A expert panel discussion on the evolution, trends, and scope of technology in Treasury
The following are excerpts from the panel discussion on the topic ‘The Future of Digital Treasury’ which was conducted at Radiance 2020 held at the AT&T Stadium Dallas on the 5th of February 2020. The Treasury experts who participated in this panel discussion were –
The 60-minute discussion was attended by more than 100 treasury professionals and saw the panellists discuss the evolution of Treasury technology over the past decade, what the future holds for them. The focus will be on rising technologies such as Artificial Intelligence, automation and the disruptive impact these are already having on the way critical Treasury functions such as cash forecasting, cash management and risk management.
We’ve seen great changes in the Treasury over the last 10-20 years and it is
happening even more rapidly now than ever before. What do you think has been the
greatest change in Treasury over the last 5 years and what do you think is going to be
the greatest change in the next 5 years?
Treasury has become more business-focused and strategic. It’s getting away
from some of the day-to-day Treasury functions and focusing on things that add
value to the business, like working capital strategy, funding and liquidity strategies,
more robust foreign exchange, and interest rate risk management strategies. This
has been a road that many treasurers are increasingly taking, and it’s still evolving.
There will be some great changes in the mindset of Treasury functions in the next 5
years as we’re looking to evolve more while reacting to things that are going on in
the world, whether that be geopolitical or regulatory-driven.
For the last 5 years, I’ve seen Treasury come out of the doldrums after the
recession. A lot of treasurers did not have the budget to do things they wanted
to do until recently. Treasurers are keen on improving forecasting and their working
capital. I see a lot of organizations looking to do more with current staff with
automation, to get people out of the weeds of gathering data and compiling it so
that it can add value to the organization.
What I’m seeing is there really is a stronger focus on efficiency and controls,
efficiency so that people can do more within the limited hours. And in control, a lot
of automation these days is going towards avoiding bad stuff from happening. There
are ways to leverage technology to add control there. That’s my vision for what’s
going to happen over the next few years.
The position of Treasury groups is to take a more strategic role versus one
that’s more customer oriented. That’s always been the case in the last 5 years.
I see more trends towards supporting the business and part of that is this end-toend view. In the last 15-18 years, working capital has been near the top of the list of
what people are doing and more people are getting involved. So that involvement in
the organization is very healthy and fruitful.
Another area that’s taking place on the control front is the level of cyber-attacks and
sophistication of criminals, their yields are so much greater. This is requiring a
lockdown on not just data, but the whole payment process. So there’s a technology
Treasury has become much more risk management focused, not just with different
types of financial instruments, but in conjunction with organizations experiencing
globalization and creating more complexity. And that’s driving them to have a risk
managing orientation. But they’re also looking at it operationally where more people
are involved and reducing errors.
In the last couple of years, I think technology has enabled us to start earning
that strategic step at the table. I’m starting to see an evolution to AP and AR
coming back up to Treasury.
My next 5 to 10 years is aspirational. I want Treasury to take more control of the
cash conversion cycle. I want to teach everyone to be more social, more
collaborative, come out of their shells, and really earn that strategic seat at the
table. It’s time to take some action and actually lead across the enterprise, and
technology will help us do that.
There was some consistency there and your responses around control efficiencies,
technology. Here at HighRadius, we’re focused on artificial intelligence. How do you
think artificial intelligence specifically can be applied to areas other than AP?
We might start to see folks actually doing their borrowing decisions as we’re
able to forecast out the timing of our borrowings and also warnings about our
debt covenants. And so to leverage all that capital that we have access to is an area
that might be getting some traction at some point.
Artificial intelligence is good for determining what’s out of the ordinary and
what’s anomalous. That helps with fraud detection, error detection, quality
control, those are a couple of things that AI and machine learning are very excellent
at. So yes, when you have a payment process, it can monitor and detect some type
of anomalous activity, it can stop that it can call into play a payment interdiction and
pull those items out because someone else is doing something to the system.
