[00:00:00] Talk about what the platform is a bit later on. But my background is, I've been in FinTech for 20 odd years. Started off in trading investment management, started my first FinTech, back in 2005 that was called vancor, Selling it to brokers and as well as lots of other traders and people in the market.
Moved on to the technology side with, ipushpull with David Jones and Dan Eccleston, the other co-founders of the company who have been doing this for the last kinda seven, eight years or so, selling into financial institutions. Um, and I've also just joined, the advisory board of the Center of FinTech at the University of East London.
So we're very deep in the FinTech world. Uh, so that's me. I'll be covering the first half of this webinar. And Neil, do you want introduce yourself? Yep. Hi, I'm Neil Weatherall. So I've had two years of IBU Pull. Working as head of technical sales. Prior to that I spent 16 years on the sales [00:01:00] side, predominantly trading sterling inflation in cash and derivatives.
So I like to think I've got a decent understanding of what trading desks do and the role that brokers play. Today's webinar is gonna be about scaling client services to optimize OTC market data and quote delivery. But before we jump into that, we're gonna, well, I'm gonna cover kind of the latest technology trends that we are seeing in kinda the broken world and the financial markets more generally.
Neil's gonna jump into some use cases and case studies. We'll cover questions at the end, but don't let that stop you from asking questions as, as we go through. We've got quite a, you know, good, good size group here where we can, um, we can field questions as, and when they're coming in. Keep yourself on mute, and do keep your phone handy as well for, we've got, there's a couple of QR codes on some of the slides for some other stuff to look at.
I will kind of talk through some of the slides for the people that are dialing in as well. So, so, um, [00:02:00] okay, we'll, we'll kick off. I think we've got a good, good group here now, so, Okay. Starting off. So ipushpull. Who, who are we? So yeah, we, we are founded in 2013 originally, and that was for the use case of aggregating real time risk data.
That's kind of where we started. And over that time we've evolved into a real time data sharing workflow platform. So through our platform and, and customers that are using us for lots of kind of omnichannel workflows across lots of different, um, parts of the financial market life cycle, we're actually probably in about 70 financial institutions.
Lots of brokers, banks, asset managers and so on. There's a couple of logos on there, the type of clients that we have. So I'm going to give you a bit of a whistle stop tour of some of the latest trends in technology from. Kind of unique perspective. And when I say unique is because we, we kind of work on lots of different sides of the fence.
Sometimes we're working for the buy side, sometimes we're working with brokers, [00:03:00] sometimes it's with banks cause we're an independent, you know, software vendor. So we do see lots of different perspectives as well as having kind of our finger on the pulse of, of latest technologies. So it's gonna be quite fast and furious is my, my first half cuz there's quite a lot of ground to cover.
But I'm hoping that I can at least, um, you know, you, you can come away with at least learning one thing from, from today's session, right? So it's kick off. Let's firstly talk about the cloud, right? The cloud is the biggest enabler of technological change in our market. So it means firms can be fast to market, it's more efficient and it means it's very quick and easy to get stuff done.
The cloud is so front and center that the FCA is spending 120 million pounds over the next two years to maximize its move to the cloud. And the FCA CEO has the goal of becoming a data and digital first regulator. So that's really important because they've now opened up the [00:04:00] floodgates to using cloud technologies, given it their stamp of approval.
So this acceptance and the adoption of the cloud has really helped kind of catapult a lot of companies like ourselves, which are FinTech into this arena. So looking at the cloud providers like aws, gcp, and Azure, what we're seeing now is lots of mission critical workloads. And what that means is kind of core parts of the business, which makes revenue and delivers products and services to, to their clients or your clients.
A lot of that is now moving to the cloud and looking at, you know, the big guns. You know, these big cloud providers are making ridiculous revenues and they've, they're making blockbuster revenues in the last quarter as well as all through the pandemic. So Microsoft Cloud, their revenue is 23 billion. It's up 32% in one quarter, aws, so that's, that's uh, Amazon Web services part of Amazon.
You know, they're posted their highest growth rate for the fourth straight quarter. Their [00:05:00] revenue's up 37%. Google Cloud, they're actually the fastest growing their revenue. 44% just in the last water. So think about that. And we're still at the cusp of using cloud because not many people are really using it, you know, for everything.
But we're seeing that shift now. So the other thing with cloud, you know, initially it was seen for compute and storage and databases and so on. Whereas now there's data and analytics, there's ai, there's machine learning, all of these tools that come out of the box, that's more than hosting. Looking at FinTech, the cloud has enabled this massive rise.
In FinTech. There's been a convergence of lots of different things, you know, funding environments. And in the UK there's lots of investment schemes that have helped, you know, create this kind of bubble of, of activity in fintechs. But the cloud has been probably the core part of that movement. And fintech's big advantage is speed, and that's where cloud has a big part to play in.
So look at what it's [00:06:00] enabled. So on this. On this, uh, this, this grid. You can see on this picture you call map even, um, you can see hundreds of capital market fintechs, if anyone's spotted. I push pull yet we are on there. Um, I'll help you. Um, you can see us just down there, well actually put us in the wrong category,
Um, but that's, that's CB Insights, which is a good research kind of platform to look at. Um, so that's, I push pull. Um, but this capital markets ecosystem, what it's doing is disrupting the incumbents, right? The incumbents that you, that you know and maybe love or hate, um, you know, Bloomberg, Refin, you know, s and p market and so on.
Now put it into perspective. FinTech is the biggest growth area in the next decade in financial services. Financial services grow a very small amount every year, FinTech, and now crypto as well is growing rapidly. So there's a lot of investment coming to this, and all of this is disrupting a lot of traditional ways of [00:07:00] doing things.
And Paul, listen to perspective again, drill down even more to that capital markets FinTech world. You can see the valuations of fintechs. Something like True Mid, which is a corporate bond trading platform, or Symphony, The chat platform that's more, you look at Symphony, they're worth more than the two biggest in dealer brokers in the world, which is crazy, right?
