation Officer that it’s all about the third frontier of technology — so, the stuff that is really out there, that is not going to be useful or usable for the next 10 years but the bank should be thinking about them. They’re doing a lot of experiments, and they tend to have budgets for POCs, but not much beyond that. And then, there is the Chief Innovation Officer that is essentially the new technology IT person. So, I would say that my role at BNY Mellon was a combination of the first and second. So, while I was at BNY, my role was a lot about bringing learning into the organization and helping the organization position itself in a changing market, and running experiments with technologies that, at the time, were very new for us. My role at QNB was very different. It was, what are the things that we should be doing and we should be seen to be doing for the type of corporate citizenship we want to have in our chosen markets, both in Near East Africa but also in the sort of Far East subcontinent and beyond — Southeast Asia, where competition and technical literacy was extremely high. So, the Chief Innovation role for QNB was, “Come in and help us do the things we need to do fast, but also help us move the needle a little bit on the ways of working internally.” And I say ‘move the needle a little bit’ because a lot of Chief Innovation Officers are all about the internal workshops. This was, I would say, more indexed into doing things that were business focused and external-facing without changing the infrastructure of the bank. So, it was things that could either plug into that infrastructure or stay on the glass, and less about changing the ways of working. So, to answer your question, it could be anything, and my two innovation roles have actually been very different — but very useful in the sequence that they were in because a lot of the experiments we had done at BNY Mellon were the learning I needed in order to go straight into implementation at QNB.

[00:05:56.05] Ben: Do you think it’s a more important role than it was in the past?

I don’t think there has been a clear sense of where profitability will lie in the future.

Leda: Perversely, I would say no. Actually, somebody called me recently and said, “Would you take another innovation role?” I was like, “Nope.” I think it was an extremely important role early on because it both signaled internally and externally, that the organization is engaging with some hard topics. And also, it showed an acknowledgment that the way we work, the way we learn, isn’t right for the way that the market is moving, and therefore we need to change. Fast forward almost 15 years later, there are very, very few organizations that have moved the needle meaningfully in terms of either way of working or transformative technology use. Some have, but they’re few and far between. And even those that have, haven’t done it through their innovation departments. So, I would say that the function it represented as a department is more vital than ever — the new ways of working, the different deployment schedules, leveraging technology differently, all of that is more important than ever — but I would say that the structure doesn’t work anymore. What the innovation departments taught us is that we can’t do it through innovation departments. It has to be right at the heart of the business.

[00:07:10.10] Ben: And do you think that’s why those banks have found it so hard to introduce significant change? Because there hasn’t been this sort of CXO buy-in, other broader buy-in of management. And therefore, do you think it’s as much cultural as it is technological change that’s needed?

Leda: Yes and no. So, I think there is a cultural change that is bigger than the technical change — I think you’re right — but I think it’s much more systemic than saying people are resisting. I don’t think people are resisting. I think the structures we have created are not conducive to the type of decision making we need. Everything from the fact that you may be running an agile project in your part of the bank, but the testing schedules for the wider bank are waterfall and therefore you need to book in your testing before you’ve started building. It’s mad, right? It makes no sense. But it is how it is. Similarly, the risk matrices you apply, the way you measure success on a quarterly basis, the way that shareholders measure success, all of those things we bundle under culture change, but it’s actually much bigger than culture. It’s about how we build up the business, how the business reports success to the owners of the business, and how the business makes sure mistakes are not made. So, it is facile to say culture and dismiss all of those things as an attitude problem. It isn’t. I would say that the biggest challenge — when we started this journey, part of the question was, “Well, are these technologies real? Are they useful?” And we spent a lot of time in labs, testing and finding that the technologies are both real and useful. They’re robust, they’re scalable, they reduce the total cost of ownership, they do all the good stuff. But they also fundamentally transform the business model, both in terms of how they enable you to operate in a way that you’re not prepared to operate in — the speed of decision making that these technologies enable you to do, you don’t have the governance for. So there’s a big change piece that is around governance and approvals that is human, yes, but not just cultural; it’s organizational. The second piece is that cheaper infrastructure and faster infrastructure kind of requires a different business model because you can’t go charging the same for a very different service. Your customers are wise to the fact that you do different things and potentially less from a human perspective. So, I would say that the challenge hasn’t been technical for a while. It’s governance and monetization.

Maybe what we’re seeing is a transition to a world where retail banking is a public service utility. And I’m not saying it necessarily needs to be run by the government but it is approached by a utility, and therefore the profit structures become very different. And that’s something that your challenger banks don’t necessarily address

[00:09:46.03] Ben: On that topic of business models, banks, in general, know where they’re headed in that direction — you know, what the business model opportunities are — and if they know where they are, which one would suit them best?

