Digital game-changers in financial markets
Digitisation is driving competitive edge in financial markets globally. Giulio Katis, Head of Financial Markets eCommerce at Westpac Institutional Bank, explains what’s on the way.
Financial markets continue to be revolutionised by digitisation, moving from manual phone-based transactions to electronic transactions, through online portals and terminals, such as Bloomberg.
But there are more changes on the way as algorithms, data, machine learning and real-time trading ramp up.
There’s a strong push for banks to keep digitising internal processes, due to the focus of regulators across the world on financial institutions’ abilities to capture data on customer interactions.
In this Q+A, Giulio Katis, Head of Financial Markets eCommerce at Westpac Institutional Bank, outlines how institutional customers and banks will connect to markets, make investments and manage their risk in the foreign exchange and fixed income markets.
What’s next in digitisation of financial markets?
In recent years there has been a rise in the importance of what we would call ‘multi-dealer platforms’ that aggregate prices from many different banks. They’ve been set up by Bloomberg and others – like FXall or 360T in the FX market – specifically for customers who choose to use a panel of banks.
Multi-dealer platforms are a bit like supermarkets. They offer users speed and convenience – so instead of ringing six banks for a price, customers can be set up so that their panel of banks can send prices to them on demand, and all in the one place.
The users of these platforms get transparency and a wealth of information on pricing, which consequently raises the bar on consistency, speed and competitiveness in pricing.
Banks can also distribute other electronic services through these platforms. For example, Westpac was the first Australian bank to offer its customers self-serve execution algorithms on a multi-dealer platform, FX Connect, in 2016.
Some customers still want a bespoke advisory service where they speak to a dealer, though. So, the future may blend the two, with electronic platforms allowing banks to provide a richer type of service through those platforms.
For example, we are exploring the benefits of the globally popular Symphony collaboration platform. In addition to traditional chat-based communication, it allows customers to send requests to many banks, and the banks to respond via ‘bots’ in a workflow format that the customer has designed.
It integrates into the customer’s own systems and data analytic tools, and allows banks to offer a more bespoke user experience.
And how are interbank markets changing?
Globally, many parts of the interbank market are becoming increasingly electronic, and are using algorithms, data and machine learning in real-time trading.
A number of non-bank liquidity providers also are starting to compete with banks, particularly in liquid markets like spot FX, equities and futures.
How are individual markets digitising?
It's a hybrid picture. Some parts of the market are digitised, while others still aren’t.
Spot FX has definitely become highly digitised over the last 10 years. And futures execution has become increasingly digitised.
In the FX Forwards market, the interbank market is still not that electronic. However, customers expect to be serviced electronically in FX, and the FX market as a whole is highly competitive. So, an FX Forwards desk in a bank must straddle the digital and non-digital worlds.
Most fixed income markets in Australia are not yet as digitised as they are in the US and Europe. Much of the Australian Fixed Income market is driven by the local electronic exchange-based futures market. It is traded as a “basis to” these futures, so Australian fixed income trading desks need to straddle ‘voice’ and electronic marketplaces.
What is the role of technologies such as algorithms, machine learning, AI and analytics?
There’s a spectrum here, too. For example, in spot FX, for quite some time at Westpac we've had end-to-end, fully algorithmic, machine learning-based ability to create prices for customers, manage the risk and execute as required in the interbank market.
These algorithmic models choose when and how to warehouse risk and when to exit risk, based on customer flow and then how we engage in the market. We have a team of quantitative trading researchers continuously optimising those algorithms, because that market is super-competitive.
At the other end of the spectrum is the advisory business for wholesale customers, when these customers are not looking for that speed of execution, but perhaps considering whether to undertake a large or complex transaction. They may be dealing in an illiquid market such as Aussie corporate bonds, or seeking advice on how to manage foreign exchange, interest rate or commodity market risks.
These instances require more access to data, information, analytics and research – and someone to discuss it with!
And, mid-spectrum, there's workflow orchestration using a hybrid of electronic and human interaction, for customers whose needs are not so high-end advisory, but who may want to speak to someone to facilitate a trade or execution.
How are algorithms helping you to manage risk?
In the spot FX market, we no longer have the luxury of a customer knocking on our door so we can respond with a price. Now we have to be independently streaming two-way auto-executable prices for about 100 currency pairs – in some cases in deal sizes for well over AUD 100 million – around the clock and on platforms around the world.
To do that, you have to be very systematic in the way you choose where your prices are and how you manage your risk. If a customer executes on those prices, the data will go into a portfolio that's managed by an algorithm.
The algorithm looks at the broader portfolio of currencies and positions that we've inherited from our customers around the world and decides what our overall risk is at the moment. Do we need to rebalance and reduce that risk? And how should we go about it?
For example, we use algorithms to minimise the ‘market impact’ of our customers’ trading by ‘internalising’ as much of their flow as we can, minimising how often we need to execute in the market.
It’s a form of quantitative trading.
We have all been taking this approach in some of the futures markets we trade, but there is more for us to do in this direction.
Does this mean no more human input?
Our customers will always be looking for direct human help with more bespoke and complex needs. Even in the more algorithmic parts of the business, humans are still driving the service and outcomes – they are just doing different things.
For instance, think about the data scientists and quantitative traders, who are responsible for continually refining the algorithms that are used to price, make risk management decisions and to execute in the market.
The human IP is still there, but rather than sitting with an individual trader who's having to price and respond to each individual deal, it's sitting within a set of processes and algorithms that are constantly being refined and modified by a research team.
We also have a team of ‘algo supervisors’ and eProduct specialists who monitor the algorithms around the clock and intervene when needed – which isn’t often.
They work closely with the research team and are critical to ensuring a variety of market, liquidity and operational risks are managed, and that our customers are best serviced through electronic channels.
I am really excited about the momentum of digitisation in financial markets and what we can do in this space, both to make outcomes for customers even more consistent and to promote competitive pricing across our products.
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