Um, and that's cuz they're technology companies. Um, you get big multiples in technology companies, whereas traditional breaking businesses, you know, if, if it's very, if it's all kind of voice driven, they don't see those multiples. So that's why you see this big investment back into technology. Cause it's more scalable, more efficient, and ultimately more value makes you more valuable as a company.
So looking at voice and chat, you know, that's obviously massive in the world of pro. You know, the people on the desk and fintechs like Symphony have really shaken up the market around those workflows, and it's very interesting to see. Symphony recently bought Cloud nine, which is a soft turret or online dealer board.
So got Brad [00:08:00] Levy, he's the new Symphony ceo. Um, he was formally, um, he was head of strategic investments at Goldman's, used to work at Market and Lay wants to unite voice-based trading with natural language processing. So you can kind of see where, where the trend of the industry is going in this regard.
Now talking of chat, you know, you're all familiar with the chats that you use every single day. Probably a lot of you be using things like IB Chat or Bloomberg Chat. That's the biggest player, right? Bloomberg, even Bloomberg is now opening up their chats and their APIs and bringing in bots into their platform and so on because they have seen the success that Symphony has had with bots.
You, Refinitiv Ice you're probably familiar with, and chat platforms. They also have bots in there as well. And then Microsoft Teams is one to watch. You know, they've been stealthily growing in the market, well, sorry, in our market. Um, and there's even kind of early beater versions of Intercompany Chat. You know, a lot of you might use teams already for doing your internal chats and your [00:09:00] internal voice calls.
You can do that, you know, you'd be able to do that externally and talk to people externally. You know, Symphony, Affinitive, Bloomberg, they've all got beater integrations with teams as well. So Teams has got 300 million users globally. They had 10 million before Covid. Bloomberg's got 300,000 users. So again, things that you should be aware of.
Then there's slack, you know, that's very big in developer communities and therefore we've, we've seen it being used in lots of the high frequency trading communities as well. Now if you, if you've got things like chat, you can then start using things like bots. When I say bots, I'm talking specifically about chat bots.
So bots inside chat applications. So this area has seen massive growth initially led by all the big banks who are looking for more efficiency. But since then, we've seen the usage of bots grow far and wide. So you can use chat bots in financial markets for doing simple tasks like fetching data as you can see on [00:10:00] the screen through to more complex workflows such as mapping, fixed messages into chat, or streamlining complex pre-trade workflows.
If you wanna see some examples of that, just take a picture of that QR codes. Um, and you can see some videos. We'll share these slides if you've not seen 'em already.
Now I push pull, you know, our platform, we even have our own chatbot framework. So we've got our own proprietary chatbot framework, which means you can build a bot through configuration. You don't need to use any codes. And then you can roll that out and it will work in a variety of different chat platforms that you integrate into.
So our chat platform is already being used in, you know, number of brokers, lots of banks, market makers, buy sides to lots of the industry. Again, this is another kind of key, key focus that you need to look out for. Um, now with bots in place, that allows automation to become pretty straightforward, um, because you can standardize, you know, workflow, standardized data.
You know, one of our IDB [00:11:00] clients is already doing fix messages, and then we map and convert those into chat messages. And then even doing like botto bot workflows with like, there's, there's a major tier one US bank already doing that with. So this makes, you know, if you're using then bots automation, this makes the broker more efficient so that they can focus on those high value tasks.
And it gives the client the workflow in the application that the client wants, in the format that they want. And why does the client want it in this in a, in a particular format that they're demanding? That's because they have their own automation and streamlining and so on that they can feed in on their side of the fence so they can save time and money as well.
So all of these technologies help you save money, help your clients save money, and make everything more efficient. Right. Let's move on to data. You know, that's kind of what we are. We're data sharing and workflow. We do all the bots, but we also do data. That's the core of everything really. And what we're seeing is obviously data sharing hasn't really changed in decades.
When you are particularly in the OTC kind of derivatives [00:12:00] market, there's a lot of stuff done manually. There is still voice and phones, but lots of that is migrated into chat. But chat is still people typing in the chat, which is crazy. You know, people manually typing things, copy pasting things around, emailing spreadsheets around.
So all of this is really slow. It's really inefficient. It's very manual. There's lots of operational risk. I'm sure you, I'm sure you've all seen the news this week where Citi Group made, made a massive fat finger, uh, and, and like they, they wiped off loads of money off the stock markets. I think the Swedish stock exchange, the omx, was down 8% of at one point, and that was literally just a fat finger.
But nonetheless, data is the lifeblood of this intra of, of this industry. But on the topic of manual workflow, let's have a quick poll around Excel. Give me a chance to catch my breath as well. So, so if everyone could just jump into the chat that you can see on the right hand side, um, of, of your screen.
Um, in your business, is Excel being used for any mission critical [00:13:00] functions? So in, in the chat, if you just type yes, no, or sorry, if you type one for yes, two for no, and three for don't. No. So if any, if, if you can all just jump into the chat and just, and just check in there, you know, if you, if Excel is being used for any mission critical functions.
Got two responses so far. Can I think you do a little bit better than that? Um, okay, so we've seen lots of yeses, so. The chat, um, icon's on the top right, if you can't see it on the top right of the screen. All yeses so far. Um, so lots of yeses where people are using excel for mission critical functions. So that's really interesting, right?
Cuz Excel is what you call, um, shadow euc. It's kind of things that's flying under the radar. It's not productized, but as you can see with these answers coming in, you know, Excel was being used for lots of mission critical functions. So thanks everyone that responded. You know, the more the merrier here.
It's good to get lots of responses from everybody. Um, okay, so moving on. Right. So, um, moving back onto data, [00:14:00] right? So, so data is often called like the new oil. Um, but we see data more as like building blocks or like Lego, you know, you can use tools like data mapping, validation, and enrichment. You can turn unstructured data that could be sitting in Excel, could be sitting in a, you know, another application somewhere else.
