Treasury and Risk - October 2019 Special Report

via spreadsheets. FX exposures were determined by pulling together spreadsheets from various businesses and then aggregating and analyzing that data, much of which originated in enterprise resource planning (ERP) systems and other finance and accounting applications.

This approach often raised troubling questions, Gage notes, such as ‘Do we have all the data we need?’ and ‘Is the data we have accurate?’ “Once they retrieved all of that information, treasury people were not confident that they had the right data,” he says. Analyses of inaccurate data could distort actual FX gains and losses.

FX risk management automation consists of technology tools and platforms that reduce or eliminate manual interventions throughout the end-to-end FX risk management lifecycle. “There are various tools and platforms available to potentially automate the entire currency risk management lifecycle,” Patri explains. “The most common components of currency risk management programs that can be automated are the upstream exposure aggregation, netting, and analytics processes and downstream transaction execution processes,” which include execution, confirmation, and settlement.

Gage and Krishnan offer simi-
lar definitions that center on the
automation of most pre-trade pro-
cesses (e.g., exposure analysis and
the automation of workflows) and
post-trade activities (accounting for
a trade in treasury management sys-
tems and accounting applications).
“To me, FX risk management auto-
mation is having a set of automatic
responses to any movement in FX
rates—and this is in both directions
of an FX trade,” Krishnan notes.
“Automated systems should predict
FX risk ahead of the risk itself, and
with predictive analytics, managing
FX risks and outcomes becomes a lot
easier.”
Krishnan lists five functions
these applications support: iden-
tifying FX exposures; recommend-
ing risk-mitigation strategies (e.g.,
hedging approaches); creating
seamless integration to the treasur-
er’s preferred bank(s); equipping
treasury functions with insights on
how to minimize hedging costs; and
leveraging advanced technologies—
including application programming
interfaces (APIs), robotic process
automation (RPA), and machine
learning—to better manage the
end-to-end currency risk manage-
ment process and strengthen deci-
sion-making.

Deloitte’s Patri agrees, noting that RPA, predictive analytics, and other advanced technologies are currently driving the evolution of FX risk management automation solutions by helping treasury functions more effectively aggregate, analyze, and predict FX exposures. “Greater vendor investment in the FX technology space,” he adds, “is driving more end-to-end automation.”