Leda: I don’t think they do. I don’t think they do. And it’s not an easy thing. I don’t think there has been a clear sense of where profitability will lie in the future. I was recording a podcast with John Egan from BNP Paribas, recently, and he led with the statement, “Banks don’t know how to make money in the new situation. Therefore, what are the options?” And it’s very refreshing to hear someone say that from within a bank, although admittedly, he doesn’t sit on the traditional side of the bank. I think there are a couple of pieces there. One is the appetite of the market is shifting. Certain products that we were comfortable seeing being profitable, aren’t profitable anymore. Retail banking isn’t profitable. Mortgages, credit cards, institutional banking, transaction banking, investment banking, that’s all still profitable, but the regulatory pressure to change pricing and the way that money is made is definitely making it less profitable than it once was. It’ll be interesting to see how far the regulator will push certain things. I’m seeing banks change their infrastructure and invest in technology, not because they want to be seen as innovative, but because they want to lower their total cost of ownership. They’ve reached a point where growing their top line is much harder than it used to be, then, actually reducing your operating costs is the only way to increase profitability. So, we’re definitely seeing that shift. But I would say that monetization is a challenge for the challengers — funnily enough — not just the traditional banks, because the challengers, they are extremely well-capitalized, burning through cash, building up something that is very, very beautiful from a UX perspective, that is, challenging banks, the assumptions we had on how hard or easy it should be to do certain things, they have definitely reduced what has now come to be considered predatory pricing and all of that. But at its bare bones, their business model is not too different. I was at a panel a few months ago, and Nick Ogden turned to Anne Boden and said, “What challenger? Your business model is exactly the same as everyone else’s!” And Anne made some very interesting points around pricing and focus on the consumer. And she’s right in all of those points, but actually, at the level that Nick was raising the challenge, he is right. If you look at the challenger banking model, their proposition was, “We can make money the same way, but by being cheaper to run, we can also be cheaper to use. So we will pass that benefit to our customers.” The reality is, retail banking is not profitable, not in the same way it used to be. And the traditional banks are making money because they have universal banking. And the challengers are looking at their business model going, “Oops, that doesn’t make money.” You know, there are the Revolut’s of the world that do make money through crypto trading. There are other ways, but the traditional retail banking, as we knew it, is only profitable for the big banks, after the third or fourth product per customer, which is not a scale that your challengers have. And I went on for an hour here to answer a very straightforward question. I don’t think they know how to make money. And I don’t think it’s an incumbent problem. I think it’s a systemic problem. It’s banking, as we know it.

[00:13:26.04] Ben: If retail banking becomes some sort of a lost leader almost, to around which you have to bolt on more profitable businesses, what does a more radical business model look like? One that accepts the premise that, you know, retail banking is not inherently very profitable.

Leda: I would start with the proposition that maybe it doesn’t need to be. Maybe what we’re seeing is a transition to a world where retail banking is a public service utility. And I’m not saying it necessarily needs to be run by the government but it is approached by a utility, and therefore the profit structures become very different. And that’s something that your challenger banks don’t necessarily address because, in order to have a credit card and an affordable mortgage and an affordable consumer loan, you tend to have a balanced book, underwriting, repacks, and investment vehicles that move that debt around and leverage it in instruments that are highly complicated and have nothing to do with retail banking. And that is how you make mortgages more affordable. That’s how, allegedly and theoretically, you make credit cards more affordable. Now, I think there are two questions inherent in the question you just asked. One is, can you create retail banking that is systemically independent from institutional transaction investment corporate banking? And I would say not with the current pricing models that we’re used to because things slosh about and move around. And the second is, can you create a business model that says, “It won’t be particularly profitable, we will do it at cost and we will perceive it as a utility.” It is possible. The technology we have to do it would allow for the running cost and maintenance cost to be lower. But I would say that the cost of lending will probably go up, or you will have to pay for a current account, which in some societies already happens, and people wouldn’t even blink. But in places like Britain, people were like, “Whoa, what’s that all about?”

even if it makes perfect sense to focus on the thing you’re best at or the thing customer comes to you for and leave other things to others who are better at them, there is an emotional blocker there

[00:15:30.03] Ben: Do you think we’re seeing the first indications that that’s actually happening? In the sense that the manufacturing balance sheet part of the banking is becoming more and more heavily regulated and I guess less and less profitable? And then secondly, because we’re already starting to see big bank mergers, which would suggest that we’re moving into a phase now where institutions are trying to just maximize economies of scale, which is what’s at play here. Would you say we’re already heading in that direction or we’ll take a more direct intervention from governments or regulators to make it happen?

The challenger banks measure their success in terms of accounts or in terms of being primary accounts, but the number of people who close their high-street bank account is minimal. The whole notion of being multi-banked is a given now.

Leda: It’s too early to tell, actually, is what I would think. We’ve definitely seen, as you rightly point out, some mergers and consolidations. But in the world of banking, those mergers and consolidations — or de-mergers — are part of how business is done. We have not seen big banks exit retail banking, which I bet is tempting. But actually, bankers, not to bash them as cynical, but I have never met a bank CEO who didn’t feel a sense of duty towards the community they serve. And even though no bank CEO’s retail arm is where the money is made, they all feel extremely strongly about retaining that. And I can’t stress that enough, there is no bank out there that I can think of — actually no, I lie; there are a couple in very particular circumstances — but for the vast majority of banks, their retail division either breaks even or loses some money. But no one ever considers killing it, because they do feel a sense of duty and responsibility to their communities. And they don’t need the regulator to tell them that. They actually do that themselves. So, to answer your question in the negative, the obvious thing would be to kill your retail banking and focus on the profitable stuff, but people don’t. And I don’t think the regulator would permit it, even if people were inclined to go that way. I think there will be a couple of things: there will be consolidation, as you say, because there’s definitely profitability in scale. I think we will see an acceptance that certain products will become less profitable, and that will become the new normal. And I hope — but I have seen very little indication of that — I hope that people will start making the hard decisions to invest in the infrastructure of the core entity, not the greenfield captives, not the small experiments, but really create an overhaul of the infrastructure of the bank, that will mean that the cost of ownership and the cost of doing business will go down. And therefore, yes, you know, the return on equity will be terrible for a few years. But once they’ve paid off the cost of build, then actually, they will have a much lighter infrastructure. So the fact that certain things are not as profitable won’t matter as much, because they’ll be much cheaper to run.

