TECHNOLOGY
Y
ou can gain some measure of how im-
portant artificial intelligence (AI) is to
today’s banking industry by counting
the number of conference sessions that were
devoted to it at Sibos 2018. At least 27 sessions
covered the topic, which was more than those
looking at application programming interfac-
es (APIs) and definitely more than quantum
computing.
As Dr Ayesha Khanna, chief executive of
Singapore-based incubator and advisory firm
Addo AI Services, told Sibos delegates: “AI is
one of the core pillars of the fourth industrial
revolution.” It would seem that AI is, at last,
coming of age and as someone who studied
and worked in AI in the lean years of the
1970s and 1980s, I find this particularly grati-
fying. But is this yet another false dawn?
Jörg Hessenmüller, head of group digital
transformation at Commerzbank, told Club@
Sibos that AI will help the bank to make use of
the huge amount of internal and external data
it collects and analyses to provide clients with
tailored offerings. “Already today machine
learning plays an important part in our busi-
ness. At fraud detection we use self-learning
algorithms. The success ratio of 99% for the
prevention of CEO-fraud, we stopped illegal
transactions for our customers with a volume
of more than ¤100 million.”
The promise of a true mechanical intelli-
gence is almost as old as computers them-
selves. Alan Turing predicted in 1950 that
by the end of the century people would
speak of computer “thinking” without fear
of contradiction and that people would take
computers for walks in the park and exchange
witticisms with them. But we soon discovered
that these wonderful new machines were
excellent at summing columns of numbers but
utterly floundered when trying to understand
a simple typed English sentence. Computer
chess – a popular challenge for the pioneers,
many of whom played the game themselves –
was, to put it kindly, very weak at best. Worse,
it turned out that everyday activities that most
human beings can do, such as visual process-
ing and navigating around in the real world,
were simply impossible for an automaton.
Then in the 1980s, along came expert
systems. The expert system had limited
scope. There was no robotics, vision, voice
recognition or other fancies, just a generic
application which could be trained by an
expert (a doctor, lawyer or financial adviser,
for example) to do a specific expert task. Once
trained it would be available for all time, tire-
less, unerringly accurate and, of course, much
cheaper to “run” than the human expert who
trained it. Some of today’s machine learning
systems have a similar approach. But in the
end there were only a handful of successes,
mainly because the experts found them too
difficult to train. Basically their expert rea-
soning couldn’t be expressed using the limited
ad hoc user interface provided by the expert
system itself.
Club @ Sibos
After these earlier failures it would seem
that, at last, there is good reason today to be
optimistic for the future of AI and, along with
many other industries, it will have a consider-
able impact on banking.
First, there is Moore’s Law, which states
that every 18 months computer power – both
processor speed and memory capacity –
doubles. That may not sound so spectacular
but over the decades the cumulative effect is
enormous. For example, the inexpensive and
unexceptional notepad computer on which
I’m typing this article has 2000 times as
much memory as and 300 times the proces-
sor speed of the state of the art DEC PDP-10
which I used in time-sharing mode when a
“AI is one of the core pillars
of the fourth industrial
revolution.”
AYESHA KHANNA, ADDO AI SERVICES
research student at Edinburgh University’s
Department of Artificial Intelligence in the
late 1970s. Imagine: 300+ DEC-10s, no longer
housed in air-conditioned fortresses, but
dumped in my day bag alongside my lunch
box! I’ll spare you a detailed comparison of
the old DEC with a real research tool, such
as IBM’s Summit super-computer, where the
figures are truly eye-watering but, in brief, the
Summit is billions of billions of times bigger
and faster. And with that amount of grunt,
even a stupidly naïve AI program can start
to look somewhat smarter and maybe even
achieve something practicable. We’ve seen
in part what raw processor power can do for
computer chess – from bumbling amateur
in the 1970s to world championship level in
1996.
Second, we are now in a digital world. If
the expert systems of the 1980s had all that
data to draw upon, things might have been
different but, at the time, there was nothing,
only what was lodged in the expert’s brain.
Nowadays practically all information is avail-
able in digital form – words, pictures, sounds
and knowledge along with proprietary data
held in state and company archives. For exam-
ple, machine translation now works – albeit
crudely at times – thanks to the vast body of
(human) translated text available online. And
it turns out that no great insight into human
language processing is needed – in essence a
translation system merely finds a close match
of the supplied text with something in the
immense archive of translated works created
over the decades by human translators.
Finally, we have machine learning (ML) sys-
tems. ML, especially in the shape of artificial
neural networks was once much discredited
by mainstream AI researchers. Now, extraor-
dinarily, the networks have been found to
work and work very well. A recent spectacu-
lar breakthrough was a deep neural network
learning system called Alpha-Go, which beat
the world champion Go player, Lee Sedol. Af-
ter his defeat he commented on the machine’s
winning move: “I’ve never seen a human play
this move. So beautiful.” The game of Go was
previously thought to be out of reach of AI
but Alpha-Go’s extensive use of ML and its
ultimate success shows how powerful this
technique is.
The financial sector is pursuing many
possible ML applications. One area of interest
is fraud detection. The theory is that given
enough sample data of fraudulent and honest
activity, a machine can learn how to differ-
entiate between the two and thence be of
practical value in fraud prevention. Such a
system might, for example, by itself discover
Benford’s Law. Benford was there first though
(in 1938) with his discovery that in many real
numeric data sets, numbers starting with the
digit “1” predominate, followed by “2” and so
on down to “9” which is seven times less fre-
quent than “1”. Application of this law could
easily have caught Bernie Madoff, whose
returns data on his funds didn’t match the
Benford pattern (because, as we now know,
Madoff was just making them up). This high-
“AI is not disrupting tomorrow.
It is disrupting today.”
TOMER GARZBERG, GRONADE
lights another issue in this area. A fraudster
who knows about Benford’s Law can easily
make up fraudulent figures that comply with
it so, in the long run the fraud prevention sys-
tem needs to be one jump ahead or, perhaps,
be incomprehensible in its reasoning – a fea-
ture that is often thought to be a disadvantage
in AI financial systems.
It is still early days for AI in banking but
given the level of interest it is likely that the
financial world will see more and more AI
applications going live in the near to medium
future. But Tomer Garzberg, chief executive
of Gronade, an enterprise growth lab and
future of work company cautions: “AI isn’t
a free thinking sentient machine. There is
not enough computer power in the world for
that.” Instead, he says, AI can be seen as a
marketing term, an umbrella statement for a
group of technologies that sit underneath it.
There are many active areas besides fraud de-
tection, for example, anti-money laundering,
hyper-customisation of services and identi-
fication of untapped business opportunities.
Natural language understanding has a part to
play too, allowing non-technical employees
to access data analytics insights or combined
with voice recognition as a facility in call
centres. I will leave the last word to Garzberg:
“The way we work is changing. AI is not dis-
rupting tomorrow. It is disrupting today.”
www.clubsibos.com | CLUB@SIBOS | 17