treasury teams can be relieved from
performing mundane, tedious tasks,
allowing them to be a more strategic
player and operate in new and more
effective ways.
ML and treasury
management systems
Cash forecasting is a key priority
for most organizations. With large
volumes of data, detecting historical
trends and patterns can be impossible
for the human eye. ML can help them
navigate through all this information
to automate and improve cash
forecasts.
There can be a lot of internal or
external events at an organization
that can instantly require them to
change their cash forecasts. For
example, events such as a company
merger, an unexpected drop or rise in
sales numbers, or an unanticipated
event or huge surplus in the coming
months. While the machines are
providing the cash forecast on
historical data, at a certain point the
treasury team needs to communicate
with the machines to add insider
and current day information to
produce the best outcome for the
forecasts. ML can expedite the cash
forecasting process. Instead of
having many people in subsidiaries
creating manual and probably
biased forecasts, the AI will create
a forecast for the whole group in
seconds based on the data. The AI is
not only faster, but more accurate.
Additionally, organizations may use
ML algorithms to validate, and cost-
effectively implement a cash forecast.
This is just one example. We foresee
additional use cases that could
dramatically reduce implementation
costs and time to market of a TMS.
Are the robots taking over
Treasury?
Human treasury will not be replaced
by machines and robots in the near
future. It is the humans that are
interpreting and understanding data
and what is working with multidimensional volume-based tasks
such as payment or hedging and
developing new strategies, not the
computers. The human should be
less involved with repetitive, manual
tasks and will be supported by AI
to help them get more insights. We
should embrace what we can learn
from new insights that AI brings
and use that information to improve
our own performance. Ultimately,
the combination of machines and
humans will collaborate to produce
better results, each bringing their
superior skills to the partnership.
Preparing for the future
Digital revolution – we’ve all heard
about it and about the significance
for the future of treasury systems.
Many technology providers have the
vision to apply the power of AI and
ML to help treasury professionals
become more effective and add more
value to the businesses they serve.
At ION Treasury, the future is here.
In 2019, ION was the first to provide
its customers with ML capabilities
directly within the TMS, marking a
significant innovation milestone in
TMS technology. This work has been
years in the making and is the result
of a long-term partnership with a
major university, machine learning
experts, and the ION Treasury
community.
We are proud to take a leadership
position, and to be at the vanguard
of providing machine learning
capabilities in TMS solutions. And
we believe the future of treasury has
never looked brighter.
iongroup.com/treasury
treasury@iongroup.com
2019, ION is the first to provide its customers
with machine learning capabilities directly
within the Treasury Management System.
Definitions:
Artificial Intelligence (AI) is the study of how to make
computers do things at which, at the moment people are
better. (Elaine Rich, 1983)
Machine Learning (ML) is a subset of AI and the ability
to learn without being explicitly programmed
Deep Learning (DL) is a subset of machine learning
and is inspired by the function of the human brain
(neural network)
ARTIFICIAL
INTELLIGENCE (AI)
DEEP
LEARNING
(DL)