[00:18:26.28] Ben: I want to come back to that point about how banks should transform technology. And so, I’m going to come back to that, but just in the meantime, I wanted to ask you: so, if retail banking doesn’t necessarily get split off from other types of banking, do you think you’ll have different players doing the manufacturing from those that do the distribution? Because, as you say, the manufacturing part is capital intensive, it’s not very profitable, but the distribution part seems to be where you could achieve network effects and where you could achieve much higher margins and potentially very low cost of customer acquisition and so on.

No one will ever enjoy buying banking services. One of the things that the banks have to accept is that you can make it as snazzy and fun and cute as you like, it’s not going to change the way people feel about it.

Leda: Well, you speak sense, and that should be the direction of travel, right? Whether it will happen or not, will depend on a lot of things. Regulation is one — we don’t have a clear direction of travel from the regulators, but there is an increasing push for separation clarity and demarcation lines between different pieces of the life cycle that the regulator is pushing towards. So, that may be a factor. But what is holding banks back from doing this is emotional, it’s not practical. I mean, over the years — I worked in a transaction bank and custody bank and I kept saying to them, “Plumbing is amazing! Why do you care about the sexy stuff?” Like, plumbing is where you can make money, you’re needed, but it is unsexy and people emotionally want to do the more exciting stuff, the client-facing stuff. So, even if it makes perfect sense to focus on the thing you’re best at or the thing customer comes to you for and leave other things to others who are better at them, there is an emotional blocker there. So, you see, for instance, quite a lot of the traditional high-street banks who don’t actually drive profitability through their retail businesses, should say, “I’ll tell you what: open banking has landed, I’m not very good at this digital journey stuff. But people still want to have their money in a place that feels secure, so why don’t you, Mister Startup, create all your propositions on top of my platform and account, your customers’ money will be in an HSBC account, but they won’t even see HSBC, they will see PensionBee and Revolut. Neither of them is doing that, and there are many reasons for it. For the challengers, it’s both the independence that you get from having your own license, but also the feeling of being a grown-up and sitting at the grown-ups table, and not just being a little app that sits on top of another system. The traditional banks are convinced from the old way of running relationships, that owning the customer is important, right? If you sit inside a traditional bank, there are usually fights between departments about who owns the customer. The notion that you need the customer touchpoints, you need to own the customer, that’s where profitability comes from, is actually complicated, convoluted, and in some cases, entirely misled.

Leda: The point is that you have the challengers spending a lot of time and money creating infrastructure that, to your point, should be created by someone else and it should be sold as a utility to all banks. The traditional banks are spending a lot of time trying to create propositions and user journeys that they’re not very good at. Meanwhile, they don’t make any money from them and they could just sit back, take the deposits, let other people be creative. They were symbiotic relationships that could have been explored and haven’t. And I think we’ve reached the point now, where none of what exists makes sense at scale. All of the various banking players will need to think about scalable and robust infrastructure. And, as part of that same discussion, they will need to think, “What am I for? And do I need to build all the bits that I will use to be that?” And my personal view is one should only build the technology that is tied to their differentiator and partner or buy the rest because it means that you carry less legacy, you carry less need for dependence on know-how, and if technology moves on and your provider doesn’t, then great, you change providers.

[00:22:37.11] Ben: So if we think about, I don’t know, eCommerce, right? You’ve got Amazon as an aggregator, and Shopify as a platform, right? How do you think it plays on banking? Do you think banks can be aggregators? Or do you think they’re destined to be platforms?

the data that you need for timely, intelligent, embedded financial services is there, but nobody is doing it yet

Leda: That’s a very good question, and I think it depends on two things. One is the economics of it. So, the way that financial relationships are monetized right now makes it very hard to go down, actually, either of those paths, because the way you make money is hard to unbundle. It’s not a case of, “Okay, now you will be doing 30% of that process, so you get 30% of the revenue.” It’s sadly not how it works. The second challenge is, which bank has the technology to actually even start thinking about that? The people who are quietly, but interestingly, doing quite a lot of that work is Standard Chartered. They are looking at the types of work they have historically done and creating partnerships to allow them to retain their usefulness. So, it’s less about, are you an aggregator or are you a platform? And more about, in what you currently do, where do you retain brand relevance? And where are you still actually a meaningful part of the puzzle? And who can you partner with upstream and downstream to make that piece where you’re still good, bigger? And the only bank I’ve seen do that to any meaningful scale, actually, so far is Standard Chartered.

[00:24:08.19] Ben: The big advantage that the incumbents have, as you say, is every challenger is spending hundreds of millions of dollars on trying to acquire customers that the incumbents already have.

