Matthew Cheung: 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 Ransquawk selling it to brokers and as well as lots of other traders and people in the market.

Matthew Cheung: Moved on to the technology side with I push pull with with David Jones, Dan Eccleston, the other co founders of the company who have been doing this for the last seven, eight years or so selling into financial institutions. I've also just joined the advisory board of the centre of FinTech at the University of East London.

Matthew Cheung: So we're very deep in the FinTech world. So that's me. I'll be covering the first half of this webinar. And Neil, do you want to introduce yourself?

Neil Weatherall: Yep. Hi, I'm Neil Weatherall. So I've had two years of Ikebush Poole now working as head of technical sales. Prior to that, I spent 16 years on the sales side.

Neil Weatherall: Predominantly trading sterling inflation in cash and derivatives. So I'd like to think I've got a decent understanding of what trading desks do and the role that brokers

Matthew Cheung: play. Yes. So today's webinar is going to be about scaling client services to optimize OTC market data and quote delivery. But before we jump into that.

Matthew Cheung: We're gonna well, I'm going to cover kind of the latest technology trends that we're seeing in kind of the broken world and the financial markets more generally, and then Neil's going to jump into some use cases and case studies will cover questions at the end, but don't let that stop you from asking questions.

Matthew Cheung: As we go through required, you know, good, good size group here where we can we can filled 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. And I will kind of talk through some of the slides for the people that are dialing in as well.

Matthew Cheung: So, so okay, we'll, we'll kick off. I think we've got a good, good group here now. So, okay, starting off. So I push Paul. Who are we? So, yeah, we were 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.

Matthew Cheung: So through our platform and customers that are using us for lots of kind of omni channel workflows across lots of different parts of the financial market lifecycle, 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.

Matthew Cheung: It's 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 our 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, sometimes it's with banks because we're an independent software vendor.

Matthew Cheung: So we do see lots of different perspectives as well as having kind of a finger on the pulse of latest technologies. So it's gonna be quite fast and furious. My first half because there's quite a lot of ground to cover, but I'm hoping that I can at least you know, you can come away with at least learning one thing from from today's session, right?

Matthew Cheung: So to 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 faster market is more efficient, and it means it's very quick and easy to get stuff done. Now 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.

Matthew Cheung: And the F-C-A-C-E-O has the goal of becoming a data and digital first regulator. So that's really important because. because they've now opened up the floodgates to using cloud technologies, giving 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 fintechs into this arena.

Matthew Cheung: So looking at the cloud providers like AWS, 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 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're making blockbuster revenues in the last quarter as well as all through the pandemic.

Matthew Cheung: So Microsoft cloud, their revenue is 23 billion. It's up 32 percent in one quarter. AWS. So that's, that's a Amazon web services part of Amazon. You know, they're posted their highest growth rate for the fourth straight quarter. Their revenue is up 37%. Google cloud. They're actually the fastest growing their revenues up 44 percent just in the last quarter.

Matthew Cheung: So think about that. And we're still at the cusp of using cloud because not many people are really using it for everything. 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.

Matthew Cheung: That's more than hosting. Looking at fintech, the cloud has enabled this massive rise in fintech has 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 activity in fintechs.

Matthew Cheung: But the cloud has been probably the core part of that movement. And fintechs big advantage is speed. And that's where cloud has a big part to play in. So look at what it's enabled. So on this Transcribed On this this, this grid, you can see on this picture, you call map even you can see hundreds of capital market fintechs.

Matthew Cheung: If anyone's spotted, I push pull. Yeah, we are on there. I'll help you. You can see us just down there. We actually put us in the wrong category, but that's that CB insights, which is a good research kind of platform to look at. So that's I push pull. But this capital markets ecosystem, what it's doing is disrupting the incumbents, right?

Matthew Cheung: The incumbents that you that, you know, and maybe love or hate you know, Bloomberg, Refinitiv, you know, S& P market and so on now put it into perspective, fintech. is the biggest growth area in the next decade in financial services. If financial services grow a very small amount every year, fintech and our crypto as well is growing rapidly.

