Background
For more than 80 years, Georgia’s Own Credit
Union has provided long-term value for 187,000
members while serving its local community of
Greater Atlanta. Georgia’s Own Credit Union
offers a wide variety of financial services
including personal banking, individual
investment options and wealth management
and manages more than more $2.3 billion in
assets.
Challenge
Georgia’s Own Credit Union’s IT team is
responsible for managing the data related to
each member’s account. This small group
interacts with business analysts in various
departments, such as Lending and Marketing to
provide accurate information related to
Operations and Account Reconciliation. IT needs
to run hundreds of data processes – or health
checks – on incoming data, account services
and member interactions to make sure
everything aligns in the core banking system.
The challenge lies in how quickly the
information may be acted upon and in
uncovering any exceptions that require editing.
One of the more critical tasks is to spot
discrepancies between large data files from
their ATM vendor with records in the bank’s core
system, according to Thomas Stratton, an
Information Architect at Georgia’s Own Credit
Union “Our goal is to find and resolve any issues
in our members’ accounts and to ensure a positive
member banking experience,” Stratton said.
“However, it takes the intensive focus of an
analyst – upwards of four to six hours daily on top
of normal work tasks – to complete this manual
data entry and manipulation.”
Solution
The IT team sought a solution that could
automate these manual processes, improve the
speed in which data reconciliation took place
and increase the overall accuracy of reporting.
Beginning with the ATM and account data
reconciliation process about two years ago,
Datawatch Monarch applies intelligent data
processing to complex text reports, spreadsheets
and databases. Rather than having an individual
business analyst spend hours each day
manually loading large files into an SQL Server
Database, running logic on it and comparing it
with the information in the core banking
system, Datawatch Monarch can complete the
task in mere minutes.
“By my estimate, we’re saving 160-200 hours of
man power each month by automating these
previously manual processes. By quickly matching
and comparing data from the two separate
databases, we can determine where there are
exceptions, allowing our Operations team to
quickly process and solve any issues before our
members are impacted,” Stratton added.
Results
In addition to the core data intelligence
processing of member transactions, Stratton
and the IT team use Datawatch Monarch to
handle and automate hundreds of other data
processes, resulting in greater departmental
efficiencies across the credit union.
Another example is in processing and reconciling
monthly member statements, which
requires that hundreds of thousands of
statement images be correctly delivered to
members and electronically stored in the
banking systems so that information is available
for legal, accounting and member service
purposes. The IT team must verify that all the
information is loaded properly – a process that
used to take upwards of five days and can now
be done within 20 minutes.
“If we send out 100,000 statements to be made
available for members via eStatements, print or
mail and we only get back 78,000, we know there
is a discrepancy. With Datawatch’s data
intelligence platform, we are able to proactively
determine which statements were not processed
and correct it in a timely manner. We are
improving the overall member experience by
resolving any issues within hours rather than
days. Datawatch Monarch provides a direct
service improvement for our members and our
staff,” Stratton said.

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