Leda: That the incumbents already have and don’t lose, right? Because it’s actually a false statistic we see. Because you are absolutely right. The challengers measure their success in terms of accounts or in terms of being primary accounts, but the number of people who close their high-street bank account is minimal. The whole notion of being multi-banked is a given now. I don’t know a single person who has one bank account.

[00:24:43.27] Ben: Yeah. So, you’re almost saying that that’s not a meaningful statistic anymore, right?

Leda: No. So, I’ve done a very informal survey of a few friends of mine who took the leap, so to speak, and started paying their salary into a challenger. And so, rather than having your traditional bank for your salary to be paid into when you’re spending money, playing money in your challengers, they actually started paying their salary into their Starling, their Monzo, their N26. But you ask the next question, if the vast majority of them sweep what they don’t expect to use immediately. So, actually, the deposits, which is where the money is made from a banking perspective, still go to the traditional institutions, either because they offer better interest rates, or because they offer higher protection, better security. The motivations are multifaceted, but if you say that the main thing that a banking player will monetize is deposits, then even the people who pay their salary into the challengers — and I would say that that number is nowhere near as high as the total number of customers, obviously — even these guys don’t leave their deposits in the challenger in any meaningful sense.

we will see much more embedded finance, much more embedded payments, actually much more complicated financial transactions being embedded in the commercial activity, but it won’t be driven by finance. It will be driven by the consumer need and the consumer opportunity.

[00:25:57.29] Ben: And do you think that’s like some sort of proxy for trust? And do you think trust is the key attribute to be able to do aggregation? I.e. I’m going to introduce you to other products and services you might find useful and value-added, because you’ve given me your trust?

Leda: Trust is absolutely vital. However, I think the main thing is that people don’t want to think about any of these things unless they absolutely have to. So, the proactive up-and-cross sell the banks are trying to do is noise. Nobody says, “Do you know what I’m gonna do today? I’m gonna pick a car loan. This is my plan for the afternoon.” People will say, “I have to renew my mortgage and I hate it, and I’ve been putting it off.” No one will ever enjoy buying banking services. One of the things that the banks have to accept is that you can make it as snazzy and fun and cute as you like, it’s not going to change the way people feel about it. The second thing is, people want these things to be available when you need them. So, I keep getting mortgage offers from my bank — my high-street bank — even though my mortgage is paid for out of that bank; but I get first-time buyer offers on a weekly basis. So, the data that you need for timely, intelligent, embedded financial services is there, but nobody is doing it yet. And I mean, from the standard banks. And I would say that the challenges are not doing as much of it as they could.

Leda: There was a proposal I saw recently that N26, was going to be doing this. I don’t know whether it actually went live or it got delayed because of COVID. But essentially, it was, if all your movements take place within your N26 account, N26 says to you, “Hey, leader, you pay this much for rent, you qualify for this kind of mortgage, and you can afford an apartment in the neighborhoods you do most of your spending in. So, in the neighborhoods where you spend your life, you can afford to buy.” Those data points are actually available, either publicly or through your own protected account. Now, that is a useful service, right? That is intelligent, embedded finance. But I don’t think that my mortgage provider saying “Do you want a credit card? You can afford one.” Or, my high-street bank saying, “Would you like to buy a house? We can help you.” is in any way helpful. Trust or no trust — because, of course, I would trust them to execute — but it’s not like the supermarket where you will buy your favorite shampoo because it’s an offer even though you don’t need it. And yet, the way financial products are promoted is exactly like your supermarket offers. People will see it and buy it. No, it doesn’t work that way. So, intelligent embedded finance is technically possible. It’s absolutely possible from a data perspective. When it becomes the way we offer services, then the people who do it best will be the people who read the situations best, not the people who have the best pricing.

digitization is not about allowing the banks to dominate this conversation. It’s about allowing them to stay compliant and relevant with the way people live

[00:29:01.18] Ben: So you’ve mentioned the term ‘embedded finance’, which is something that’s become, I think, really quite fashionable, over the course of 2020. Do you not think that it will be embedded into products and services that aren’t financial at all in nature, i.e. those that have the highest engagement? Because that always seemed, to me, the problem, as you said, which is, I only go into my banking app when I need to do banking, whereas, you know, I’m in WhatsApp all day. So, isn’t it easier to try to engage me with financial products and services through apps and services that I’m regularly using, and in which I have the pool of engagement?

Leda: I would expect so, but I think that the starting point would be interesting. So, for instance, the Uber example is a very interesting one, right? By taking away the process of payment, they’ve given you back, what? Two minutes of your life? And yet, it felt like a revelation the first time it happened. Uber didn’t create embedded payments to help MasterCard make money. They did it to create a proposition that would make them more attractive to the user. So, I think we will start seeing embedded finance for that purpose. And it will put everything on its head. I was speaking to someone who works for Experian, and they were saying, “Creating a good credit footprint has become second nature. We all know you need to brush your teeth and have a good credit score.” But the reality is that assumes you need to enter the credit system. That’s an assumption that we have made without even thinking about it. But is that the right thing? Is that something we should be encouraging new generations to do? So, for me, the interesting thing is, we will see much more embedded finance, much more embedded payment, actually much more complicated financial transactions being embedded in the commercial activity, but it won’t be driven by finance. It will be driven by the consumer need and the consumer opportunity.