Treasury in the data front could stop the exfiltration of data. But it also can be used
for quality control for transactions and what is happening.
We see many use cases within finance, AP, AR, and that’s sort of the evolution
of AI. There are some very sophisticated corporate treasuries that have the skill
sets and their analysts are able to build algorithms and do things. We see a little bit
of that in foreign exchange, risk management, and some other areas. The world
within Treasury is still evolving and can certainly benefit from the forecasting aspect.
Proactively, we’re seeing it used in forecasting. AI can help you with
forecasting, and everybody wants help with forecasting because it’s just no fun.
Treasury versus finance is a really interesting part if you’re in external audits.
They’re all putting AI in to reduce auditors at a massive level. It’s a very different
story for Treasury. Treasury has never been highly staffed and in the last 5-7 years,
Treasury has had systematic increases in staff. Treasury can leverage that technology
to do much more that’s always needed to be done.
I think there’s a clear difference between automation that helps do really repetitive
tasks. But beyond that, moving into where there are clear decisions that must be
made as a result of the data that you have, don’t you see applications not just around
fraud and looking for anomalies, but maybe in hedging, where given a policy, these
are the recommendations of the trades and policies that should be made and these
are the recommendations of the investment or debt decisions that should be made?
Do you see other applications of that throughout Treasury?
That’s where it’s certainly going in terms of those applications. But again, when
we’re talking about machine learning, AI is the ability to learn from exceptions in
the past and curate a smarter decision making. What we see is getting more of the
RPA and the automation. That’s not quite necessarily the learning aspect yet and
that’s evolving and developing. And again, I’ve seen a few cases, but those
applications will become far more popular in the years to come.
You applied a few drawbacks and didn’t get too excited about how fast this might be
moving. For all those people that are here working in Treasury, what does this mean
to them? What types of tasks are they doing right now that are probably going to be
entirely eliminated? And more importantly, what can they do to prepare for the
I did a study last year on how satisfied Treasuries were with their technology
and what we found was the best treasuries that were getting the best value from
their technology had really smart finance people who understood how to use the
technology and were able to apply it. And in hiring decisions these days when
people come to me and they say we’re looking for somebody, they’re looking for
someone who has a finance background, and a technology background, so they
need to have both. And if you’re trying to stay valuable going forward, you’ve got to
find ways to be the person who knows how to turn the knobs on the technology.
When you look at what AI or Robotic Process Automation can do to your
organization, then you need to look and say where is this taking us and how can
I fit in as I go forward? Can I leverage the technology, guide my team, and do things
that take advantage of the changing fields before us? Because otherwise, if you like
the manual keying-in information, or populating the database, adding stuff in, you’re
going to use Robotic Process Automation or bots to replace that. You need to learn
skills to control and program those things or leverage your unique intellectual
You really got to look at what are the skills that you need to find your career
success and then what are the skills you need to obtain. When I talk to folks that
are Treasury recruiters, I’m glad to hear it’s more about leadership, leading projects
collaborating, having an appetite to learn. Hopefully, with automation and AI, you’ll
have more time to do that. But like Craig was saying, you need to get your digital
skills up to speed.
At the end of the day, the best treasurer is going to mitigate the time the CFO
spends on non-value add activities. But we can start to take some of the strategic
stuff off the CFOs plate and then we can position ourselves to elevate the treasurer.
Just being flexible, learn whatever you can, whatever’s coming out new to
technology. Have an appetite to learn and engage people at your company and learn
what they do and what their challenges are and learn how you can help them and
you’ll build your brain within your company.
I think that’s very true, Ernie that being a partner to the business means knowing the
business, getting out and getting involved in what it is that your company does. But
also being able to interpret data. I mean, right now we’re definitely in a data phase.
Every company is looking for ways to harness data better. But the people who are
most valuable are the ones who can help interpret what this data is telling us, what
decision should we make differently, and how are we going to use that data.
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