NEW WAYS OF WORKING
The emerging finance technology foundation is
different from the one created by the first wave of
automation. Currently, most finance organizations use
process-specific systems to pull data from multiple
ERPs. As a result, process owners get an incomplete
and inconsistent view into the end-to-end information
flow. This affects the speed and accuracy of the
account-to-report process as well as the capability
to deliver important data to FP&A, so analysts can
produce reliable forecasts and plans. This friction in
the process also leads to persistent inefficiencies
and obscures common trends, creating financial risk
through inaccurate reporting as well as the potential
for misleading analysis.
To overcome these hurdles, more organizations
find value in adopting core finance solutions that
converge disparate processes by anchoring on a
flexible and intelligent data source, which can ingest
large quantities of financial, external and operational
information. Enabled by machine learning, the new
data core can learn overtime how to process common
transactions. It can also detect and elevate anomalies,
surface exceptions, automatically route them to the
right staff and recommend remediation actions.
Account-to-report organizations are taking the digital
lead and triggering a true transformation of the finance
function. The core data source allows finance to break
down traditional process barriers and reconsider
which activities take place where. For example, in the
typical setting, accounting uses general-ledger data to
reconcile, close and consolidate. Meanwhile, financial
planning and analysis (FP&A) collects data from the
business through the forecasting cycle or income-
statement information. FP&A often relies on a separate
planning solution to build the budget, run variance and
other analyses and produce the forecast.
This separation between accounting and FP&A need not
be. By pulling together vetted financial and operational
data from a single source of truth and automating a large
share of routine activities, accounting staff have capacity
to run first-level analysis to extract insight and seamlessly
hand off enriched information to the FP&A team. FP&A
then steps in to apply more sophisticated techniques
like scenario planning and statistical modeling to gain
deeper insight and improve the quality of the planning,
forecasting and performance analysis process.
The application of an intelligent data core extends
beyond traditional finance departments to T&E and
even procurement. For example, using OCR, finance
can reduce friction in capturing expense receipts or
supplier invoices. Using machine learning, the system
can then route supplier invoices and suggest invoice
assignment to the right accounts payable clerk.
Example #1
Aon, a global professional service company with
operations in 120 countries, adopted a new accounting
solution last year that includes a core data source
and advanced functionalities that streamline the
close process. Given its geographical diversity and
multiplicity of charts of account, the original close
process was highly manual and required extensive
reconciliation. By replacing its legacy systems with a
cloud-based core finance application, Aon’s account-
to-report team shortened the close cycle by 25%. The
new solution includes automated workflow definitions
for the journal entry review and approval process;
the ability to attach support materials for journals in
order to efficiently process approvals; a mobile app
for rapidly addressing tasks, and in-system drill-down
capabilities that enable quick research and analysis.