Dan Knight, Chief Technology Officer, Global Financial Services, Hitachi Vantara:
I'm great, thanks.
Jeremy:
Well, as well as you, you've brought along some amazing people. So let's meet them and let's get in this conversation.
Dan:
So, I brought along actually a partner of ours … we're part of a pioneering proposition around helping banks understand their liquidity from an intraday perspective. And with me, I have Bruce who's a CEO. Bruce, why don't you introduce yourself?
Bruce Hrovat, CEO of Intraday Insights:
Hi Dan. I'm Bruce Hrovat, CEO of Intraday Insights
Dan:
And I also have Adam, who's their CTO. Adam, why don't you introduce yourself?
Adam Sullivan, CTO, Intraday Insights:
Adam Sullivan, CTO, Intraday Insights.
Dan:
Awesome. Since what we've been doing is we've been realizing that when we look at the banking space, there is a lot of waste. And I don't know about you, but I hate having waste, especially when it comes to my money. Right? And so what we did is we took the base platforms that we've had so much success with the banks on and we've realized that there is an entire ecosystem of partners that we've been developing and we are having tremendous success with. I want to introduce this technology set to you guys because I'm so excited about it, because of what it can actually do to a bank and how they service their corporate clients. So Bruce, maybe you could share a little bit about what the total solution is. Why do we care about this? What do you see in store for these banks that try this out?
Bruce:
Thanks, Dan. Really what happens is that the whole process today works end of day, globally. And we're moving with technology and regulations. We're moving to an intraday space. The technology allows for it, the regulators want it to. Systemic risk is needed to be reduced. So now what happens is that because we have the regulations, because we have the technology, we're now able to take in things like real-time payments and are now able to collapse the time between an inflow and an outflow.
Dan:
Oh, real-time payments. That's a big topic these days. It is huge. Money has been spent on this area. Huge money has been spent on the idea, right? I think there's real-time payments today, right? Because I use a Venmo or I use PayPal, and instantly my son has money. Right? But is that really instant?
Bruce:
So in some parts of the world, yes it is. And the UK for example, real-time payments have been in existence for 10 years. Even though in the United States we're far behind. But recently the fed has announced a program that in 2023 or 2024, real-time payments will be in the United States. But in the UK, for example, if you make a payment to your son's college, that payment is there within five seconds and it's settled, it's reconciled, it's finished.
Dan:
And here in the States, I write the check and then hope I get the money in the place before it needs to get cashed. Right?
Jeremy:
Just under the wire. Right.
Dan:
So we've created a huge infrastructure for real-time payments, but the banks are really coming on board just now.
Bruce:
Yes. Especially the United States, infrastructure and banks. So what happens is that we have this kind of a lag timing difference between the inflow and the outflow. It's estimated that that rent runs around, you know, 1 to 2% global GDP. GDP globally is somewhere around $90 trillion. So let's say round numbers, $1 to $2 trillion of kind of lazy cash that's not really earning anything intraday.
Dan:
Well, not earning in a bank is bad. Right?
Jeremy:
That's some fundamentals there for the financially naive. Yes.
Adam:
Well, you know, they are called buffers and each of these banks … I mean, you talked about big money earlier. We're talking about trillions of dollars in the system worldwide that is just held in reserve in case a lot of payments come through at once. Right? And so what all of these banks are regulated to is kind of the high water mark and the low water mark of the transactions that they're processing.
Dan:
Right.
Adam:
Now if you take a technology like ours, you can time payments and time activity so that the high watermark and the low watermark are actually flattened. That way you don't have to keep as much of a buffer. Then that lazy cash is no longer lazy.
Dan:
So make it real for me. Let's say that a bank has some dollar figure, I don't know what normal is. Maybe one of you can share what's normal for a mid-sized bank to have in terms of lazy cash. Is it $1 billion? What is it?
Bruce:
So for the systemic banks, United States, they typically would have a minimum of about a $25 billion buffer.
Dan:
Okay. Okay. So that's … partially it's needed, right?
Bruce:
Absolutely.
Dan:
But there's also some waste in there. What's the waste percentage?
Bruce:
Usually it's somewhere … our experiences are around 20% of excess buffer. Some of that is strategic for the bank, but some of it is just because they're not able to determine at any point in time specifically on what their inflows and outflows will be.
Dan:
Okay. So I heard some different terms. I heard lazy cash, I heard buffer, I heard waste. Make it real for me. What does it cost the bank to have that money sitting there? Is it a million bucks? Is it $10? Is it $10 million? Make it real.
Bruce:
So for any bank, let's just say that their cost of capital, their debt plus their equity, let's just say round numbers, it's 10% and make this easy. So for every billion dollars you take out of circulation and put in a buffer, you have $100 million of some type of opportunity costs.
Dan:
Right. That means less mortgages. That means less ability to be dynamic in their offering a pricing.