[00:30:56.12] Ben: If I were to summarize, you’re saying that it will be more intelligent, more useful, and it will be pull not push, right?

Leda: Yeah.

[00:31:03.25] Ben: Changing the topic slightly, do you think that open banking is the catalyst to move us to this world of pull not push, and intelligent and useful embedded banking services?

the consumer will not choose the bank that has the best user journey; they will choose the bank that gets out of their way the most

Leda: I was asked a similar question not too long ago, and we had been talking about dance earlier and something entirely unrelated. And I used the analogy in a way that was relevant in the moment, but I think it still works. And what I said is, gravity is essential for dancing. But nobody thought, “Gravity is great. How could I use it? Let me invent dancing.” And the thing that has been frustrating for most big organizations that open banking came, and the banks kept looking at it until the eyes bled, and couldn’t figure out how to make it work for them, how to make money through it. And I heard equally a lot of startups looking at it and going, “There’s an opportunity in there but I don’t know how to monetize it.” And I think that if you stop staring at it, and you start going down the path of solving real problems, then open banking will be an enabler, a facilitator, and an accelerant to things that you can do to solve real problems. A little bit like you couldn’t dance without gravity, but the two are not… You know, nobody came up with dancing or slides by thinking I need to use gravity for something.

the boat that said ‘digitization is your key to future profitability’ has sailed. The boat now says ‘digitization is a key to survival and compliance’

[00:32:20.16] Ben: It seems to me that the whole digitization of the industry banking, is the new driving force to embedded finance, all these other downstream applications that will be super useful and value-added. But it doesn’t seem like open banking itself is actually that relative to everything else that important. Or do you disagree? Do you see it as being really quite significant in pushing us towards this?

Leda: I think open banking is going to be significant in enabling solutions that wouldn’t have been possible before. But it’s for the consumer, and for the creativity that the industry will see. It’s not for the incumbents. I think that, as I said earlier, embedded finance, and those truly empowering capabilities won’t come from the banks. They might be powered by the banks, but they won’t come from the banks. So, the revolution and the truly transformative pieces won’t be because the banks finally found a way of doing this. It will be because somebody thought of something that is now possible because of open banking. So, digitization is not about allowing the banks to dominate this conversation. It’s about allowing them to stay compliant and relevant with the way people live. So, for instance, at my high-street bank, you can’t set up an international payment on the app. You can only do it online. Now, if you’re not a banker, you either don’t question that, or you assume it’s for security purposes. If you are a banker, you know that’s because their systems don’t talk to each other and the online bank is on an entirely different infrastructure than the mobile bank. The reality is that a time will come — and that time is not far away from us — that the challengers and some of the incumbents will solve some of these problems. So, the consumer will not choose the bank that has the best user journey; they will choose the bank that gets out of their way the most. And by getting out of their way equally means not bombarding them with products they don’t want, but also enabling them to do things on-the-go. I remember a few years ago, I was here, in Athens, visiting my parents, and I needed — it was a routine KYC check for my mortgage. I couldn’t do it remotely. I had to go in-person into the branch that has changed in the intervening time. So, there are things that are hygiene factors these days, both because the customers expect them and because the regulator expects them, but I think that the boat that said ‘digitization is your key to future profitability’ has sailed. The boat now says ‘digitization is a key to survival and compliance’.

[00:35:06.26] Ben: I suppose asking the question a different way, do you think that… So, open banking kind of creates an obligation to share customer data. Customers, as we observe in other realms, they’re happy to share their data where they perceive there’s a utility for doing so. So, do you not think that this will happen anyway, as the right use cases emerge?

Leda: I don’t think that use cases will come from the banks. Now, I think open banking is not an obligation to share data, it’s an obligation to share infrastructure that enables the sharing of data, should the customer consents to it. And the onus was on that consent mechanism, because banks would allow you to screen scrape in the past, which is extremely insecure, but cheaper to do. I think creating that infrastructure for consent and control has been what? Has been a sort of a point of contention, because it was expensive, and the banks couldn’t figure out how to make money with it. But I firmly believe that the creative solutions that leverage open banking are not going to come by people who look at open banking and think how can I make this — make money? They will come from people who are solving problems and go, “Oh, look! With open banking, it’s much safer and easier to do that.”

[00:36:18.12] Ben: What effect you see COVID-19 having on banks, on B2B FinTech companies, and then on the B2C challengers?

Leda: I think it very much remains to be seen. I’m extremely skeptical of all the triumph of, ‘this has sealed our digital efforts and accelerated and…’ I don’t see that. What I see is, banks that knew they had challenges and problems in their infrastructure faced those problems by throwing more people at the problem, the people working extremely hard to create bridging solutions, and delivering for the clients and the bank being extremely proud of their people — as they should be. And then, the conversation of whether they should actually challenge change, restructure being not even started. So, I would say that the banks have done well in the midst of COVID because of sheer hard work, creativity, and determination on their human sides. But not because their systems were up to scratch. And I’m not seeing anyone saying “Okay, our systems were not exactly up to scratch, and I should do something about that.”

you can’t change systems without changing the supporting economics and surrounding governance. Which means that if you’re doing the slow refurb process, you’re going to have a schizophrenic organization for quite a long time

[00:37:30.03] Ben: What about, do you think, they’re investing more in B2B FinTech solutions to help them to digitize faster or to, at least, service customers digitally faster than they were?