We can turn that unstructured data sitting in lots of different places. Connect to it, put a structure around it, make it real time, connect it anywhere you want. This, this is kind of the future. This is the way, you know, lots of firms are beginning to do things now. Data can be distributed, it can be pushed, or it can be pulled.
You know, client can be fetching things on demand. You know, we can do that from connectivity into any application or any system. And again, this is all without using any code. In terms of what we do, I push pull so we can connect to your data. So this could be some AEs that is sitting in in your email, right?
This could be some data sync in a spreadsheet. [00:15:00] This could be something that someone's manually typing into a chat. We can connect into that. We can connect if you've already got it in a database, fantastic KDB database, real time ticking streaming database, and connect into that as well. What we can then allow you to do is distribute that out to your clients into whatever application they want.
So typically people for our, our clients are using it to distribute out to client side Excel as an API into chat, or even into other systems.
Right With data, you've got so much data that's been generated everywhere and constantly across financial markets. If you've got a way to track it and record it. Whereas if it's coming through something like I push pull, we can do that. We can then easily use that data and then plug it into off the shelf applications and machine learnings.
There's some really cool technology out there, which is literally off the shelf. You're just gonna plug your data in. Um, so trading analytics, you know, you can use all these best of breed of solutions that are out there like Snowflake and [00:16:00] Google. Google, big query, other technologies like that. So you can utilize the clever machine learning that Google's been making lots of money out of for the last few decades, and you can use that technology to provide insights on your data.
So combining these type of tools with something like bots means that brokers can start building, you know, new pre-trade data sets. You can start collating information and data around your negotiation from different sources. You can feed that into better analytics and insight, and then you can use that to create a virtuous circle where it's gonna help you be a better broker, or you're gonna use it then to, to wrap it up and monetize it and sell it to someone else.
All of these things are now possible because of all these technologies. So my last section here is blockchain, which is worth a mention because blockchain's actually been around for 13, one and a half years now. You know, it, his first, um, the first white paper came around in October, 2008. And why is it October, 2008?
Important? Cause the month before is when Lehman's went bust, [00:17:00] right? There's, there's this, there's a direct kind of, from the 2008 crash, that's where blockchain come, came from directly out of those kind of ashes. Now, Sotoshi Nakamoto, you know, he wrote this seminal white paper about Bitcoin. The QR code there is, is, is for the, for the white paper.
Read it. If you haven't read it, read it yet, read it. It's eight pages long. It's very short, but it's worth a read so you can understand, you know, people. Crypto and crazy things, right? Read the paper. It's easy. Eight pages, but, but you'll learn something from reading it. Also, if you like it, read the Ethereum white paper as well.
That actually dives a little bit more into, uh, financial use cases. They talk about derivatives, talk about GA gaming, uh, sorry, talk about gambling. All this type of stuff worth a read. So we've seen a few full starts with blockchain in the past, probably five years or so, but now blockchain and crypto's coming into its own.
Probably everyone knows someone that's quit a bank and worked at a crypto firm, right? You know, it's happening right now. So, Nothing puts this into [00:18:00] perspective more than money revenue, right? This is revenue full year, 2021 revenue for crypto exchanges and traditional exchanges. Look how much money they're making.
Our Binance is making nearly $15 billion revenue for the year. Compare that to the cme. 5 billion. You know, it's three. It's three x and what they have. So crypto exchanges are big and you know, you need to watch them. You need to look at what they're doing. Now we want real use cases though, right? You know, it's all in a good crypto trading.
But what about us through, in financial markets, we do this stuff. So what are the real use cases? So the beginning to emerge, it's worth keeping your own defi. So that's decentralized Finance. Defi as a whole collectively is now worth $113 billion. So on its own, it's worth a lot. But in the world of trading, it's worth looking at protocols like the PI network, that's P Y T H, which is something that I push, pull is connecting into.
So, On the left is Sam Bankman Freed. You know, some of [00:19:00] you may have heard of him, but a lot of you may not have. He's worth 22 and a half billion dollars. He's made all his money in the last three years and he's the youngest billionaire in the world. He's made it all through crypto. What did you do for crypto?
He worked for Jane Street. He was a high frequency trader for Jane Street. He saw the arbitrage opportunities available in crypto and he moved straight away from traditional markets into crypto. He was making $20 million a day for quite a while. He's the youngest billionaire in the world. Anyway, so he got together with his old buddies at the market makers, you know, Jane Streets and jump trading and people like that.
And they created the PI network. So what they're doing is providing free market data cuz these are all market makers, right? They're quoting prices, they're creating free market data. Based on the Salina blockchain, which is very fast and cheap, unlike Ethereum. Uh, and the market makers then earn tokens when they're contributing prices.
And what that means that all these different contributors are actually owning the network. So that means no more centralized, you know, market data [00:20:00] feeds that you have to pay for. Instead, you can get it for free via something like the PI network. So it means the contributors own the network and this is this big web three shift that you have in thinking.
Right? Lastly, NFTs. I love NFTs, right? I'm big in them, Um, but that's why I'm mentioning them. But, Kind of fun aside. Data NFTs is something that I push bullets looking at, right? And data NFTs is something that you should be thinking about as well. So forget about, just completely forget about board apes and digital art, all of that.
That's just the Wild West Cowboy Casino. You know, if you can do that, fantastic. And made lots of money overnight, but that was last year and you could have done this year, you're not gonna do it. Now. What you wanna be thinking about is how can you use NFTs or non fungible tokens and apply that to use cases in financial markets, right?
So check out protocols, like there's one called the Ocean Protocol. Yeah. So check out that Ocean Protocol. So they're creating NFTs based on data, and then they get data and then they provide access [00:21:00] rights to that data and they fractionalize ownership through tokens. So think about how can you use that type of model that'll apply it to OTC market data.
If I've got some access or I've got some prices, how can I, how can I create ownership of that and fractionalize it? So that's what you wanna be thinking about with NFTs. Forget about digital art, right? That's my bit done. Um, before I hand over to Neil though, we'd just like to get, um, your thoughts on where you see these trends yourself, and what is your company investing in.