Matthew Cheung: So there's a lot of investment coming to this and all of this is disrupting a lot of traditional ways of doing things. and pull this into perspective again, drill down even more to that capital markets. FinTech world. You can see the valuations of FinTech, something like true mid, which is a corporate bond trading platform or symphony, the chat platform.

Matthew Cheung: That's more. If you look at symphony, they're worth more than the two biggest inter dealer brokers in the world, which is crazy, right? And that's because the technology companies you get big multiples and technology companies where traditional breaking businesses. Yeah, if if it's very, if it's all kind of voice driven, they don't see those multiples.

Matthew Cheung: So that's why you've seen this big investment back into technology. 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 proking of 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.

Matthew Cheung: So you've got brad levy. He's the new symphony ceo. He was formerly he was head of symphony. Strategic investments at Goldman's used to work at market, and they want to unite voice based trading with natural language processing. So you can kind of see where the trend of the industry is going in this regard.

Matthew Cheung: Now, talking of chat, you know, you're all familiar with the chats that you use every single day. Probably a lot of you will be using things like IB chat or Bloomberg chat. That's the biggest player, right? Bob. 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 Symfony has had with bots.

Matthew Cheung: Yeah, 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. And there's even kind of early beta versions of inter company chat. You know, a lot of you might use Teams already for doing your internal chats and your internal voice calls.

Matthew Cheung: You can do that. You know, you'd be able to do that externally and talk to people externally. Yeah, Symfony, Refinitiv, Bloomberg, they've all got beta 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.

Matthew Cheung: Then there's slack. You know, it'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.

Matthew Cheung: 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 the screen.

Matthew Cheung: through to more complex workflows such as mapping fixed messages into chat or streamlining complex pre trade workflows. If you want to see some examples of that just take a picture of that QR code and you can see some videos. We'll share these slides if you've not seen them already.

Matthew Cheung: Now, I push pull 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 code, and then you can roll that out when it will work in a variety of different chat platforms that you integrate into.

Matthew Cheung: So our chat platform is already being used in a number of brokers, lots of banks, market makers, buy sides to lots of the industry. Again, this is another kind of key focus that you need to look out for. Yeah. Now with bots in place that allows automation to become pretty straightforward because you can standardize, you know, workflow standardized data.

Matthew Cheung: You know, one of our IDB clients is already doing fix messages and then we map and convert those into chat messages and then even doing like bot to bot workflows with like there's a major tier one US bank already doing that with us. So this makes, you know, if you're using them bots automation, this makes the broker more efficient so 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.

Matthew Cheung: And why does the client want it in this? In a particular format that they're demanding. That's because they have their own automation and streamlining and so on that I 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.

Matthew Cheung: Right, let's move on to data. You know, that's 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're particularly in the OTC kind of derivatives market.

Matthew Cheung: There's a lot of stuff done manually. There is still voice and phones, but lots of that has migrated into chat, but chat is still people typing in the chat. which is crazy. You know, two people manually typing things, copy, pasting things around, emailing spreadsheets around. So all of this is really slow, is really inefficient, is very manual.

Matthew Cheung: There's lots of operational risk. I'm sure you I'm sure you've all seen the news this week where city group made a massive fat finger on like they wiped off loads of money off the stock markets. I think the Swedish stock exchange, you know, MX was down 8 percent of one point. That was literally just a fat finger.

Matthew Cheung: But nonetheless, data is the lifeblood of this industry of this industry 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 of your screen in your business is Excel being used for any mission critical functions.

Matthew Cheung: So in the chat, if you just type yes, no or sorry if you type one for yes. Hmm. Two for no and three for don't know. So if you can all just jump into the chat and just, and just chuck in there. Yeah, if you're, if Excel is being used for any mission critical functions. Got two responses so far. I can't think of a little bit better than that.

Matthew Cheung: Okay. So we've seen lots of yeses so far. The chat icons on the top right. If you can't see it in the top right of the screen, all yeses so far. So lots of yeses where people are using Excel for mission critical functions. So that's really interesting, right? Because Excel is what you call shadow EUC.

Matthew Cheung: 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, Excel is being used for lots of mission critical functions. So thanks, everyone that responded. The more the merrier here. It's good to get lots of responses from everybody. OK, so moving on.

