Reconciliations between systems, ledgers, or accounts are time consuming error prone tasks that can be easily automated. RPA allows employees to add value by investigating exceptions (missing or incorrect values), instead of the monotonous task of identifying them. As businesses grow in size and scale, the volume of reconciliations in total processes and transactions also increases. RPA minimizes the burden of the additional workload, by flexibly scaling to meet the increased demand.
The Bot in a Nutshell:
RPA easily extracts the necessary source data, regardless of storage location or format. Enablers such as database integration, OCR, and website scraping remove the traditional barriers to automation.
Once extracted, RPA can structure the data accordingly and perform the reconciliation process. The robot will output two files – the matched transactions and the unmatched transactions. Thus allowing employees to focus on the value-added task of understanding the unmatched transactions, instead of identifying them.
Often, systems and accounts don’t “talk” to each other, forcing human reconciliation. RPA bridges this gap and quickly identifies missing, duplicate, or extra entries in any chosen source.
Once identified, humans manually review the exceptions to identify root causes.
This process is applicable across almost every industry, as all use financial accounts that require reconciliation.
Cost & scalability
Pain points targeted:
Reconciliations are time consuming
Poor data quality
Scalability for peak times
Reduce monotonous work
Challenges to expect:
What will the bot do?
To read more, find out our RPA use case on Data Extraction and Integration