Um, so if you can all jump into the chat. Again, it's on the right hand side, the little chat icon at the top. Just jump in there and just type out what number you as a company are investing in. So number one is cloud. Number two is voice and nlp. Number three is chat bots. Number four, automation and just type the number into the chat.
Come on, here's, there's a bunch of you there. Come on, let's, let's, let's get, let's get this in, let's get this bit more involved with you people as well. You, [00:22:00] you know, sharing what you are doing. You know, it's good for us to understand what you're doing cuz then we can focus the next staff of the session as well.
So it's relevant to you. So just put the number of, of what you're interested into the chat. Yeah, so number four is automation, five is data distribution, six is data analytics, seven is blockchain, and eight is all the above. So yeah, any, any, uh, anything is great. Okay, so we're seeing a bit of a mix here.
Couple of people, all the above. Well that's interesting. You must have lots of money to spend, um, one, five and six cloud data and data analytics. Yeah, that's a good one there. El McCarthy. Uh, I think that's probably more of a, um, the, a good, easy first on ramp onto all this new technology. Um, fantastic. Okay, well, we'll move on.
Thank you for everyone who answered there. So I'm just going to, I'm gonna kill the share. Without leaving the room. There we go. Poor stop sharing. And I'm gonna hand over to Neil. Give me one second. Where are you, Neil? Where you are.
Make presenter. [00:23:00] Yes. There we go. You got that, Neil? Yep. Cool. Okay, well go. Great. Thanks Matt. Um, so I'm gonna talk about what we see in practice and some of the solutions we've seen in the market. Um, so as I said earlier, most of my career I spent on the sales side, on the trading desk. Um, so I'm coming up the, from the point of view of traders and brokers, you know, what data were we interested in?
Um, so first I wanna talk about data. When, when I talk about that, what, what am I actually referring to? When this case is, you know, it's prices, so this is bond or swap curve mids, there's quotes. So that's your bids and offers, acts as iis, and these can be live or very recent or historic. I guess I'd classify these as something you were, you know what it means and you can trade it, but you also have data that you might just want to consume.
For example, avol surface, which he use as an input to a pricing [00:24:00] model. There's then data on actual trades, you know, what's trading the price, the size, and then you've got the trade details themselves. So such as confirms and any sort of s STP messaging. So in terms of who uses this data, well, I'm thinking about traders, I'm thinking about brokers.
But increasingly I'm thinking about other users within the bank or externally who are using the data for compliance, best execution, tca, you know, and many other uses. And you know, what is the aim of this data? What you know, why should you be collecting it and using it and analyzing it. Really, you know, you're gonna use this data to trade.
You wanna inform your clients, You're trying to build relationships, find ideas. You wanna help your clients with their data needs. You wanna fit in with the new ways of working on your side and on your client's side. And obviously we've all got to deal with regulatory changes. I guess the point here is you need to make the best use of the data you've got available.
A part of that is the ability [00:25:00] to compile it, store it, and to validate your data. And then you've also got to analyze it and share it where it needed. For us. This all comes down to the right data, the right time, and the right place. So when I talk through several use cases and broadly, I think these fall into three different areas.
There's always overlap, but obviously this is just a helpful way of categorizing them. So in terms of what you might be looking to do and also how this can change over time, I. So the first up is just data distribution, and this is something we're being heavily used for by TPI cap. So the problem, um, we started with options, full surfaces and capital premiums.
So these are large grids of data and there are lots of them. Now they're in-house Excel adin that they're given out to clients was legacy technology. It was hard to support. It was out step with their other [00:26:00] in-house technology, no access control, no usage tracking or audit the solution. A new white labeled Excel ADIN backed borrow the I push pull platform on the cloud.
Now this has the benefit of only needing one integration between IPE pull and TPI cap. And that's what I'm showing here. So this graphic on the screen is just showing you that single connection into the IPE pull service. We then serve the I TPI cap clients into their application of choice. That's very important to note here that IBU pull is deployed within the sort of TPI TPI cap infrastructure and with white labeling, their clients may not even know that we are involved.
So what do they gain by using push pull? Well, they've got an extensible and a scalable service. Um, they have one platform that allows 'em to offer clients Excel API [00:27:00] chat onto desktop and mobile. There's an integration into their in-house SSO and permission controls. And with the cloud, you've got future proofing and a very agile death path.
What are the other benefits? Whereas quick delivery and a very low project risk, a reduced cost of ownership and off off the shelf compliance with data security and audit needs. What do their clients get out of this? They get a customized service. They get very quick and easy access. For example, one client coded to our rest API in under an hour, possibly even under half an hour.
Brokers can now track the usage and they can provide a highly tailored, higher level of service, and it's obviously led to increased desk revenues and efficiencies.
Next up, I wanna talk about data transformation. Now there was a twofold problem here. [00:28:00] Our customers clients were using shared worksheets to publish and consume data, but they also needed to have shared model parameters between the users on the desk, and all the solutions they'd looked at so far were implemented were pretty cumbersome and prone to error.
They also had issues with the data being contributed to them. Everyone was sending in the same data, but in multiple different formats. So for example, sorting and organizing by expiry in different ways. People were perhaps using spreads rather than outrights. Differences in product names, any changes in the format, the template of the data that was being sent in could take days to update and would often need client involvement to work out what was going on and how to change.
So the solution here, again, using the I Pull platform, but specifically with our transformation module. So we set up some client specific mappings that were applied to the data that was being [00:29:00] shared via New X Adam. So what happens here, The data is validated and monitored on the way in. It's mapped as needed, but more importantly, any errors are flagged up immediately.
The platform suggests a fix, and then that can be actioned in minutes without any client involvement. So there's no more delays of days or weeks has happened before. They found that buying in a solution allowed a very quick rollout. There's an iterative development path for new products or with new clients, and they saw a high roi.
Now we can transform more than just goods of data. For example, you can expand this to chats syntax. It's almost like translating different languages between the two different people. Or it could be as simple as performing calculations in our platform to reduce the processing needed on your side of things.