Matthew Cheung: Right. So moving back onto data, right? So, so data is often called like the new oil. But we see data more as like building blocks 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 another application somewhere else.

Matthew Cheung: 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 to anywhere you want. This is, this is kind of the future. This is the way lots of firms are beginning to do things. Now, data can be distributed. It can be pushed or it could be pulled.

Matthew Cheung: You know, a 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. So In terms of what we do, I push pull so we can connect to your data. So this could be some access that is sitting in in your email, right?

Matthew Cheung: This could be some data sitting in a spreadsheet. This could be something that someone's manually typing into a chat. We can connect into that. We can connect. We've already got it in the database. Fantastic. KDV 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.

Matthew Cheung: So typically people for our clients are using it to distribute out to client side Excel as an API into chat or even into other systems.

Matthew Cheung: 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 iPushball, we can do that. We can then easily use that data and then plug it into off the shelf applications and machine learning algos.

Matthew Cheung: There's some really cool technology out there, which is literally off the shelf. You just got to plug your data in. So trading analytics and you can use all these best of breed of solutions that are out there, like snowflake and Google, big Google, big query and other technologies like that. So you can utilize the clever machine learning that Google has been making lots of money out of for the last few decades.

Matthew Cheung: 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 going to help you be a better broker, or you're going to use it then to wrap it up and monetize it and sell it to someone else.

Matthew Cheung: 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 has actually been around for 13 and a half years now. You know, it was first the first white paper came around in October 2008. Why is October 2008 important?

Matthew Cheung: Because the month before is when Lehman's went bust, right? There's, there's, there's, there's... 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, Satoshi Nakamoto, you know, he wrote this seminal white paper about Bitcoin. The QR code there is is is for the for the white paper.

Matthew Cheung: 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 talk about. crypto and crazy things, right? Read the paper, it's easy, eight pages, but you'll learn something from reading it. Also, if you like it, read the Ethereum white paper as well.

Matthew Cheung: That actually dives a little bit more into financial use cases. They talk about derivatives, talk about gaming 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 is coming into its own.

Matthew Cheung: Probably everyone knows someone that's quit a bank and working at a crypto firm, right? Yeah, it's happening right now. So nothing puts this into 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.

Matthew Cheung: Like 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 We need to look at what they're doing now We want real use cases though, right? You know, it's all well and good crypto trading But what about us through in financial markets we do this stuff.

Matthew Cheung: 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 Pith network, that's PYTH, which is something that I push, pull is connecting into.

Matthew Cheung: So. On the left is Sam Bankman Freed. You know, some of 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 is the youngest billionaire in the world. He's made it all through crypto. What did he do before crypto?

Matthew Cheung: 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, the market makers, you know, James streets and jump trading and people like that.

Matthew Cheung: And they created the PIF network. So what they're doing is providing free market data. Cause these are all market makers, right? They're quoting prices. They're creating free market data. Based on the Solana blockchain, which is very fast and cheap. Unlike Ethereum and the market makers then earn tokens when they're contributing prices.

Matthew Cheung: And what that means that all these different contributors are actually owning the network. So that means no more centralized, you know, market data feeds that you have to pay for. Instead, you can get it for free via something like the PIF network. So it means the contributors own the network. And this is this big web three shift that you have in thinking.

Matthew Cheung: Right. Lastly, NFTs. I love NFTs, right? I'm big in them. But that's why I'm mentioning them. But, you know, 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.

Matthew Cheung: That's just the wild west cowboy casino. You know, if you can do that fantastic and make lots of money overnight, but that was last year and you could have done this year, you're not going to do it. Now what you want to be thinking about is how can you use NFTs or non fungible tokens and apply that to use cases in financial markets, right?

Matthew Cheung: 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 rights to that data and they fractionalize ownership through tokens. So think about how can you use that OTC market data.

Matthew Cheung: If I've got some access or I've got some prices, how can I, how can I create ownership of that or fractionalize it? So that's what you want to be thinking about with NFTs. forget about digital art. Right, that's my bit done. Before I hand over to Neil though, we'd just like to get your thoughts on where you see these trends yourself and what is your company investing in.

