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RPA and Smart Automation
Case Studies

Gross to Net Payroll Reconciliation

The Global Finance Unit of a worldwide technology conglomerate is responsible for processing employee payroll each month. Before automation, this process involved a team of ten analysts comparing the data in the business’ Working File against the payroll vendor’s monthly Gross to Net File in order for payroll to be approved.

Manual reconciliation for a medium-sized entity takes 45-60 minutes, while a larger entity can take up to three hours. This process is performed for 27 entities each month.

Accelirate developed an automated solution to relieve the analysts of this highly time-consuming process and eliminate the rework associated with human error. To do this, the scheduled Bot navigates to a folder where the monthly Working File, generated by the business, and the Gross to Net (GTN) File, supplied by the company’s payroll vendor, are stored. The Bot downloads both fi¬les.

Next, the Bot compares the Working File with the GTN File line item by line item, looking for any discrepancies within the 20+ tabs for each entity. Since the client is a global company, the Accelirate team took a modular approach when developing the automation so the Bot is able to account for the different tax laws and market nuances in each entity.

If the Bot fi¬nds a mismatch, a record is made in the Output Report indicating the tab and column of the variance. This logging method improved process efficiency by showing the analysts the exact location of the items that need to be investigated, eliminating any additional time spent searching for the variances in such a large ¬file. After the Bot ¬finishes running it generates the ¬final Output Report indicating which entities ran successfully, which were unsuccessful, and which need to be rebalanced. The Bot repeats the process until there are no more variances. Once all rebalancing is complete, payroll is ¬finalized and approved.

¬27 Entities Processed Each Month
78% Reduction in Processing Time
99% Accuracy in Data Management