Dave Essex: I'm David Essex, editor at large at
SearchERP, discussing AI and finance with
consultant Kimberly Ellison-Taylor. She's a former
chair of the Association of International
Certified Public Accountants, who has held
leadership roles in finance and IT at NASA,
Motorola, KPMG, and Oracle. To hear the full
interview and learn more about the use of AI in
finance, click the link above or in the
description below. Are there specific applications
that you feel are particularly far along now or
some of the more advanced uses of AI in Finance?
Kimberly Ellison-Taylor: Good points. I think that
so when I'm talking with -- so I wear two hats,
David, you might be aware that. So I wear this
finance hat and I wear this technology hat. So I'm
with when I'm with my CIO colleagues, I say, we
really need finance to start leading these areas,
because technology is an enabler. And we don't
want technology to be given a black eye because
it's not meeting whatever the mission objectives
are. But what it ultimately is just a tool. And so
to answer your question, when I go and speak with
a finance colleagues, I am talking about Okay, so
how is it helping you improve your decision
making? How is AI helping you to be more assistive
and answering questions and being responsive to
your clients, internal and external. So I do see a
lot of it happening with chat bots, and digital
assistants, I see us using AI as it relates to
risk and identifying risk, because we don't like
that F word, just fraud. So we're already going to
be attuned to looking for anomalies and thresholds
that are exceeded, because they can be triggers to
indicate that something's happening that shouldn't
we see AI also being used to help with credit
decisions, helping with whether or not we have the
right reports in place and, and other thresholds
to help us assess risk and manage risk at the same
time. But also, we see more and more of it during
this whole and I you know, financial planning, and
whether or not we can do cash liquidity
assessments and analysis as it relates to
investments and using AI to supplement the
insights that we could bring. Because there's no
way that any one person could analyze the wealth
of data that's out there. And I think our finance
colleagues know that. So even as I talk to the
CIOs, I'm talking to the CFO saying, we can lead
these projects, because technology is an enabler,
but you have the vision, the business oriented
mission, that it needs to really add context to
why we're doing it in the first place.
Dave Essex: There's certainly a growing awareness
about using AI in other areas like HR, and that if
you do that, that can be a black box problem, you
know, that the notion that you don't really know
how the AI is doing what it does, whether you can
trust it, how you going to understand it. And so
part of the reaction of that a big reaction of
that is this push for Explainable AI. And also
like algorithmic auditing, to make sure the AI is
working within certain parameters that the company
sets and certain legal parameters in some case. So
I'm wondering, Kimberly, is there also a blackbox
problem in using AI and finance, that has to be
solved?
Kimberly Ellison-Taylor: Absolutely. And I would
say that because from an algorithm perspective,
when the external auditors come in, before they
even get there, the internal auditors are on deck,
and understanding how the process actually
happens, the logic that is applied, and what
happens when exceptions occur, absolutely must be
determined. And I would say the controls around it
have to be tested, especially when we're talking
about this heightened cybersecurity environment.
So it is important to know how we got the answer
and how the processes are occurring. But it's also
important to understand from a third party
perspective, who can even make changes to the
algorithms, because that's a whole internal
controls environment that we need to investigate
as well. And I suspect depending on whether it's
our financial controls or not financial reporting,
it'll be definitely something that we're looking
at for, you know, Sarbanes Oxley or something when
we're looking at management assertions over the
integrity of the financial reporting. Or if
there's something that goes wrong, it might be
part of the evidence showing that happens when
people go back and try to figure out what happened
and So we I think, increasingly are going to hear
more and more about this. I don't know exactly how
or what. But it just makes sense to me, because we
were auditing at one point around the computer.
And now we're going through it because we have
experts that come on board. I have no doubt we'll
have people who are experts in data lakes and all
kinds of data models and data analytics that can
assess the integrity of the systems that are
supporting, you know, those data environments can
also the same thing for AI and algorithms. We
definitely should be paying attention because with
great power comes great responsibility.