Leda: There is some loan disbursement work that has gone ahead. But honestly, what happened when COVID hit is that people went into panic mode because they needed to deploy quick solutions for particularly loan holidays for both businesses and individuals. The systems were not set up to do that. And the reality was that if you have a COBOL-based system to change an interest rate is two months’ worth of development work. The reality is they threw the people at it, they made the change, but the system is still COBOL-based.

[00:38:13.24] Ben: And then, what about the B2C FinTech companies? Because there was a piece that was written by McKinsey, I think it was called, “Rerouting Profitability” or something, and they made the point that all of these challenges in the unit economics of some of these businesses have been laid there, and now they’re struggling to raise new funding, and the FinTech sector is an existential crisis. How do you react to that kind of comment?

Leda: I’ve heard about the existential crisis before. I saw that report. And they were saying the FinTech sector is an existential crisis, then the FinTechs responded all over Twitter and LinkedIn, “You’re the one, an existential crisis”. And the reality is, no one is an existential crisis. This is a long game. It’s a long game. And in the last 15 years, everything is moving in the direction we said it would, but because it’s not moving as fast or as radically and because the startups that were around at the beginning are mostly not still around, therefore, the winners and the losers are not as black and white, people are feeling a little bit more relaxed than they should. But we said 10–15 years ago, the economics of banking are changing, the significance of technology is changing, the world will be more connected, service orchestration will be the name of the game, unit economics will change. All of that is happening. It’s just happening slowly, as you should expect, and therefore, I don’t understand people who are trying to find comfort in these radical revelatory moments of, “We fixed it” or “This was wrong” or “This is right, and that is wrong”. It doesn’t go that way. There is a direction of travel and we’ve been very much moving in that direction for 15 years. Certain events accelerate certain parts of the journey — regulatory moves, certain mergers, the rise of the Chinese giants, COVID. But it’s not a pivotal moment after which everything is different.

[00:40:10.02] Ben: What about payments? Do you think the impacts on payments from open banking has been more transformational?

Leda: I mean, my answer when it comes to open banking is, it is what it is, right? There’s no change. Payments are in an area that has had a lot of focus and has had a lot of innovation over the last 10 years. It hasn’t been transformative, because the monetization relationships are similar. But we’ve seen incredible speed in payments, particularly cross-border. So, payments, I would say, is a place where we’ve had so much innovation and creativity, that unless the rest of the banking infrastructure starts catching up in terms of the build of the systems that can support, there’s only so much more that we can see in payments, because there’s a little bit of saturation.

[00:41:00.10] Ben: I want to go back to something you said earlier on. If we think about this, there’s typically been a few approaches to technology transformation. One is the Big Bang approach, which I think is becoming increasingly less viable or palatable. And then you’ve got this progressive…

Leda: People lose their jobs when they do that, right?

Ben: Yeah. I mean, I just think it’s just, the time to value is too long, the risk to value is too high. And so, we’ve seen people moving to more progressive renovation type renewal strategies, and then also, as you said — I think you used the term ‘greenfield captives’ — but this idea of building a new digital bank, and then trying to migrate customers and books of business to that new bank. So, first of all, I’m sort of inferring you’re a bit of a skeptic when it comes to this build and migrate strategy. So is progressive renovation the right approach to technology transformation, or do you see another option?

Leda: There are many options, right? One is Big Bang, the other is refurbish as you go, and the third is build and migrate. We have not seen the migrate part happen yet. Some of the greenfield builds have failed to provide the skills. So, First Direct is a good example, right? Everyone who uses First Direct loves it. Why am I still on the old HSBC systems? I don’t know. But they haven’t migrated. Now, I would suggest that it’s too late now because even though First Direct customers are very happy, the technology is almost 20 years old now. But even with a successful challenger, no migration took place. Then, you have situations like RBS’s Bo where, for whatever reason, they pulled the plug on the effort. Now, a lot has been said, and a lot of criticism has been piled on top of RBS for killing Bo. I actually think that I don’t know enough about the ins and outs of it, but I think if you think something isn’t working, having the courage to say, “Pivot, move” is actually brilliant, is what we should be doing as well. That’s what we said our innovation departments were for. And the fact that quite a lot of the technology is being redeployed elsewhere shows that the experiment was not a failure. But the whole idea of build something at arm’s length and migrate didn’t work there.