Matthew Cheung: 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 clouds. Number two is voice and NLP. Number three is chatbots. Number four, automation, and just type the number into the chat.

Matthew Cheung: Come on, 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. Yeah. Yeah. Sharing what you're doing. It's good for us to understand what you're doing, because then we can focus the next half of the session as well. So it's relevant to you.

Matthew Cheung: So just put the number of. of what you're interested in to 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, anything is great. Okay, so we've seen a bit of a mix here. A couple of people all the above, well that's interesting.

Matthew Cheung: You must have lots of money to spend. One, five and six. Cloud data and data analytics. Yeah, that's a good one there. L McCarthy. I think that's probably more of a good easy first on ramp on to all this new technology. Fantastic. Okay, well, we'll move on. Thank you for everyone who answered that. So I'm just going to I'm going to kill the share without leaving the room.

Matthew Cheung: There we go. Stop sharing and I'm going to hand over to Neil. Give me one second. Where are you, Neil? There you are.

Matthew Cheung: Right, presenter. Yes, there we go. You got that, Neil? Cool. Okay, I'm gonna go on.

Neil Weatherall: Great. Thanks, Matt. So I'm going to talk about what we see in practice and some of the solutions we've seen in the market. And so, as I said earlier, most of my career I've spent on the sales side, on the trading desk. So I'm coming at it from the point of view of traders and brokers, you know.

Neil Weatherall: What data were we interested in? sO first I want to 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 bonds or swap curve mids, there's quotes, so that's your bids and offers, axes, IOIs. These can be live or very recent or historic.

Neil Weatherall: I guess I'd classify these as something where you know what it means and you can trade it. But you also have data that you might just want to consume, for example, a vol surface, which you use as an input to a pricing 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 STP messaging.

Neil Weatherall: 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?

Neil Weatherall: What, you know, why should you be collecting it and using it and analyzing it? Well, really, you know, you're going to use this data to trade. You want to inform your clients. You're trying to build relationships, find ideas. You want to help your clients with their data needs. You want to fit in with the new ways of working on your side and on your client side.

Neil Weatherall: 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 to compile it, store it, and to validate your data. And then you've also got to analyze it and share it where it's needed.

Neil Weatherall: For us, this all comes down to the right data. The right time and the right place. So I want to 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.

Neil Weatherall: So the first up is just data distribution. And this is something we're being heavily used for by TPI CAP. So the problem. We started with options for all surfaces and capital premiums. So these are large grids of data. And there are lots of them. Now their in house Excel add in that they've given out to clients was legacy technology.

Neil Weatherall: It was hard to support. It was out of step with their other in house technology. No access control, no usage tracking or audit. The solution, a new white labeled Excel add in, backed by the Eifish Pool platform on the cloud. Now this has the benefit of only needing one integration between Eifish Pool. And that's what I'm showing here.

Neil Weatherall: So, this graphic on the screen is just showing you that single connection into the IpishPool service and we then serve the TPI cap clients into their application of choice. That's very important to note here that IpishPool is deployed within the sort of TPI cap. TPI cap infrastructure, and with white labeling, their clients may not even know that we're involved.

Neil Weatherall: So what do they gain by using iPushPool? Well, they've got an extensible service. They have one platform that allows them to offer clients Excel, API, chat onto desktop and mobile. There's an integration into their in house SSI and permission controls and with the cloud, you've got future proofing and a very agile dev path.

Neil Weatherall: What are the other benefits, whereas quick delivery and a very low project risk, a reduced cost of ownership and 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.

Neil Weatherall: Brokers can now track the usage and it can provide a highly tailored, higher level of service. And it's obviously led to increased desk revenues and efficiencies.

Neil Weatherall: Next up, I want to talk about data transformation. Now, there was a twofold problem here. Our customers clients were using shared worksheets to publish and consume data, but they also needed to have shared model parameters between the users and the desk. And all the solutions they'd looked at so far were implemented, were pretty cumbersome and prone to error.

Neil Weatherall: 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 of 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.

Neil Weatherall: So the solution here, again, using the iPresspool platform. But specifically with our transformation module. So we set up some client specific mappings. that were applied to the data that was being shared via a new Xthin add in. 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.

Neil Weatherall: 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 as 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.