So the next example is where we [00:30:00] take a grid of data and we use our no code workflow tools to build an application. So we identified broken in sales desk for having issues with collecting and sharing pricing information. Now we put this down to a number of different factors. There seems to be a number of, uh, there's been an increase in the number of sources that brokers are having to monitor.
You've got your phone, you've got multiple chat platforms, email, perhaps even some automated order entry that can now be a split across physical locations due to hybrid working or perhaps just operating in multiple different locations. And then there's the old danger of chat rooms just being proliferated with farty many, many messages to be useful or worthwhile to anyone.
So the idea here is to ensure everyone has access to the same up to date information no matter what platform or location, be it at your desk or in the golf course in some cases. So the [00:31:00] solution really is a very simple level, a digital whiteboard, but with a host of extra functionality. So you can view or update this from any number of desktop or mobile apps, and it can alert you when there's something you want to know about happens.
You can use it internally or externally and give customized views to each individual client. I'm gonna show a short video now as to how this actually works and what it looks like on screen.
So we call this live quote views and we basically see it a way of a, a way of bringing the pricing information together when you wanna put new information into the platform. This can be added from Excel or from chat. Uh, you can use the classic popup forms with dropdowns, or you can copy and paste in syntax as well.
It gets validated. Then the underlying platform updates the view so that everybody sees [00:32:00] what's been go, what's gone into the platform. Not only can you see it on screen, it can also alert you.
You can then share this internally, or you can share externally with your clients to the web excel into fixed messaging into api. The important point here is that you configure the platform to each use case using the Lego building blocks that Matt talked about before. So what are the benefits where you have the centralized golden source of this data?
You can help automate, automate away some of the manual repetitive tasks to increase your efficiency and your reach. The more you use it, the more data you build up that helps you analyze what's going on. And obviously the goal here is just to re make the most of every single trading opportunity and to increase revenue.
Now the final use case rolls around fixed messaging. Uh, fixed messaging is obviously an industry standard, but we tend to find that it's expensive to [00:33:00] set up, it's expensive to run, requires technical expertise, and it's just outta reach for a lot of firms. What do we see people looking for? Well, we use, I push pull to deal with data in and data out and transforming the data fix is just another format we can convert, fix into our standard grids of data.
For example, just for displaying on the web or in Excel. What I'm showing now is an example on screen of fixed messages being fired into a chat. So you can see the top half of the screen. This very long string, it's very hard to decode, doesn't really make any sense, but our chatbot reads them. It decodes that message.
It applies some validation and a data type, and it then makes this available into, within the IBU platform. So here you can see the message coming in and then it updates in our web app. That could also be pushing through to Excel ensemble, ka, other API access. [00:34:00] And the example here is bond access. So we've gone from a very long meaningless string into something you can see in a simple formatted onscreen grid.
So what do we think this does? But it allows you, in this example, to use fix without being an expert and without having your own service, you can tie fix into your existing workflows. Also, if you provide this to your clients, you're saving them money and you're also opening up new technology to them.
This might mean you can attract new clients, or you might just get more use and more, more quotes and more interaction per client because you're making it easy for them and you're giving them new ways to communicate with you. I realize we've, we've, um, gone through this pretty quickly, obviously. Any more information, please ask on the chat or get in contact with us or, you know, at another time.
Do you wanna just move it ahead to one slide, Neil? [00:35:00] Um, so, so we are kind of wrapped up the, the kinda the first part of the presentation there. We can move to any questions Now, obviously the title of this, um, webinar is Scaling Client Services to Optimize OTC Data. And quotes. So some of the technologies that we've been talking about is ways that we can connect into your data and then you can make that data available out to clients.
That's kinda one piece. And then the other piece is actually building them workflows around that data. So typically, you know, we've seen people on broken desks where they'll spend, you know, 80% of their time dealing with 20% of their clients, but those 20% of their clients is 80% of their revenue. So what then happens to the long tail of all the small clients that you don't talk to all the time?
You know, having tools like the live quote view, for example, that Neil just pull up on the screen. Um, it gives you ability then to present prices out to, to people anywhere. I could be on a mobile, it could be on a, on a web browser, it could be into their spreadsheet, [00:36:00] but again, just gives you the ability to, to service the long tail on one side and on the other side gives you the ability to.
Create, um, uh, so provide your, your prices and your quotes out to clients in the applications they want. So on the other kind of end of the spectrum, doing things like, you know, bots to bot workflows and so on. So using something like what we have means that you can just connect the data you have into us and we take care of all the, all the, all the kind of heavy, heavy work to do with all the applications and the integrations and so on.
Um, so yeah, if anyone's got any questions, you know, we, we, we finished the presentation now. If you got any questions, please put them into the chat or you can just come off mute and just, um, just talk to us the old fashion way. Um, but I've just seen some questions from the chat now, from, from David. Have you found that people are happy to use chat bots?
I guess Neil, do you, do you wanna pick that one up because you're probably more on the, on the front line talking to the, to the clients who are actually using it. And you used to, you weren't using chat bots when you were a [00:37:00] trader, but you can now see the efficiencies of, of how they are being used. Yep.
So, um, people are very happy to use them in, in what we see. Um, we're seeing them being used for, uh, well, I guess I would call self-service. So, you know, I'd like to get some data. I don't wanna have it on a screen ticking, you know, my screen real estate is valuable. If I can go into a qu chat and just query something, data comes back into the chat that, that's ideal for me.
So I guess that's, that's the sort of, I guess, low risk use case. Um, but what we're seeing is that people are using it for trading. So one of our clients is in that West, They have a execution bot within Symphony, where, where their clients can, uh, ask for prices in bonds and can actually execute within the chat.
What we've been involved with as well is a, is a sort of pre-trade, uh, negotiation whereby, uh, we have an asset manager who goes out [00:38:00] and just canvases access. And some interests on the chat, rather than having to read them off the screen, they're then, uh, able, our bot framework can pick it up and can do all the sorting and present it in a nice fashion back on the screen for them.
