THETRADETECH DA I LY
news review
THE OFFICIAL NEWSPAPER OF TRADETECH 2019
Big data facing growing scrutiny
to mitigate ‘bad data’ risks
BIG DATA IS COMING UNDER GROWING SCRUTINY IN CERTAIN QUARTERS, AS THE RISKS OF BAD DATA COULD HAVE AN
ADVERSE AFFECT ON MACHINE LEARNING TOOLS.
F
inancial services have embellished the
concept of big data analytics, leveraging it
alongside artificial intelligence (AI) technology
and machine learning to identify investment
opportunities, detect erroneous behaviour or
emerging risks, reduce operational inefficiencies
and even predict behavioural patterns or the
future needs of clients. While application of big
data has yielded some positive results, namely
reducing settlement fails and mitigating frauds,
its wider use is coming under growing scrutiny
in certain quarters.
Big data analytics can help organisations
identify trends but its accuracy in some instanc-
es is spurious. The proliferation of big data is
indisputable but a large proportion of it is fake
or manipulated, suggesting the insights being
acquired from vast data lakes may be mislead-
ing.
Furthermore, a study of 800 bankers by Ac-
centure found more than half of organisations
appeared not to be doing enough to validate
the authenticity of their data, despite 80% of
respondents basing their most critical strategy
decisions on that very data.
Mitigating bad data risks will require firms to
ensure they have a solid oversight of the origin
of the information which they are processing
and this should be followed up by cross-exam-
ination of the insights with other data variables
and metrics to ensure its accuracy. Institutions
have also been advised to conduct more in-
depth due diligence on data providers to check
their sources are credible and legitimate partic-
ularly in light of GDPR (General Data Protection
Regulation) and growing concerns about privacy
rights.
“Having privileged access to data is of strate-
gic importance – and transparency is important
here too. Larger institutions – with sizeable
budgets and in-depth capabilities – typically
have more robust processes when verifying
where data comes from. However, the prolifera-
the data providers,” said Matthias Voelkel, a
partner at McKinsey.
Leading academics have also voiced doubts
about machine learning tools being used to
analyse big data sets. An academic, speaking at
the American Association for the Advancement
of Science, warned machine learning tools,
combing through big data sets, were producing
unreliable results, as the technology was only
detecting patterns in those specific data sets
and not the wider world. The academic added
mistakes by AI tools had not been spotted until
subsequent analyses of different data sets
revealed conflicting results.
“The application of machine learning in finan-
cial services in general – and capital markets
and securities services more specifically – has
an ever-growing number of positive use cases,
principally around data extraction and cleansing,
scanning and structuring documentation, and
answering customer queries, all of which will
bring efficiencies as well as better service. While
there is a lot of potential upside to adopting
machine learning more widely, firms do need to
be sure that the data they input into machine
learning applications is accurate and its origins
can be traced,” continued Voelkel.
In financial markets, bad data can be danger-
ous. If a traditional asset manager or private
equity firm bases an investment decision on
poorly constructed AI-driven insights derived
from sloppy data, losses could transpire. At
larger trading houses, those losses might even
have systemic implications. In response, more
institutions are becoming increasingly hesitant
about relying excessively on big data tools when
“Having privileged access to data is of strategic
importance – and transparency is important here
too. Larger institutions typically have more robust
processes when verifying where data comes from.”
MATTHIAS VOELKEL, MCKINSEY
tion of data – which is often derived from new,
alternative sources – has forced organisations to
up their game in terms of checking the accuracy
and validity of data, as well as the reliability of
performing investment analytics, with a greater
number of firms now stressing the importance
of human intermediation in some of these
processes.
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