THETRADETECH DA I LY
in-depth
THE OFFICIAL NEWSPAPER OF TRADETECH 2018
TECHNOLOGY
Artificial intelligence must
address transparency concerns
to realise benefits
INDUSTRY EXPERTS WERE KEEN TO EXTOL THE VIRTUES OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING, BUT PERCEP-
TIONS AROUND TRANSPARENCY MUST BE ADDRESSED FIRST.
A
rtificial intelligence (AI) has become one of
the key watchwords in asset management
technology in recent years, with institutional
and boutique firms alike having made signifi-
cant investments in this space.
Panellists were also keen to talk up how AI
and its various subsets can optimise trading
processes and its theoretical benefits to the
wider industry once further development efforts
have been made.
Vanguard’s head of investment operations
for the EMEA region, Sean Kennedy, outlined
the various forms that AI-based technology
can take, including sub components such as
machine learning, robotic process automation
(RPA) and deep learning.
“The real value we have seen in application in
other industries has been to look across func-
tional areas of the full life cycle, in our case trad-
ing. So we are spending time at the moment
looking at the ways to apply machine learning to
optimise the entire life cycle,” he said.
“What we are seeing at Vanguard and also the
whole industry is that what have traditionally
been middle office functions are becoming more
closely integrated with the front office, so those
lines are becoming very blurred. The opportunity
there around new technologies is to start weav-
ing them together through machine learning to
drive optimisation.”
Sanoke Viswanathan, chief administrative
officer at JP Morgan CIB, highlighted the institu-
tion’s use of natural language processing – the
application of computational techniques to the
analysis and synthesis of natural language and
speech – in its research space, for functions
such as sentiment analysis and news analytics.
“Probably the emerging area where there is a
lot of time spent but not a lot of yield is what
we call auto-decision making; robo-trading or
robo-hedging, coming up with automatic in
ways in which to answer client queries and the
like. That’s an application taxonomy that I find
resonates well with end users because that’s
the way people can decide how they want to
deploy these techniques,” Viswanathan said.
However, despite the proven and potential
benefits that AI and its technology subsets can
offer, there are still areas of concern that must
be addressed before the industry can be com-
pletely comfortable with the technology.
“We are not satisfied with the level of funda-
mental research in AI that is focused on financial
markets. In discussion with clients, on the types
of issues we are dealing with, there isn’t enough
core research going on in areas such as market
simulations, time-series predictions and things
like that. So we want to set up a research capabil-
ity that is focused on that,” Viswanathan said.
Kennedy highlighted one of the main obsta-
cles to the further adoption of AI technologies
as a lack of transparency and trust in how these
systems operate, highlighting the difference
between shallow and deep learning techniques.
“Shallow learning is essentially creating small
models or computations that you can go back
and review, or even watch in real-time to see
the input and output, and essentially justify
the output through being transparent,” said
Kennedy.
“In deep learning you lose transparency. Infor-
mation goes in, a bunch of computations take
place and the system trains itself to learn, and
out comes the output, which in its ideal state
is used to drive decision making. That’s where
I see the majority of hesitation, which can be
challenging.
“There are plenty of applications of the tech-
nology that are far more advanced than we use
in this industry, but getting regulators, clients
or even users internally to trust that type of
output and march forward using it seems to be
the real challenge.”
Speaking at a separate session at the confer-
ence, T. Rowe Price’s global head of systematic
trading and market structure, Mehmet Kinak,
suggested that AI was a “great buzzword” for
the industry but he had yet to find any organisa-
tion that developed a good system.
“Machine learning on the other hand is inter-
esting, like a broker wheel for example. It incor-
porates a lot of transaction cost analysis (TCA)
and data into the