The benefit of that as well is, um, that's all getting recorded. They can refer back to it later rather than, like, I, I think I said before, if people are just using chats to communicate, um, you're having to scroll back through, try and find any information. So I think that, you know, the rise of chat box from, um, just being used as a sort of query tool or a, a pseudo sort of Bloomberg command, it's been pretty rapid.
We're seeing it in the financial markets and the areas we deal in, and we're also seeing it, uh, a huge amount in other areas. For example, in prime brokerage,
Cool. Thanks Neil. Um, okay. I've got a couple of questions in here now, so we'll just kind of work [00:39:00] our way through them. Um, are larger financial organizations changing their attitudes to having their data in the cloud? Uh, yes. Yes. No, I think I, I always have seen, um, Cloud in big financial institutions has been like a bit of a tanker ship that's taking a very long time to, to kind of turn around.
Well, I think that that kind of turn is, is, is happening at a faster rate. Um, I think most financial institutions now are using the cloud in some shape or form. There's still this level of. Sensitivity of data, uh, and some data has to remain on Preem. And a problem that you kind of have, that you're probably all aware of, the problem you've got with data that's sitting on the cloud is particularly on the bank side, um, more than anything is, is the regulatory fines.
You know, if there's a breach of data, if there's, you know, someone can access it, you know, the fines that you can get from the regulator can be in the billions. So therefore, the sensitivity of [00:40:00] data, anything that has, you know, client identifies and names and things like that, you do find, um, some of it.
Does stay on prem, but on prem can mean on the cloud as well. You know, you have different levels of on-prem, you've got what's called bare metal, or you're actually deploying it into a physical box that's sitting somewhere through to having your own, uh, dedicated cloud service that could be hosted in AWS or somewhere like that.
One of the big banks, um, was it Bank of America? I think? Um, they, they created their own cloud data center that's sitting in Nevada under some rocks somewhere. But essentially that's the same as a cloud service, but it's on-prem. So yes, attitudes are changing towards data on the cloud. Um, and I think some of the encryption technology is helping making that easier.
Uh, and then, like I said at the very beginning of the presentation, the likes of the fca, you know, they are using it, They are happy with it. Um, and they've set out their guidance as well. If you just google FCA guidance on the cloud, it's worth just getting [00:41:00] familiar with that if you're not already. Um, okay.
I think on that, on that, I might follow up, um, just relating to the cloud, uh, question how, how scalable. Are these solutions, uh, especially for brokers with large retail client base can chat but handle very large numbers. What are the maximums? Um, not, not within my intelligence and the maximums, but the, the, the benefit of cloud deployment is that it's scalable.
You know, as user demand rises, um, the solution auto scales to meet the demand that's placed upon it. So we are, aw, we're AWS based. Um, Somebody called ECTs, and they basically, the resources of those expand and contract to deal with the level of traffic that we're seeing coming into the platform. Uh, scalab scalability, essentially, potentially unlimited.
The other point as well is that by having a scalable service, um, your [00:42:00] costs in essence mimic the demand that's being placed upon you. You know, as, as you scale up, you end up paying more per hour as or per unit of time. Uh, but in quieter times you're not having a lot of redundancy.
Well, thanks Neil. Um, yeah, and, and Neil mentioned EC two. So EC means elastic compute. Um, so that basically means services and machines get scaled up and down depending on how much, you know, traffic there is how much usage there is. But that's, you know, this kind of. Way of scaling things up means that potentially, um, Sally, just to answer a question, you can handle an unlimited number of, of kind of bot queries, for example, because an AWS cloud service will scale up as and when you need it and then scale back down again when you don't need it.
Um, but yeah, there's, yeah, but it's no secret that Jeff be also on the Richard Richard's people in the world. And I think, you know, a massive chunk of the [00:43:00] revenue for Amazon is probably more than the retail side comes, comes from aws. And again, that's part of that trend of people moving to the cloud. Um, okay.
I'll, I'll answering any more of these questions. Um, Darren says, Are all clients using the same platforms or do you need to support multiple ones? Um, I would say our clients use. So we've got about 20 integrations in our platform. Um, probably clients are using three of them, maybe four. Um, and, and most of them, you know, and this would be, so we have an Excel ad in which you can push and pull data is where the name comes from.
Um, and that allows you, um, to be distributing data to your clients in Excel, for example. Or your, or your clients could even be, you know, submitting prices or contributing prices or orders from Excel back to you. So we've got chat that we spoke. You know, we mention all those d these different chat platforms.
So Symphony is something that we, we do a lot with, [00:44:00] um, because their, their platform and their APIs are very open and you can do a lot with it. Um, so Symphony Chat bots and also Symphony, what's called extension apps. So they're little mini apps that will sit inside Symphony. Um, and you can do things like, so for example, some of those videos that Neil showed you, you can embed that inside a symphony chat.
You could be clicking buttons in this little app and that would be sending messages into chat. So we're doing a workflow actually around that in, um, Buy side, sell side workflow, You know, we've got a large buy side asset manager who's able then to, to share or, or to do kind of pre-trade negotiation, uh, inside, um, Symphony using a combination of, you know, connecting the data to us, um, or connect or connecting the data to, to this application using sy something like Symphony, having any extension app, having a chat bot.
So that's, that's. Excel chat. Uh, the other one is APIs, you know, ev everyone is, is kind of [00:45:00] moving towards this kind of API driven world. So API is obviously application pro program interface. So it means you can access an application without needing to use, you know, a screen with buttons on it. You can do it all programmatically through an api and that means that, um, you know, a lot of the sophisticated clients that you might have, some of the hedge funds, some of the high frequency firms, some of the banks, if they want to be able to connect to your data easily, providing them an API is a very arrest API is a very easy way to do it.
Like Neil mentioned earlier about one bank. Um, who managed to connect to our REST API in year, it's like 30 minutes or so. And then you've got other things like you're probably, you're probably familiar with, like fix connectivity. So FIX is great, you know, it's a standardized way of doing stuff. However, there's quite a heavy lift to connect into it.