Bruce:
Yes, and less earning power. And so some of what you see today in terms of the net interest margin of banks is that we tend to be at the lower end of their historical norms.
Dan:
Wow.
Bruce:
You know, there are around 340 or so basis points: high marks being somewhere around 500 and maybe the lowest mark they've been in the last, let's say 10 or 15 years is about 300 basis points.
Dan:
Okay 3 to 5%. So we're talking about tens of millions of dollars in an average bank. Okay. So let's make it real from here. Now we realize that this is worth tens of millions of dollars to these banks.
Bruce:
Yes.
Dan:
Okay. So it's, it's, it's worthwhile to have a conversation about it, you know.
Jeremy:
I am interested.
Dan:
So what we've done is, we've already been working with this and … Adam, maybe you could shed some light on it. Can you talk to me about the high, low and what are the outcomes of the platform of Intraday and Hitachi Vantara working together? What does a bank get out of it? Yes, we're talking money, but can you expand on that?
Adam:
Well, think of a console that the treasurer's office can use where the treasurer can see in real time money coming in and money going out against a forecast, and that that is being continually updated. And then the treasurer is getting cued, as we're continually scrubbing data and we're pulling out opportunities for them to re-time payments.
Dan:
Okay. So what is, if I could re-time all these things more accurately or closer to the numbers, where the inflow and the outflow begin to match. Is there some magic?
Adam:
A magic that comes, is that they get to see when they execute these transactions, they get to actually see the dollar amount that they will reduce costs for that day.
Dan:
Okay.
Adam:
So we're looking at, you know, for, for a typical bank, a systemic bank, we're looking at tens of thousands of dollars a day.
Dan:
Wow.
Adam:
And these are just, you know, we're measuring it: two and a half percent, let's say in 15 minute buckets for a bank that's seen a $3 billion draw down, which is not uncommon. You know, during the day that they've got $20 -- $25 billion in buffer. That's not a big impact. But even then, if we can re-orchestrate about 20 or 30 transactions, and oddly enough, it's the big ones that make the biggest difference. But if we can orchestrate those transactions, catch them, they're the needles in the haystack. Fortunately with Hitachi Vantara and Pentaho, it's really good at managing haystacks, right? We come in, we can find what we need to find, and we can recommend those to the treasurer. The treasurer can act on them and save tens of thousands of dollars every day. And it's a lazy way. I mean, I don't want to say it's lazy, but it's where we're getting lazy cash out of the system and it doesn't take a whole lot of effort to do.
Dan:
Okay. So let's say that we started today, you know, we drop it in, we do a little hook-up, we grab some data, we pump it through the system. How long does it take to actually see value? Is this years? How long does this take?
Adam:
No. Well, what we try to do is pull up enough data from the past so that we can get a decent forecast. So that we can see kind of the fingerprint of what today is going to look like. And then as we see the transactions flow in, we get higher confidence in that forecast and the transactions start popping up as to what they could do. So within 30 days.
Dan:
30 days. Nothing happens in a bank in 30 days.
Adam:
Yeah. But within 30 days you will have known whether these transaction recommendations are worthwhile or not.
Bruce:
But we should say, Dan, that you know, the magic here in some ways is this kind of center console that Adam was talking about called Pentaho, right? So it acts as an air traffic controller. It pulls in, you know, basically the information from a very large infrastructure from the large banks. And because that data is accessible, now it makes solutions like ours really easy to implement. So the 30 days is not really a stretch.
Dan:
So, is this for banks just here in the States? Is this for banks, you know…
Bruce:
Globally.
Dan:
Okay, great. So really what we're talking about is: Let's have some conversations. Let's actually set up some proofs of concept. Let's look at what could you actually save and what could you do with that kind of funds? Is that right?
Adam:
Absolutely.
Jeremy:
If nothing else, you'd turn lazy money into fit money. I mean it seems like, and you know, it was any way you want to make that happen.
Adam:
Yeah. And, and it's interesting because we as members of the public, we're constantly talking about fed fund rates and concerned about liquidity in the system. 2008 made everybody sensitive to that. What we're actually doing is making the system have a lot more liquidity because this money that's been sidelined for "just in case" does not need to be as big as it is. And if people can orchestrate what they're doing with computers, which is what we've been doing for what the last 40 years, right?
Dan:
That's probably machine learning.
Adam:
Let's put it to work.
Adam:
Bruce.
Adam:
Let's instead of just working on: Hey, let's make this manufacturing line more efficient, or let's make this a process of making a hamburger more efficient. Why not make how we handle cash more efficient? And just by doing that, we're liberating a bunch of cash in the economy that can be put to work.
Dan:
Love it. Love it. Yeah.
Jeremy:
That sounds like a really fundamental shift in how that would all work and something we should definitely have some coffees and lunches about. Dan. Bruce, Adam, thanks so much. You guys are amazing.
Thank you. We
will contact you shortly.
