sidhellman/clean_data.txt · Datasets at Hugging Face

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extensive innovation
algorithms that beat passive asset selection.26 Risk
management is another field where generative AI is
seeing use as an advanced research tool that goes
beyond its current rollout. Use cases here include trying
to better understand asset correlation27 and tail risk,
among others.28
While much of the abovementioned activity has
occurred in academia, some companies are
interested as well, at least in principle. UBS, for
example, is researching the use of generative AI
for trading applications. One compelling avenue is
using the technology to express news, in the form of
unstructured text, as a numerical vector in order to
assess its impact on asset prices.
The companys preliminary results show promise in
improving the ability to forecast volatility changes
driven by incoming news. Meanwhile, a blue-chip Wall
Street firm has applied for a trademark for what it
hopes will be a tool that will advise customers on stock
selection.29 In practice, however, Briest observes that
the banking industrys restrained approach reflects the
one being taken across the financial services industry
as a whole. The sector is relatively conservative in
adopting new technological trends, he says.
Risk management is another field where generative AI
is seeing use as an advanced research tool that goes
beyond its current rollout.
16
MIT Technology Review Insights
05
05
W
hen grappling with the challenge of
adopting new technologies,
companies often have to tackle the
confluence of legacy technology and
a tight labor market.
Legacy technology
Financial services companies, especially banks, were
among the early adopters of IT decades ago. Choices
made then, though, have long resisted further change.
The most striking example of this phenomenon is that,
as late as 2017, 43% of banking systems relied on
a six-decade-old computer programming language,
COBOL, which was also behind 80% of credit card
transactions and 85% of ATM activity.
Typically, COBOL drove large mainframe computers
because it was the only option decades ago. Although
such arrangements have provided substantial stability,
they make it difficult to add new capabilities arising
from more recent technological developments.30
COBOL encapsulates the broader legacy-technology
deficit in the financial sector. Its a problem that
encompasses old software and siloed data storage
arrangements that have evolved to meet challenges
across decades but are no longer fit for purpose.
Two general
challenges for new
technology adoption
According to an Accenture survey of large banks, even
though the respondent pool consisted of companies
interested in cloud usage, only 31% had moved more
than half of their previous mainframe activity to the new
platforms.31 A lot of banks maintain old IT systems, says
Briest. Were hearing from technology companies about
a lot of pilot projects starting and companies moving
quite quickly to the next step, but this is going to take
some time. Its an observation shared by Chia. Most
financial services organizations have a lot of data that is
usually poorly structured or even fragmented, he says.
Despite this enduring challenge of legacy IT for many
companies, the problem has been diminishing across
the industry because of extensive digitalization in recent
years. A lot of financial services firms have invested
heavily in digital transformation, says Chia. Most have
gained a certain capability in data management and
theres already a level of fundamental readiness in terms
of technology investment.
One of them is RCBC, which was established in 1960.
The past three years have been pivotal for our digital
transformation, says Villanueva. The introduction and
expansion of generative AI solutions will be smooth
and easy. Meanwhile, new entrants do not have a
technological deficit to overcome. Mileham says that
More generally, companies have a big opportunity to
use generative AI to accelerate the shift off some
legacy applications that maybe it was just cost-
prohibitive to consider previously.
Michael Briest, Head of European Technology Research, UBS
17
MIT Technology Review Insights
Betterment, as a cloud-native company, can deploy
generative AI as broadly as it sees a use for it relatively
quickly. Im confident that major cloud providers are
going to be able to productize these capabilities and
expose them to companies very efficiently, he says.
Cont also says that he believes that financial companies
are, overall, pretty ready to make use of generative AI.
Even those who currently are not in such a state may

extensive innovation

algorithms that beat passive asset selection.26 Risk

management is another field where generative AI is

seeing use as an advanced research tool that goes

beyond its current rollout. Use cases here include trying

to better understand asset correlation27 and tail risk,

among others.28

While much of the abovementioned activity has

occurred in academia, some companies are

interested as well, at least in principle. UBS, for

example, is researching the use of generative AI

for trading applications. One compelling avenue is

using the technology to express news, in the form of

unstructured text, as a numerical vector in order to

assess its impact on asset prices.

The companys preliminary results show promise in

improving the ability to forecast volatility changes

driven by incoming news. Meanwhile, a blue-chip Wall

Street firm has applied for a trademark for what it

hopes will be a tool that will advise customers on stock

selection.29 In practice, however, Briest observes that

the banking industrys restrained approach reflects the

one being taken across the financial services industry

as a whole. The sector is relatively conservative in

adopting new technological trends, he says.

Risk management is another field where generative AI

is seeing use as an advanced research tool that goes

beyond its current rollout.

16

MIT Technology Review Insights

05

W

hen grappling with the challenge of

adopting new technologies,

companies often have to tackle the

confluence of legacy technology and

a tight labor market.

Legacy technology

Financial services companies, especially banks, were

among the early adopters of IT decades ago. Choices

made then, though, have long resisted further change.

The most striking example of this phenomenon is that,

as late as 2017, 43% of banking systems relied on

a six-decade-old computer programming language,

COBOL, which was also behind 80% of credit card

transactions and 85% of ATM activity.

Typically, COBOL drove large mainframe computers

because it was the only option decades ago. Although

such arrangements have provided substantial stability,

they make it difficult to add new capabilities arising

from more recent technological developments.30

COBOL encapsulates the broader legacy-technology

deficit in the financial sector. Its a problem that

encompasses old software and siloed data storage

arrangements that have evolved to meet challenges

across decades but are no longer fit for purpose.

Two general

challenges for new

technology adoption

According to an Accenture survey of large banks, even

though the respondent pool consisted of companies

interested in cloud usage, only 31% had moved more

than half of their previous mainframe activity to the new

platforms.31 A lot of banks maintain old IT systems, says

Briest. Were hearing from technology companies about

a lot of pilot projects starting and companies moving

quite quickly to the next step, but this is going to take

some time. Its an observation shared by Chia. Most

financial services organizations have a lot of data that is

usually poorly structured or even fragmented, he says.

Despite this enduring challenge of legacy IT for many

companies, the problem has been diminishing across

the industry because of extensive digitalization in recent

years. A lot of financial services firms have invested

heavily in digital transformation, says Chia. Most have

gained a certain capability in data management and

theres already a level of fundamental readiness in terms

of technology investment.

One of them is RCBC, which was established in 1960.

The past three years have been pivotal for our digital

transformation, says Villanueva. The introduction and

expansion of generative AI solutions will be smooth

and easy. Meanwhile, new entrants do not have a

technological deficit to overcome. Mileham says that

More generally, companies have a big opportunity to

use generative AI to accelerate the shift off some

legacy applications that maybe it was just cost-

prohibitive to consider previously.

Michael Briest, Head of European Technology Research, UBS

17

MIT Technology Review Insights

Betterment, as a cloud-native company, can deploy

generative AI as broadly as it sees a use for it relatively

quickly. Im confident that major cloud providers are

going to be able to productize these capabilities and

expose them to companies very efficiently, he says.

Cont also says that he believes that financial companies

are, overall, pretty ready to make use of generative AI.

Even those who currently are not in such a state may