So again, with something like us sitting in the middle, we can connect to fix and we can make that interface available in whatever is easy for you. Um, yeah, so they're kind of the three, three main ones. And I said kind of four as well, the fourth. [00:46:00] Web and mobile cuz everything that you can kind of connect data to and is available inside our platform, you can then access that in on the web or even on on a mobile as well.
So again, it makes it very easy for you to scale your client services and hit all of that long tail of people. If you can just give some, you know, give people your quotes and prices and a web browser, it means you don't need to worry about installing something, deploying something, you know, building a fixed gateway, all that stuff disappears cause you can just give it to them in the web.
Um, so yeah, they're, they're the, they're the kind of the most common ones. We support them all. You know, that's, that's kind of what we do. Um, okay. Got a question here from vi. Um, is Symphony getting much traction? Isn't everyone still using Bloomberg Chat? Um, So Symphony's getting more traction now, I think Bloomberg has about 3, 300, 300 30,000 users.
Symphony has between three and 500,000 users. However, Symphony is owned [00:47:00] by, um, you know, the shareholder group who owns Symphony is all of the banks as a few buy sites. And then as Google, they're all the shareholders of, of Symphony. Now when I say the banks, obviously they're all sell side. So lots of sell side use Symphony a lot.
So if you are an inter dealer broker or even some agency brokers, but if you're dealing with the sell side, a lot of them will start to be using that and maybe ask you to connect via Symphony Bots and so on. Again, if that's a case to give us a call, um, but in terms of, um, the traction and how it's growing, You have lots of sales side use it.
There's your typical bell curve, you know, pretty skewed a little bit, but you know, some banks use it a lot. You know, the big banks, your jps and your Goldmans and cities and people like that use Symphony a lot. Um, and then the smaller banks maybe, um, not so much or some of the European banks, not so much.
But it's generally kind of shifting because people are appreciating that Symphony is not just a chat, it's this kind of infrastructure that's that's easy to connect to lots of different [00:48:00] places. Um, Bloomberg Chat on the, on the other hand also, actually just to finish on Symphony, Symphony's obviously very cheap if Symphony costs 20 bucks a month, um, whereas Bloomberg costs 2000 bucks bucks a month.
So all of the big banks and other reason interested in using Symphony cause it's, if there's people who are just using it for chat, you know, you might wanna be getting people off the $2,000 version onto the $20 version. Um, but, uh, The other, you know, and particularly there's people in the middle office of the back office that don't need all the functionality in Bloomberg.
They might want to use a cheaper symphony chat. What I was talking about at the beginning of my presentation as well about this, this thing where the cloud has opened up all this world of FinTech and this massive ecosystem that, that I showed on the screen. You know, all of that ecosystem is disintermediating Bloomberg and bits of Bloomberg and bits of financial market infrastructure that's been around for a long time and not changed.
So then you can take Symphony and you can take, you know, I might use this [00:49:00] bit of trade analytics, I might use this bit of market data. You can start recreating your desktop using stuff that's not Bloomberg, which is what the banks are obviously interested in, cuz Mike Bloomberg makes me know billions every year through selling Bloomberg terminals.
But Bloomberg, like I said, are beginning to come more towards what Symphony are doing with opening up the chat, bringing in bots, bringing in api. So it's gonna be interesting to see what happens. Um, okay. Another question here from Brendan. Can we use the platform to send notifications to clients? Do you wanna answer that one, Neil?
Yep. Uh, so the answer is yes. So, uh, I guess when I was talking about, you know, what do we do the data wants is in the platform, you can amend it, you can collaborate, but a large part of what we do is to send out alerts and notifications. So you can choose to set up, you can choose to have a message sent, uh, on an action.
So literally like clicking a button on a web app, [00:50:00] you can have it to be set up when a certain constraint is met. For example, you know, if this price goes above x or if this trade status changes to y uh, or you can also have it done on a sort of more system events. So you've got two parts already. You've got, you know, one or, you know, when do I send.
What are the constraints? And the second part is where do I send it to? And in terms of where you can send it to, I mean, it's within all our other integrations. So this could be pinging, uh, a text message, it can be into WhatsApp, can be an email, it can be a message into the chat platforms we support. Um, we can sort of do something into Excel, I think, very frankly.
Um, and then we can also be sending messages off into sort of API endpoint. We are looking, uh, well, we're talking to Bloomberg about what they might open up to. Um, but yeah, the answer is yes. You know, The third point, I guess, is also what message can you send. It can be pretty much whatever you want it can be just something to say, this has [00:51:00] changed.
You can pick up the data from the actual grid of data or from the data that's in the platform. You can send out a, a sort of customized view of that as well. It, it's becoming a more and more, uh, Frequent use case for us. Like I said, you know, working on a trading desk, broken desk screen, real estate, is it a premium?
Often you just wanna know when things are happening and you wanna be told about it and you want it to, you want it to be delivered to where you're gonna pay attention to it, whether that be your phone, whether that be a particular chat room. And you also wanted to, if it's going out to clients, you wanna make it look good and you can also add data objects.
And when you start in involving bots, you can send a message to them, people can respond to, so that only you're informing people, you also getting them to, you are also able to get them to reply or do something with it. Neil, I've got a question for you. Um, so you, you worked as a trader for a long [00:52:00] time, um, on the sell side, on a trading desk, and you were working in a, in a, in a quite a manual market as it were.
You know, it wasn't stuff that was on a screen, it was all still, you know, talking to brokers, talking to sales desks. Um, how, how would you, how do you think the, the way of working has changed and evolved from, from when you were sitting on a trading desk? What, three, three years ago was it now? Um, in, in terms of, and what you're seeing now and some of the kind of innovations in the market?
Good question. Um, okay, so, so the things I remember being the most annoying, um, If I had five or six or seven different brokers for inflation, for futures, for interest rates, all of them wanted me to sign into their own individual system in their own different screen. Um, I would much prefer to have something [00:53:00] where I can consolidate all those into one screen or where I can get something back in the chat, uh, automatically a sort of self-service type option.