Neil Weatherall: And they saw a high ROI. Now we can transform more than just grids of data. For example, you can expand this to chat 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.

Neil Weatherall: So the next example is where we take a grid of data and we use our no code workflow tools to build an application. So we identified broking and sales desks were 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 there's been an increase in the number of sources that brokers are having to monitor.

Neil Weatherall: You've got your phone, you've got multiple chat platforms, email, perhaps even some automated order entry. There 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 far too many messages.

Neil Weatherall: 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 solution really is a very simple level, a digital whiteboard, but with a host of extra functionality.

Neil Weatherall: 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.

Neil Weatherall: I'm going to show a short video now as to how this actually works and what it looks like on the screen.

Neil Weatherall: So we call this live quote views and we basically see a way of a way of bringing the pricing information together when you want to put new information into the platform. This can be added from Excel. Or from chat uh, you can use the classic pop up forms with drop downs, or you can copy and paste in syntax as well.

Neil Weatherall: It gets validated, and then the underlying platform updates the view so that everybody sees what's been, 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. So the web, Excel, into fixed messaging, into API.

Neil Weatherall: 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? Well, you have this centralized golden source of this data. You can help automate away, automate away some of the manual repetitive tasks to increase your efficiency and your reach.

Neil Weatherall: 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 make the most of every single trading opportunity. And to increase revenue. Now, the final use case revolves around fixed messaging. Fixed messaging is obviously an industry standard, but we tend to find that it's expensive to set up.

Neil Weatherall: It's expensive to run, requires technical expertise, and it's just out of reach for a lot of firms. What do we see people looking for? We use iPush pull to deal with data in, 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.

Neil Weatherall: 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. 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 platform.

Neil Weatherall: 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. Ensembl, Kozak, other API access. And the example here is bond axes. So we've gone from a very long, meaningless string into something you can see in a simple formatted on screen grid.

Neil Weatherall: So what do we think this does? Well, 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.

Neil Weatherall: 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 gone through this pretty quickly. Obviously, any more information, please ask on the chat or get in contact with us, you know, at another time.

Matthew Cheung: Thanks.

Matthew Cheung: Do you want to just move it ahead to one slide, Neil? So, so we kind of. Wrapped up the kind of the first part of the presentation there we can move to any questions. Now obviously the title of this 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.

Matthew Cheung: That's kind of 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 80 percent of their time dealing with 20 percent of their clients, but those 20 percent of their clients is 80 percent of their revenue.

Matthew Cheung: 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 they'll just pull up on the screen. It gives you ability then to present prices out. to people anywhere. I could be on a mobile, it could be on a web browser, could be into their spreadsheet.

Matthew Cheung: 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, 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, bot to bot workflows and so on.

Matthew Cheung: 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. So yeah, if anyone's got any questions, you know, we, we, we finished the presentation now.

Matthew Cheung: If you have any questions, please put them into the chat or you can just come off mute and just just talk to us the old fashioned way. But I've just seen some questions from the chat now from, from. David, have you found that people are happy to use chatbots? I guess, Neil, do you want to pick that one up?

Matthew Cheung: Because you're probably more on the, on the frontline talking to the, to the clients who are actually using it. And you used to, you weren't using chatbots when you were a trader, but you can now see the efficiencies of how they are being used.

Neil Weatherall: So people are very happy to use them in what we say. We're seeing them being used for well, I guess I would call self service.

Neil Weatherall: So, you know, I'd like to get some data. I don't want to have it on a screen ticking. You know, my screen real estate is valuable. If I can go into a 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. But what we're seeing is that people are using it for trading.

Neil Weatherall: So one of our clients is not West. They have a execution bot within symphony where, where their clients can Ask for prices in bonds. I can actually execute. Within the chat, what we've been involved with as well is a, is a sort of pre trade negotiation whereby we have an asset manager who goes out and just canvases access and some interests on the chat rather than having to read them off the screen there, then able our bot framework can pick it up.

Neil Weatherall: 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 that's all getting recorded. They can refer back to it later rather than like, I think I said before, if people are just using chats to communicate. You're having to scroll back through, try and find any information.