Leda: There’s currently Mox in Southeast Asia, which looks to be an immense success — they’re onboarding a new customer every minute. Will Standard Chartered migrate customers on to there? No idea — remains to be seen. So, the build and migrate thing is brilliant as an idea and lower risk. It’s just that the migrate thing never seems to happen. The Big Bang approach doesn’t work, and we’ve seen a lot of very good CTOs stop being very good CTOs — in fact stopping CTOs at all. It’s the fastest way to end your career, right? So what are you left with? You’re left with slow refurbishment. And the thing about slow refurbishment is that it has two massive challenges. One is, it’s a long game, and people lose momentum and focus. It’s a long game. Like, if you start doing that, it’s going to take 10–15 years. And in year three people start going, “Are we still doing this?” And the answer is “Yep. And we’ll be doing it for quite a lot longer.” So people start losing focus, it stops feeling like a top priority, it feels like an endless log in a bottomless pit. The second challenge is that you can’t change — and we touched on it earlier — you can’t change systems without changing the supporting economics and surrounding governance. Which means that if you’re doing the slow refurb process, you’re going to have a schizophrenic organization for quite a long time, where part of your organization will have a different governance model and a different pricing model and that creates immense tensions, both in terms of the operating model viability, but also in terms of humans. Imagine a team that sits in the same office opposite to each other, half of them have transitioned to a new system with new governance with real-time approvals from risk and compliance and a different pricing model. And the guy sitting opposite you has to go to the risk committee. And if they miss their slot, they have to wait for three months. But that’s the reality: if you’re doing a slow migration, you have to change the system, the pricing, and the governance for each part of the bank, and then move on to the next one. And it’s not just the cost of running two parallel infrastructures until you can migrate and switch off. It’s the fact that you’re gonna have to be running two different organizations, from a governance and pricing, etc. perspective.

[00:45:26.24] Ben: One idea that I hear more often is, you know, as you said, I don’t believe there is a silver bullet — but this idea that maybe you can put some sort of orchestration platform in between channels and record-keeping, which enables you to deliver better customer experiences, and sort of buys you time to replace those record-keeping systems, which is where the real complexity and the real legacy lies. So, how do you react to that idea of introducing a new orchestration layer?

Leda: Bring it! Great! But you still need to fix the human governance, which seems to fall off everyone to lift.

[00:46:10.21] Ben: And do you think it’s issues of governance that are ultimately the reason why these greenfield captives don’t become the bank at large?

Leda: That’s a very, very good question, and ‘I am not sure’ is the answer. I would suspect the answer will be different in every captive. And it would depend a lot on whether the captive is expected to run on the existing bank infrastructure — in which case, it’s not just the governance, it’s also the infrastructure — or whether you have your own board and you’re essentially not just a separate entity name, but you’re genuinely a separate entity, in which case your decisions can be different. I think it varies. It could be lack of conviction, it could be the fact that nobody has successfully done it yet. It could be a case of, when is it big enough? Or when do you know? So, Mox is very young, so it’s easy to pick them as an example — Mox is succeeding by every metric right now, but it’s extremely early. Assuming that the plan is if Mox succeeds — and I hope they will, and I think they will — if the plan is, well, we expect that some of the traditional bank customers will choose to go to Mox, great! But if the plan is, we will migrate the customers when Mox has achieved size and scale, I bet you that nobody has specified exact numbers for that. It is so far into the future. But then it becomes a question of, okay, what is big enough? How long do you wait?

[00:47:37.16] Ben: And it’s not obvious that all customers will want that kind of service. And it’s not also obvious, to me, at least, that the regulator will allow it, because what happens to rural areas? What happens to non-digital customers?

Leda: The answer to that will be many-fold. I haven’t seen a regulator yet force banks to have branches. So, that could be a very interesting legal case that says, “If you allow Revolut, and Monzo, and Starling to have a banking charter without the responsibility to maintain branches, why are you putting the onus on me?” It’d be extremely difficult to have rules for one and not for the other, right? So, there is currently no obligation to serve the rural areas, there’s currently no obligation to have branches, there’s currently no obligation to serve the elderly. So you could see challengers that emerge that cater to those communities or you could find that actually, the way things go, they become even further underserved and marginalized. But with no obligation to retain a physical presence and the mounting cost of retaining a physical presence, I am not sure that the considerations you raised would carry the day — valid as they are.

[00:48:52.22] Ben: I guess there’s another reason to move fast, otherwise, you’re left with this sort of rump of hearts of expensive-to-serve customers. And I think it also maybe depends on something you said at the start, which is, whether this ultimately becomes some sort of public service utility, in which case, maybe there does become requirements about serving rural customers and things like that. A slightly different topic. So, I think we’ve been through the hype cycle with everything to do with cryptocurrencies, and digital assets, but it feels like we might be back into some sort of slope of enlightenment. What do you see is the role of digital assets in banking?

Leda: There are three different pieces there, right? And when this whole thing started, we couldn’t imagine them separate to each other. One is digital assets, the second is crypto assets, and the third is distributed architectures. I would say that distributed architectures and what we understood as smart contracts were a revelation, it blew our mind. But there are now ways of doing them that are much kinder to the environment than a traditional blockchain. There are ways of having a distributed architecture that isn’t DLT. There are reasons why you might still choose DLT, but you can have a distributed architecture in immutable records without DLT. So you would need some good reasons to have DLT that would go beyond those basic functionalities that, for a time, we couldn’t fathom outside DLT, but now we can.

