Barclays applies tech to predictions, while HSBC and ING look at pricing accuracy
Bank foreign exchange market-makers are increasingly using machine learning and artificial intelligence technology to improve their pricing predictions and sharpen levels shown to clients.
Non-bank market-makers have long achieved strong revenues in FX from their ability to use technology to predict where rates will go next. This allows them to adjust their hedging or inventory holdings to benefit from expected moves in spot, on top of any bid/offer they earn from the trade.
But banks are now
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It takes a market-wide crisis to know how stable the underlying pipework is that supports it. In the $7.5 trillion foreign exchange market, the measure of its stability is liquidity.
This was put to the test last month, as intraday volatility triggered by president Donald Trump’s tariff announcements on April 2 resulted in an explosion in trading volumes, a widening of bid-offer spreads, and extremely challenging liquidity conditions.
So, how much did this event expose the vulnerabilities in the
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Artificial intelligence has increasingly become an all-encompassing term in financial circles. But in foreign exchange trading, what does it actually mean?
Banks have been vocal about how they have used AI for years when developing their execution algorithms, making documentation easier, and in their client chatbots.
But from a trading perspective, arguably the more impactful use case for market-makers is applying AI and machine learning models to tick data – looking at previous prices to build up a high degree of confidence in future patterns to ultimately forecast what the price of, say, euro/US dollar will be in the next 30 seconds, 10 minutes, or an hour, and so on.
Having a good idea of where the price will go over a given time horizon can inform a liquidity provider’s hedging strategy. For instance, if it shows the euro will appreciate against the dollar over the next five minutes, it makes sense for the desk to hold on to incoming euro inventory until it appreciates before hedging. That way, they can earn the appreciation on top of any bid/offer spread they capture.
There are a lot of unanswered questions about how much of the price will be dictated by these machines
Of course, this inventory management is what any good trader has always done, and some banks have worked on real-time data and analytics models that reflect the market in the present time. But the arrival of AI and machine learning has given them better forward-looking tools that can quantify those forecasts into their prices.
These techniques have been bread-and-butter for the large non-bank market-makers in recent times. It’s understood the large banks have dabbled in it as well over the years, and that smaller banks may look to take it up as the technology becomes easier to access.
What’s interesting are the time horizons that each group focuses on. Banks, for instance, tend to look at shorter periods such as 30 seconds, given internalisation can take them out of risk quickly, whereas the non-bank market-makers concentrate on longer timeframes owing to their greater appetite for this inventory risk.
Furthermore, when market volatility is much higher – like we saw last month – models that focus on patterns within much shorter timeframes can be stable.
On the bank side, though, the question also is how automated can this be? Dealers are understandably wary of allowing AI to take live decisions that affect pricing, but manual checks aren’t really suitable for such brief time horizons.
Some of the more advanced banks have set up internal teams dedicated solely to developing these kinds of predictive trading models.
But even at those banks that are yet to reach the point where they can take full advantage of AI, e-traders have been deploying it in plenty of other front-office areas.
Some look to AI to assess the price sensitivity of certain clients and then suggest to the human sales trader how the price configuration should be calibrated to that specific client. It’s then up to the human to go away and work out exactly how to adjust the client’s pricing.
A bank can also use AI to forecast expectations of demand. For example, if a corporate or asset manager client tends to trade EUR/USD at the same time of day, in the same size and for the same tenor, this gives the bank confidence about how to plan and adjust in expectation of these flows.
Ultimately, AI models have tremendous potential to become a bigger part of banks’ pricing engines. But there are a lot of unanswered questions about how much of the price will be dictated by these machines and whether any banks will be bold enough – or comfortable enough – to fully utilise them.
In just a few years, Wells Fargo’s FX volumes in Asia have strengthened and, with a robust FX product offering, the bank expects to onboard more clients eager for cross-currency swaps and customised pricing
While being a relative newcomer to the FX landscape in Asia, Wells Fargo has long served a clientele in the fixed income space in the region. But, by launching a pricing engine at Singapore’s SG1 in the summer of 2024, the firm demonstrated its commitment to Asian FX by distributing prices to its clients directly from the region. The bank can now deliver low‑latency customised pricing to its clients across the entire Asia‑Pacific (Apac) region.
Vincent Hindman, Wells Fargo
“Having a colocated e-FX pricing engine in Singapore has been hugely helpful for the build-up of our FX business in Asia,” explains Vincent Hindman, global co-head of macro at Wells Fargo. “Given the needs of our clients in the region, it was essential for us to have a low latency and customised pricing stream that we could deliver to clients – particularly for those that are high volume and systematic in nature, where latency makes a big difference.”
In setting up its FX business in Asia, Wells Fargo’s strategy was to focus – first and foremost – on Group of 10 products and expand from there as opportunities arose. The plan proved to be extremely successful as volumes more than doubled in its second year of operation in the region (2023), before growing an additional 55% in 2024, with cross‑currency swaps being a central component of that growth.
“We have become one of the liquidity providers of choice in G10 swaps within the Asian time zone as we actively make markets in large sizes for various client types, including banks, central banks and hedge funds,” says Hindman. “We’ve taken market share from our competitors in all segments and are now top three in the global cross-currency swaps space. Our offering is truly differentiated in the FX marketplace and has been extremely well received by our clients.”
Wells Fargo’s success in the Asia FX space stems from its large institutional client network, which spans 17 countries in the Apac region. Extending its offering to include FX was a natural progression for Wells Fargo as many of its clients were already actively trading dollar‑denominated fixed income products with the bank. Providing currency trading and hedging was a logical complement to these existing activities.
