Retail Use Case: Bank Reconciliation

Use Case: Bank Reconciliation

Function: Finance

Industry: Retail


The client was facing significant challenges around visibility and accountability in the bank reconciliation process. The reconciliation was a time-consuming process, requiring the accounts department to find information from multiple sources to balance the final figures.

The previous process involved the finance team logging into the bank portal and downloading the bank statement in excel format before verifying opening and closing balances are tallied, verifying commission and VAT rates of each transaction, check for refunds or manual entries, log into SAP and download credit and debit entries, and finally tally the sums in an excel sheet. If there are particular transactions which do not match, download the transaction slips and communicate with the bank. Unmatched transactions are then used to create reconciling items and correcting journal entries.

Some of the challenges that were faced by the finance team:

  1. Duplicate entries
  2. Data clean up at the point of entry or data cleansing during a downstream process
  3. Post-extract data transformation processes to force each data type to conform a rigid format
  4. Human generated data errors
  5. Journals created and posted manually to each account and where required, analyzed to the individual store for items not present


We implemented RPA bots to automate existing processes with reduced timelines and higher efficiency without disrupting legacy systems.

RPA can quickly handle the first two parts, gathering and consolidating, which typically take the most time in preparing the final bank reconciliation sheet.

RPA can make the reconciliation process seamless, significantly reducing the need for manual intervention. While humans can only process a few transactions per minute, software robots can process thousands, reducing a process that usually takes days to minutes. The bots respond to XXXX which leads to the following process:

  • The bot downloads the statement from the bank platform and the transaction data from the Gidea, then matches and consolidates the SAP and Geidea data.
  • The bot then checks the opening and closing balances in the statement, checks for STU and refunds.
  • The bot downloads SACO books data and credit/debit data from SAP.
  • The bot creates the bank reconciliation file in pre-defined format.
  • The bot sends a consolidated report of incorrectly calculated transaction fees, missing reconciliation data and unidentified transactions for the human team to address.

Soft Benefits

  1. Reducing time to close through RPA driven reconciliation
  2. Reduce the time and labor hours it takes to reconcile transactions by 90%.
  3. Eliminate costly errors due to human input of manual and rules-based matching
  4. Improving quality of data coming into the close process including reducing data errors before the trial close

Realized Hard Benefits

  1. Volume of Transactions – 12 instances per year. Each instance has more than 1k Transactions
  2. Number of hours saved – 500+ per year
  3. Value Saved – 100k SAR per year