Neil Weatherall: So I think that, you know, the rise of chatbots from just being used as a sort of query tool or 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 a huge amount in other areas, for example, in prime brokerage.

Matthew Cheung: Cool. Thanks, Neil. Okay, I've got a couple of questions in here now, so we'll just kind of work our way through them. Are larger financial organizations changing their attitudes to having their data in the cloud? Yes, yes, no. I think I always have seen Cloud in big financial institutions has been like a bit of a tanker ship that's taking a very long time to kind of turn around.

Matthew Cheung: Well, I think that that kind of turn is happening at a faster rate. I think most financial institutions now are using the cloud in some shape or form. There's still this level of. Sensitivity of data on some data has to remain on prem on a problem that you kind of have that you're probably all aware off from.

Matthew Cheung: You've got with data that's sitting on the cloud is particularly on the bank side 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 data, anything that has, you know, client identifies and names and things like that, you do find some of it.

Matthew Cheung: 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. We're actually deploying it into a physical box that's sitting somewhere through to having your own dedicated cloud service that could be hosted in AWS or somewhere like that.

Matthew Cheung: One of the big banks was it Bank of America? I think 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. And I think some of the encryption technology is helping Making that easier on them.

Matthew Cheung: 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, and they've set out their guidance as well. If you just Google FCA guidance on the cloud, it's worth just getting familiar with that. If you're not already, um, okay, I think I'm not.

Neil Weatherall: I might follow up just relating to the cloud question how, how scalable are these solutions especially for brokers with a large retail client base? Can chatbot handle very large numbers? What are the maximums? It's not, not within my intelligence to know the maximums, but the, the, the benefit of cloud deployment is that it's scalable, you know, as user demand rises the solution auto scales.

Neil Weatherall: to meet the demand that's placed upon it. So we're AWS, we're AWS based use something called EC2s 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. Scale scalability, essentially potentially unlimited. The other point as well is that by having a scalable service your costs.

Neil Weatherall: 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 per unit of time. But in quieter times, you're not having a lot of redundancy.

Matthew Cheung: Yeah, and Neil mentioned EC2. So EC means elastic compute. So that basically means services and machines get scaled up and down, depending on how much traffic there is, how much usage there is. That's, you know, this kind of. way of scaling things up means that potentially Sally, just watch your question.

Matthew Cheung: You can handle an unlimited number 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. But there's no secret that Jeff Bezos, one of the richest people in the world. And I think, you know, a massive chunk of the revenue for Amazon, it's probably more than the retail side comes from AWS.

Matthew Cheung: And again, that's part of that trend of people moving to the cloud. Okay, aren't answering any more of these questions. Darren says, are all clients using the same platforms or do you need to support multiple ones? I would say our clients use, so we've got about 20 integrations in our platform. Probably clients are using three of them, maybe four and most of them, you know, and this would be, so we have an Excel add in which you can push and pull data.

Matthew Cheung: That's where the name comes from. And that allows you 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 about. You know, we mentioned all those these different chat platforms.

Matthew Cheung: So symphony is something that we we do a lot with because their their platform and their APIs are very open and you can do a lot with it. So symphony chat bots and also symphony what's called extension apps. So they're little mini apps that will sit inside symphony. And you can do things like, for example, some of those videos that Neil showed you.

Matthew Cheung: 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 buy side sell side workflow. You know, we've got a large buy side asset manager who's able then to to share or to do kind of pre trade negotiation inside symphony.

Matthew Cheung: using a combination of, you know, connecting the data to us or connecting the data to this application using something like symphony, have any extension app, having a chat bot. So that's, that's Excel chat. The other one is APIs. You know, everyone is, is kind of moving towards this kind of API driven world.

Matthew Cheung: So API is obviously application pro. program interface. So that means you can access an application without needing to use a screen with buttons on it. You can do it all programmatically through an API. And that means that 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 your data easily, providing them an API is a very arrest API.

Matthew Cheung: It's a very easy way to do it. Like Neil mentioned earlier about one bank client. Who managed to connect to our rest API in the years, 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. It's a standardized way of doing stuff.

Matthew Cheung: 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 and whatever is easy for you. Yeah, so they're kind of the three, three main ones and I said kind of four as well, the fourth being web and mobile, because 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 a mobile as well.

