AI Report Digital - Flipbook - Page 15
THE DATA PROBLEM
By: Kiah Lau Haslett
Most artificial intelligence applications that
financial institutions are interested in will use
internal data — and that could be an unwelcome
realization for institutions that don’t have formal
data management initiatives.
But to manage this risk successfully, Morrissey points out
that the relevant business line at the bank needs to be
involved. The IT team won’t be able to look at loan data and
ascertain what information is highly sensitive and what is
public. Lenders and credit analysts will need to share their
insights.
“I think even before [banks and credit unions] start to think
about the AI use cases and what [are] the right models and
technologies to use, the first thing they should get control of
is the data itself, and [whether they] have all the right data in
a usable format,” says Ashvin Parmar of Capgemini.
AI runs on a company’s data infrastructure: Computational
“The better the data is, the more
you can get out of it.”
power processes data via models and components that link to
an individual application or use case through connections like
Alexandra Mousavizadeh, Evident
application programming interfaces. If the data isn’t organized or clean, or if the technology underlying these connections, or rails, is “choppy” due to older, legacy or disparate
systems, then it’s harder to run the technology and get good
AI may also be able to assist here. Both Mousavizadeh and
results, says Evident CEO Alexandra Mousavizadeh.
Parmar point out that institutions can apply AI to their data
A data management strategy can help banks and credit
unions understand what data they have and what they will
need to acquire. An institution may realize that some of its
identified use cases may not be able to move forward if it
can’t validate the quality of the data, or if it’s not labeled and
reviewed by subject matter experts.
to clean it, standardize it and otherwise make it usable. AI
may be able to make sense of a fragmented data set or sift
through and strip out customer information that shouldn’t
be inserted into a model. Parmar says an institution may
even be able to juggle an AI project and a data management
project in parallel. But good data management — and good
data privacy and security protocols — are essential to truly
Financial institutions will need to closely consider their data
leverage AI’s capabilities.
privacy practices and safeguards. AI models may need to
train on or analyze an institution’s data, and organizations
are responsible for their intellectual property, including the
quality of the data, what information is in the data set and
making sure that data doesn’t leave. Institutions will need
to think about how they hide or remove personal identifying
information and mask or anonymize the data.
“The infrastructure is such a huge part of it; it’s impossible
to delineate one from the other. But you could definitely do
both at the same time. There are going to be some areas in
community banks where you can start using AI on subsets
of the data and build real capabilities,” Mousavizadeh says.
“It’s just [that] the better the data is, the more you can get
out of it.”
They could consider using a data classification system sorted
by sensitivity, public availability and market importance, says
Daragh Morrissey of Microsoft. Failing to manage this could
result in long-term reputational risk and loss of customer
trust, along with regulatory and compliance penalties.
ARTIFICIAL INTELLIGENCE: A REAL-WORLD APPROACH | 13
