e-Forex-July-23-HighRes-SCREEN

FINTECH
Cutting through the hype: Where AI is already making its presence felt in FX
In highly digitised markets such as FX Not only is it globally complex - FX
markets, AI algorithms can enhance does not correlate with isolated or
liquidity management and execution predictable events, but is rather
of large orders with minimal market impacted by random combinations of
impact by optimising duration and a lot of different factors.
order size in a dynamic fashion based
on market conditions. Traders can also “The massive amount of fast moving
deploy AI for risk management and data that is generated as a result
order flow management purposes makes AI analysis (and speed) highly
to streamline execution and produce suited to FX,” adds Carmody. “At the
efficiencies.
same time, the very liquid nature of FX
markets also favours the use of AI in
However, the OECD warned that order execution.”
the lack of explainability of AI model
processes could give rise to potential Meanwhile Alexander Culiniac, chief
pro-cyclicality and systemic risk, and technology officer at SmartTrade
could create possible incompatibilities Technologies also refers to the
with existing financial supervision complexity of the FX market as one
and internal governance frameworks of the factors that make it an ideal
- possibly challenging the technology environment for the application of
neutral approach to policymaking. artificial intelligence, in addition to
So what makes a complex global the huge volumes of data generated
market like FX - which has many and the speed with which the market
different participants reliant on
moves.
various technologies - a good fit for
AI? According to Tim Carmody, chief “AI’s ability to process vast amounts of
technology officer at IPC Systems, varied data in real-time, its predictive
the complexity and global nature of capabilities, and its aptitude for
the FX industry is the most important high frequency trading make it
empowering factor.
indispensable in this environment,”
he says. “Additionally, it enhances risk
“Even more so than other asset management by detecting potential
classes, FX is impacted by geopolitics market shifts, automates routine tasks,
and regulatory and business dynamics and carries out market sentiment
on a global scale,” he says.
analysis through natural language
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND BIG DATA IN FINANCE © OECD 2021
Examples of AI applications in some financial market activities
processing. Its adaptive learning
capabilities also allow it to respond to
dynamic market changes.”
However, Culiniac cautions that while
AI is a powerful tool, its effectiveness is
dependent on the quality of the data
it is trained on and it may not always
accurately predict unforeseen events.
EXPLOITING THE BENEFITS
One of the obvious questions in any
discussion of the use of AI in FX is
whether the buy-side or sell-side is
best placed to exploit the benefits
of AI and what factors will influence
utilisation of the technology.
Culiniac reckons this is something of
a moot point in that the distinction
between buy- and sell-side is
increasingly a grey area with both
taking and making to some extent.
“Sell-side platforms are naturally suited
to exploit AI given that they can access
multiple sources of information to
create proprietary data sets,” he says.
“Of course, there are considerations
around data ownership and licences.
However, the ability to access multiple
sources of information and derive
proprietary data sets gives these
players an advantage.”
“We see our sell side clients looking
at AI to provide clear actionable
data. Client management teams are
swamped by reports and analyses
these days - the issue is not that they
don’t have data, it is that they have
too much information.”
AI and machine learning tools
developed by SmartTrade enable banks
to see how clients are segmented
in ways no human can ascertain.
Once segmented, clients can be
targeted in terms of upselling and
marketing to make sure the bank’s
client interactions are relevant and
offer value. Further practical examples
of AI/machine learning logic include
looking at how client behaviour
patterns change in terms of how and
66 JULY 2023 e-FOREX