Accounts Payable relies on its ERP system (SAP, PeopleSoft, Oracle, etc) to prevent duplicate payments from being made. These systems use key fields such as invoice number, amount, vendor number, and date to flag a duplicate payment.

Unfortunately, they will not flag the transaction as a duplicate if the data in any of these fields varies. Variations can easily happen because of data entry errors, payments to a duplicate vendor, payments to a wrong vendor, or a cross-currency duplicate. Duplicates can also occur when companies use multiple ERP systems or a PCARD system. This can cause you to lose millions of dollars each year.

Prevent and Identify Duplicate Payments

DataShark A/P will capture duplicate payments in situations where your ERP system will not. You will also be able to find the root cause of these errors, through our extensive reports and metrics.

The tool has been developed for mid-to-high volume environments – 150,000 to millions of annual invoices, as well other complex environments like multiple systems (there is no limit, including P-Card and T&E systems).

DataShark A/P sets itself apart from other recovery audit software because of its robust, comprehensive identification capabilities. Further setting it apart is the delivery of a 50% and higher true duplicate payment rate – due to the unique ability to minimize the number of false positives.

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With DataShark A/P, Your Team Can

  • shield_smallPerform a more comprehensive analysis than with standard audit software.
  • shield_smallRecover payment errors early, before they go out the door.

DataShark A/P is Highly Customizable, and Includes:

  • shield_smallFraud detection reports to identify fraudulent vendors and questionable transactions.
  • shield_smallNo hardware or software installs are required, as it is hosted on our dedicated, secure servers. Updates included.
  • shield_smallDaily, weekly, monthly, quarterly, or annual updates based on your needs.

DataShark A/P can be used independently or in conjunction with Recovery Audit Services

Removing the Noise of False Positives in Duplicate Payment Analysis