Mandy Wan, Wells Fargo
“In 2024, we onboarded a very large percentage of our fixed income clients to our FX offering,” says Mandy Wan, head of markets, Apac, and co-head of corporate and investment banking, Apac, at Wells Fargo. “Because we already had relationships with these large institutions here in Asia, we felt that adding an FX offering would bring great value to our clients. It was a tremendous opportunity for Wells Fargo to be more relevant to our clients in Asia. And, given the current uncertainty in the markets, our ability to provide diversified solutions and products is welcomed by our clients.”
Recycling risk flow
Wells Fargo started trading with many new counterparties in 2024 across the institutional client spectrum and services clients in the region with large dollar hedging and funding needs. Many institutional clients in Asia look to the US market for investments and funding, given the fragmented nature of the Asian market and the limited size of most local markets. But, to do so safely, these market participants often look to hedge their dollar exposure through FX instruments such as cross-currency swaps, which has been very popular with Wells Fargo’s clients.
“To win business in Asian FX – especially in the G10 market – we were very intentional in areas we would like to compete in,” says Wan. “We want to be competitive and relevant to our clients. We leverage the strength of our global franchise where we could recycle risk flow between regions: clients in Asia with dollar requirements on one side, and US corporate and institutional clients with interests in Asia on the other.”
The next phase of Wells Fargo’s FX venture in Asia will be to ramp up its involvement in the emerging markets currency space, as well as FX options that the bank started trading in the region in early 2025.
“Now that we are further along our FX venture in Asia and are being recognised for our progress in the G10,” says Hindman, “our goal is to continue down that product spectrum to become more relevant to our client base in the US and Asia by building out our emerging markets currency and FX options capabilities.”
Wells Fargo was named FX house of the year G10 (Asia hours) at the FX Markets Asia Awards 2025.
Service expanded its API offering to integrate broker chats into banks’ engines for cash FX pricing late last year
Bank trading desks are optimistic that initiatives by Bloomberg to open its chat application programming interface (API) will significantly help them price bilateral foreign exchange trades more accurately – and help bridge the gap between voice and electronic market-making.
In certain currencies and cash FX instruments, where electronic liquidity on trading platforms is thin, interdealer brokers (IDBs) are still the main way for banks to gather prices and connect to the market.
But sometimes, a
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Price gaps in spot FX markets could be exacerbated by algos trading on headline risk
The increase in sudden large movements in foreign exchange spot markets driven by President Donald Trump’s chaotic tariff announcements may be being amplified by market-maker pricing algorithms reacting to changes in intraday volatility and limiting how much risk they can take on, say some dealers.
As spot FX market flows have moved to electronic channels over the years, liquidity providers (LPs) increasingly rely on pricing algos to react to market news and set bid/offer spreads. An estimated 75
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Banks, the buy side and brokers are increasingly adopting automation from the beginning to the end of trading workflows, to optimise execution speed and efficiency. This evolution has been particularly evident in foreign exchange trading, where algorithmic decision-making has become more prevalent across desks, driven by the need to respond instantly to market data shifts.
Foreign exchange trading workflows, in particular, have seen rapid adoption of algorithm-driven decision-making across desks
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A growing number of market-makers and traders say price discovery for several key G10 currencies has already shifted to CME’s foreign exchange futures markets and away from primary venues for certain pairs.
In particular, some senior figures say futures contracts have become the primary pricing reference point for the Commonwealth currencies – Australian dollar, Canadian dollar, sterling and the New Zealand dollar – instead of the over-the-counter spot market on the London Stock Exchange Group’s
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Citi is deploying its new-look single-dealer platform (SDP) to emerging markets in a bid to offer better and faster electronic onshore pricing to users through a single gateway.
Following the relaunch of Velocity 3.0 in 2023, the US bank has focused on consolidating its various electronic foreign exchange pricing platforms – including CitiFX Pulse, its execution and treasury management platform for corporate clients – into a single application programming interface (API).
“A key deliverable for us
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The London gold market is facing a mounting structural crisis.
Bullion banks that lend precious metals to producers and each other moved over 200 tonnes of gold to New York in January in response to US President Donald Trump’s tariff threats, leaving the London market desperately short of physical supplies.
Dealers in London routinely borrow gold to fund customer positions. The cost of doing so, which has historically ranged from -20 basis points to +10bp, shot up to between 300bp for short
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Calculation agents will play a bigger role under the International Swaps and Derivatives Association’s new foreign exchange definitions, while the definition of “impossibility” in disruption events will also be updated.
Isda is updating its rule book for trading FX derivatives, after the Russian invasion of Ukraine in 2022 exposed flaws around the management of disruption events and disrupted price sources.
Deepak Sitlani, a partner in the derivatives and structured products group at law firm
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Every day, thousands of bilateral trades in cash and derivative instruments are arranged via Bloomberg instant messages on the tech firm’s ubiquitous terminals, with parties sending out requests for quotes (RFQs), haggling over prices, and exchanging market colour.
However, the process is often cumbersome, requiring salespeople to manually cut and paste information between chat windows. This slows down pricing and results in patchy data capture.
But moves by Bloomberg to offer automated RFQ
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Martial law presented first test for reforms aimed at boosting deliverable KRW market
Foreign exchange activity in the wake of South Korea’s political crisis indicates recent reforms aimed at bringing more Korean won trading onshore and away from the offshore non-deliverable forward (NDF) market are beginning to have an effect, dealers say.
The Korean won sunk to a succession of historic lows against the US dollar last month amid political turmoil sparked by President Yoon Suk Yeol’s surprise attempt to impose martial law. In late-hours trading on December 3, the gap between US
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