Leda: The second thing is digital assets. And I think we were going in that direction anyway but the advent of blockchain and other digital assets forced us to create security of holding and transacting in assets that don’t even have any magic, physical representation. Because we have been dealing in digital assets and digital ledgers for a long time, but the assumption was that there was capital adequacy, that if I make a transfer to you, if the bank has that physical cash or that physical gold somewhere in its coffers, that doesn’t exist anymore. So the transition to regulating and understanding digital assets and creating a certain degree of complexity is there and is now also decoupled from crypto cash and crypto-assets. Which means that crypto has become its own segment, where part of what you’re doing is creating the distributed architecture and crypto and digital assets with the added layer of not having that provenance and ownership — essentially becoming a bearer asset, like money would be in the physical world, but in the digital space. And I think it’s not a space I personally have a massive interest in anymore. That’s not that I don’t find it interesting, is that there are only so many hours in the day. But I do find that for the industry, decoupling those three things has been helpful because then you can have the benefits of the architecture and the benefits with digital asset without getting into the moral and regulatory conversations around the crypto side, unless it’s absolutely what you were trying to achieve.

[00:51:59.13] Ben: Yep. Okay, last question. So, we’ve got this far and we haven’t talked about the technology giants — Google or Apple — moving into banking and finance. What’s the role of those mainly American and Chinese technology giants in banking in Europe?

Leda: I would say that the Chinese giants and the American giants represent a very different type of challenge because the Chinese giants have a very well-developed financial proposition. It’s not just payments, it’s investments — if you look at the two big Chinese entities, they started with payment, sure, but that’s not where they stopped. So, I would say that them coming into Europe presents a very interesting challenge because they’ve worked out how to become financial services provision players and they don’t need to build scale in Europe to become profitable, because they can leverage their scale in Asia. They already have scale. What will be interesting is how the regulator will treat their entry point, whether they will expect a lot of infrastructure separation — in which case they would need to rebuild their support, and their infrastructure in Europe in order to have that scale — or whether they would allow them to cross leverage. But I think it’s a very interesting thing with the Chinese giants in particular, that their regulatory framework has very much allowed those entities to grow because of how the regulatory framework is in China. You don’t have anything of that size in Europe, and that’s not an accident. That’s partly because the regulator is pointing growth in a different direction. From a US perspective, the giants that are being looked at as potentially entering our space are only dabbling in payments. So, they’re looking at extending whatever it is they’re currently doing into the next step, as we were talking about — the embedded infrastructure makes it natural for these entities to offer payment services, and facilitate some of those. There is no indication that the deeper credit lending and investment pieces are being addressed. The only pieces we’ve seen have been through partnership — you know, that short-lived partnership between Amazon and Wells Fargo and then more successful, but equally limited for now partnership between Goldman Sachs and Apple. We’re not seeing an appetite for those guys to become regulated financial services providers the way that Alibaba and Tencent have.

[00:54:33.01] Ben: Where do you think the bigger challenge comes from?

Leda: If you’re talking about the biggest challenge to profitability for banks in Europe, actually, I think it comes from the regulator, who’s increasingly demanding unbundling and transparency and simplicity and pushing for technology transformation without allowing the banks to pass that cost on to their customers. The business models that both of those two geographic units of giants represent would have to be tweaked a little as they enter Europe, but from a bank, multiple payment providers that sit on top of their infrastructure doesn’t provide an existential threat. Neither does a Chinese tourist making all payments through WeChat. It’s what happens about pushing them up and down the value chain, and how they monetize the place where they land, which is why I find the model that Standard Chartered is doing very interesting, because they’ve had to deal with those Chinese giants and have taken, to me, the logical path of, there are certain battles that are not worth fighting, because we weren’t winning them before these guys appeared. Therefore, let’s focus on the things that we’re still needed for, that we do well, that we have scale for, and then we can even still partner with those guys and give them depth where they don’t need to build infrastructure. Because one of the things that both the Chinese and American giants have in common is the fact that they are clear as to what it is they’re for. And what it is they’re for may be multifaceted, because they have many different business lines under their umbrella, but they’re clear as to their purpose, and they don’t carry unnecessary infrastructure if it’s not aligned to their purpose. So I think it’s important for European entities to learn that lesson.

[00:56:18.08] Ben: If an incumbent is clear about what they stand for, and they align around that, and potentially also pursue some sort of ecosystem-based model, then there’s no reason why banks can’t surf this wave of digitization and emerge on the other side with happy customers and profits.

Leda: I mean, I am not going to foretell such a happy ending for anyone because they’re potentially too many banks, and what passes as profitability for the average bank is possibly not to be seen again in the market. But I would say that anyone who refuses to do that will definitely not have a seat at the table. Consumers — and I don’t just mean retail consumers, I mean, customers across all value chains — and regulators are much more demanding, and rightly so, in terms of service provision, focus, transparency, and pricing. And therefore, unless you really know what it is you provide, what it is you’re for, you can become overwhelmed by options. Think about it, you can revamp your lending infrastructure in 10 different ways. If you can’t decide whether lending is important to you, how will you know what the best way of revamping is?

Ben: Thank you very much, indeed, for your time. That was great.

Leda: Absolute pleasure. Thank you so much!

Post-pandemic Wealth Management

4×4 Virtual Salon featuring: Keith MacDonald (Partner, EY); Christine Schmid (Head Strategy, additiv); James Dunne (MD, Santander UK); Michael O’Sullivan (ex-CIO Credit Suisse)

Lively panel discussion featuring:

We discuss:

  • Are service offerings going to permanently change post pandemic?
  • Will customer risk and investment preferences change post-pandemic?
  • How, if it at all, does the competitive environment change post-pandemic?
  • Will sourcing and operating models change post-pandemic?