Matthew Cheung: So again, it makes it very easy for you to scale your client services and hit all that long tail of people. If you can just give some, you know, give people your quotes and prices in a web browser, it means you don't need to worry about Installing something deploying something, you know, building a fixed gateway.

Matthew Cheung: All that stuff disappears. You can just give it to them in the web. 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. Okay, got a question here from Viking. Is symphony getting much traction? Isn't everyone still using Bloomberg chat?

Matthew Cheung: So symphony is getting more traction now. I think Bloomberg has about 3, 300 330, 000 users. Symphony has between three and 500, 000 users. However, symphony is owned by you know, the shareholder group who own symphony is all of the banks as a few by sides. And then there's Google. They're all the shareholders of symphony.

Matthew Cheung: Now, when I say the banks, obviously they're all sell side. So lots of sell side use symphony a lot. So if you're into 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.

Matthew Cheung: Again, if that's the case, do give us a call. But in terms of the traction and how it's growing You have lots of sell side user. 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, you know, JP's and your Goldman's and cities and people like that use symphony a lot.

Matthew Cheung: And then the smaller banks, maybe 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 places. Thank you.

Matthew Cheung: Bloomberg chat on the other hand, also actually just finished on symphony. Symphony is obviously very cheap. It's symphony cost 20 bucks a month, whereas Bloomberg cost 2000 bucks a month. So all of the big banks and other reasons are they're interested in using symphony because it's if there's people who are just using it for chat, you know, you might want to be getting people off the 2, 000 version onto the 20 version.

Matthew Cheung: But The other, you know, and particularly there's people in the middle office or the back office that don't need all the functionality in Bloomberg, they might want to use a cheaper symphony chat. While I was talking about 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 I showed on the screen, you know, all of that ecosystem is disintermediating.

Matthew Cheung: Bloomberg and bits of Bloomberg and bits of financial market infrastructure has been around for a long time and not changed. So then you can take Symfony and you can take, you know, I might use this 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 because my Bloomberg makes me a billions every year through selling Bloomberg terminals.

Matthew Cheung: 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 going to be interesting to see what happens. oKay. Another question here from Brendan. Can we use the platform to send notifications to clients?

Matthew Cheung: Do you want to answer that one, Neil?

Neil Weatherall: So the answer is yes. So I guess when I was talking about, you know, what did we do the data once it's 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 on an action.

Neil Weatherall: So literally like clicking a button on a web app, you can have it to be set up when a certain constraint is met, for example. Now, if this price goes above X, or if this trade status changes to Y or we can also have it done on the sort of more system events. So you've got two parts where do you've got, you know, one, you know, when do I send it?

Neil Weatherall: What, 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 a text message. It can be into WhatsApp. It can be an email. It can be a message into the chat platforms we support.

Neil Weatherall: we Can sort of do something into Excel, I think, very frequently. And then we can also be sending messages off into sort of API endpoints. We are looking well, we're talking to Bloomberg about what they might open up to. But yeah, the answer is yes, you know. The third point, I guess, is also what message can you send?

Neil Weatherall: It can be pretty much whatever you want. It can be just something to say, this has 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 sort of customized view of that as well. It's becoming a more and more. Frequent use case for us.

Neil Weatherall: Like I said, you know, working on a trading desk, broken desk, screen real estate as a premium, often you just want to know when things are happening and you want to be told about it. And you want it to, you want it to be delivered to where you're going to pay attention to it. Whether that be your phone, whether that be a particular chat room.

Neil Weatherall: And you also want it to, if it's going out to clients. You want to make it look good. And you can also add data objects. And when you start involving bots, you can send a message that people can respond to. So not only are you informing people, you're also getting them to, you're also able to get them to reply or do something with it.

Matthew Cheung: NEil, I've got a question for you. So you, you worked as a trader for a long time on the sales 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.

Matthew Cheung: 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 in, in terms of what you're seeing now, and some of the kind of innovations in the market

Neil Weatherall: question. Okay, so, so the things I remember being the most annoying 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 and their own different screen.

Neil Weatherall: I would much prefer to have something where I can consolidate all those into one screen or where I can get something back in the chat 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.

