Corporate Payroll Reconciliation Process
The Money In department of a Financial Investment Firm is responsible for conducting the Corporate Payroll process each week. Rather than tasking the SME’s with analyzing the incoming Payroll Files and manually navigating the task manager to pull and verify payroll file data, Accelirate implemented a Bot to reconcile payroll files and send the firm’s corporate clients an automated request for payroll deductions to be deposited into 401k accounts.
- Bot Creates Task – Once the SME receives an email containing a payroll file, the Bot opens task manager, creates a new task, and attaches the payroll file to it.
- Bot Finds Data Values – The Bot reads the task and identifies the data values required to verify payroll. The Bot pulls and verifies this data, then sends the task to the ‘Run Edits’ queue.
- Bot Runs Two Edit Sequences – The Bot makes an editable copy of the payroll file and generates a summary report of the file to run edits. During the first edit sequence, the Bot checks for conflicting information and items that require action. The Bot checks for “Rejects” – any scenario identified as out of the ordinary per the business rules. If the automation cannot resolve a Reject item itself, the item is sent to the SME for review.
The Bot then runs edits a second time to ensure all payroll summary details are accurate before creating the funding request.
- Bot Generates Funding Request – The Bot identifies the total amount of money the client must send to the institution for the 401k contributions, then generates a funding request for the amount and sends it to the client.
Accelirate’s solution yielded major business value for the Financial Investment Firm. Automating the Run Edits step was especially valuable: by identifying the most common Reject scenarios and how to resolve them, the Bot can handle most Reject items on its own. Reject items that require human action, such as a refund, will be handled manually be a SME. Applying RPA to the rules-based pulling of payroll data led to a drastic reduction in SME time and an increase in data accuracy, which eliminated the need for rework.