You know, I'm just asking you for this. I don't really want, you know, I'm pretty busy. I don't want you to pick up the phone and answer me and try and get me to talk about anything else. So I think having the ability for me to curate my own data rather than rely on what other people think I want, that's a massive.
Uh, that is happening and it's, it is a big change. The the other point is around the sort of standardization and the way that you, you get asked for trade informa, you know, get asked to quote on trades. Now we're looking at something now where rather than using spreadsheets to describe trades, it's gonna be described in fpm L and it's gonna be a delivered in a way to my trading desk, such that it comes straight into my pricing system.
So I've gone from something where I'm looking at an email with a term sheet in French. I'm having to sort of translate that, [00:54:00] translate some notion of a schedule. I'm now able to just have it arrive directly to my desk. I'm pricing their version of the trade. They're the golden source. They've put it in a standardized format, and all I have to worry about is the risk on the trade rather than the risk of all the manual processes to convert an email or a spreadsheet into something that I need to price.
So I think those are the two main things. I, I wanna curate my own information I'm getting from all these various sources, and I want to try and remove as, as much of the manual repetitive obs heavy tasks as possible, which generally you revolve around when you're being asked to do stuff that isn't just a straight bond.
It's more complicated, involves structured types of. And I suppose just drilling down on, you just mentioned about data standardization, you know, that's, that's key in a lot of these workflows because if you've got standardized data, it means you can then start automating against it. Um, getting that data and doing things [00:55:00] with it, cuz you know, it's in a consistent format.
But one of the things that our platform allows you to do is there's a, there's a data mapping and transformation kind of engine that sits in the platform. So even though, uh, your client might not be in, might not have their data in your format, they can still share it. Comes into ibu, pull the cert. The data mapping service can then map and translate that into the standards that you want, and then you can feed it into whatever different systems.
So it doesn't matter if everyone has a different view of their data, a different layout of their data, different ways they name their kind of rows and columns through to different formats and layouts. All of that can, can be, um, streamlined and standardized by coming through. Um, the I push pull service, I see that on a.
Note, there's a question here from Darren saying, how do you track and audit all the activity taking place across the platform? So it's the iris pull service. So if you imagine we kind of, we as [00:56:00] the service sits centrally and data then comes through this service sitting centrally. Um, and then because of that we can track, um, you know, what application it's come from, which users have interacted with it, um, where it's going to.
So you have this kind of real time usage tracking as well as a historical audit and log of all the interactions of the data. Now, obviously our platform, you know, I wanna say we, I say we as a company, but we don't look at your data. Data's obviously owned by you as the, as the client or the customer.
Everything's encrypted. Um, so, so, so yeah, so, so there's a, there's a number of. Different things, then you can start doing with the, the tracking and the audit, cuz then you can start creating analytics. So we're doing, um, a project around kind of predictive analytics whereby we've got this pre-trade workflow.
Where when, uh, a buy side trader is sending out messages out to different sales [00:57:00] people asking for prices or AEs or whatever it may be, when you start measuring, um, how long it takes that client's com, uh, how long it takes that, um, broker or sales desk to come back with a price, or when you ask a size that's this big, who gives you the title spread, you can start building up these type of analytics based on the data, then that's going through the service.
So the more you can connect your data together, the more you can leverage up that data. The data itself can provide value through analytics. That analytics can be useful for you as a broker so you can get more insights into your client. That those can also be useful for your client. Um, cuz they, cuz you are just providing them something different that no one else has.
So that's, that's a kind of key area that we see as we're going forward. Um, so I suppose just to round up, cause we're just coming to the, to the, to the hour now and really appreciate everyone who's kind of stuck around and, and listen to us, um, talking, um, Like I said at the beginning, I, I kind of hope you learned something new.
We, we tried to cover a lot of ground here because I know, um, it's the [00:58:00] first time we've done something with the broker club. Um, we wanna talk more about technology and then, and then how you can use that technology and apply it to problems and use cases and so on. So Neil ran through some of those. I think the slides have already been shared, so you can kind of, um, you can take a look at those if you've got any questions.
Neil, can you just forward to the next slide if you've got any questions. Do feel free to contact myself, um, or contact Neil. Um, and, and we can help you out with any questions you might have about, um, the workflows, the use cases, the integrations, if you wanna, you know, I wanna learn more about cost and how we get going.
We're very, very quick to get going, you know, if you, if you wanna build a prototype with us or we can build on for you, or you can just, you know, if you've got a developer, they can start doing themselves. You can be up and running in, you know, an hour if you want to be. It's kind of that quick. Um, and, and Neil can, can help with, with building any kind of workflow apps and so on, doing some more heavyweight integrations into your own internal systems, we can do as well.
Um, so there's, there's, there's a whole [00:59:00] bunch of stuff. Um, so. Yeah, that's, that's us all finished now. So again, thank you very much. We're honored to, to have had your time and tell, tell you about the technology trends, what we're doing and you know, and how we can help. But, uh, do reach out if you got any questions or if you think there's something that resonated and you want to kind of double click on it, then we are here to, you know, here to help you.
Um, Thank you very much Matt, and thank you to you and Neil. That was a really interesting presentation. Um, for the benefit of everyone, this has been recorded. There's lots of information there, so if you do want to listen and watch it again, um, it will be available via our website, um, from tomorrow, or you can just email me and I will, um, email a copy of the, um, recording over to you.
Um, I will also distribute the slide deck again with Matt and Neil's contact details, um, shortly after this call. So can you all join me and thank you Matt and Neil for, uh, a really interesting [01:00:00] presentation this afternoon and, um, thank you all. Have a great day. Cool. Thank you Sally. And yeah, thanks everyone for listening five o'clock and enjoy the sunshine.
Thanks guys. It was really interesting. Thank you. Thanks Jeff.
Cool. I'm gonna jump off.