Neil Weatherall: 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, that is happening. And it's a, it's a big change. The, the other point is around the sort of standardization and the way that you, you get asked for trade information, you know, get asked to quote on trades.

Neil Weatherall: Now we're looking at something now where rather than using spreadsheets to describe trades, it's going to be described in FPML and it's going to 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.

Neil Weatherall: I'm having to sort of translate that, translate some notional 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.

Neil Weatherall: So, I think those are the 2 main things I want to curate my own information. I'm getting from all these various sources and I want to try and remove as much of the manual repetitive. Obstacles, heavy tasks as possible, which generally revolve around when you're being asked to do stuff that isn't just a straight bond, it's more complicated, involves structured

Matthew Cheung: types of trade.

Matthew Cheung: 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. Getting that data and doing things with it because, you know, it's in a consistent format.

Matthew Cheung: 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 your client might not be in, might not have their data in your format, they can still share it. Comes into y, push, pull the er, 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.

Matthew Cheung: So it doesn't matter if everyone has a different view of their data, a different layout of their data, different ways they name of their kind of rows and columns through to different formats and layouts. All of that can, can be streamlined and standardized by coming through. The I push pull service, I see that on a similar.

Matthew Cheung: No, 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 I push pull service. So if you imagine we kind of, we are the service sits centrally and data then comes through this service sitting centrally. And then because of that.

Matthew Cheung: We can track you know, what application it's come from, which users have interacted with it 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 want to say we, I say we as a company, but we don't look at your data.

Matthew Cheung: Data is obviously owned by you as the, as the client or the customer, everything's encrypted. So, so, so, yeah, so. So there's a, there's a number of different things then you can start doing with the tracking and the audit because then you can start creating analytics. So we're doing a project around kind of predictive analytics whereby we've got this pre trade workflow.

Matthew Cheung: where when a buy side trader is sending out messages out to different sales people asking for prices or access, whatever it may be, when you start measuring how long it takes that clients come, how long it takes that broker or sales desk to come back with a price. Or when you ask a size that's this big, who gives you the tightest spread?

Matthew Cheung: 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 and let it can be useful for you as a broker to get more insights into your client.

Matthew Cheung: Those analysts can also be useful for your client because they Because you're 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. So I suppose just to round up because we're just coming to the to the to the hour now and really appreciate everyone who's kind of stuck around and listen to us talking.

Matthew Cheung: Like I said at the beginning, I kind of hope you learned something new. We tried to cover a lot of ground here because I know it's the first time we've done something with the Broker Club. We want to talk more about technology and then how you can use that technology and apply it to problems and use cases and so on.

Matthew Cheung: So Neil ran through some of those. I think the slides have already been shared, so you can kind of, 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 or contact Neil. And we can help you out with any questions you might have about the workflows, the use cases, the integrations.

Matthew Cheung: If you want to, you know, want to learn more about cost and how we get going. We're very, very quick to get going. You know, if you, if you want to build a prototype with us or we can build one 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.

Matthew Cheung: you know, an hour if you want to be, it's kind of that quick. 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. So there's, there's, there's a whole bunch of stuff. So yeah, that's.

Matthew Cheung: That's us all finished now. So again, thank you very much. We're honoured to have had your time and tell you about the technology trends, what we're doing and, you know, and how we can help. But do reach out if you've got any questions or if you think there's something that resonated and you want to kind of double click on it, then we're here to, you know, here to help you.

Matthew Cheung: Thank you. Thank you very much Matt, and thank you to you and Neil. That was a really interesting presentation. 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 it will be available via our website from tomorrow, or you can just email me and I will email a copy of the recording over to you.

Matthew Cheung: I will also distribute the slide. deck again with Matt and Neil's contact details shortly after this call. So can you all join me in thanking Matt and Neil for a really interesting presentation this afternoon and thank you all. Have a great day. Cool. Thank you, Sally. Yeah, thanks everyone for listening.

Matthew Cheung: Five o'clock and enjoy the sunshine.

Matthew Cheung: Thanks guys. It's really interesting. Thank you. Thanks, Jeff.

Matthew Cheung: Cool. I'm going to jump off.