Reconciliations: Automating Manual Data Aggregation and Matching using RPA

Updated: Aug 5, 2019

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.


Applications:

  • Ledgers

  • Systems

  • Accounts

  • Audit logs

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.


High-Level Plan


Main Benefits:

  • Cost & scalability

  • Process reliability

  • Data quality

  • Smart data

Hands-On Considerations


Pain points targeted:

  • Reconciliations are time consuming

  • Poor data quality

  • Scalability for peak times

  • Reduce monotonous work

Challenges to expect:

  • Process complexities

  • Issue resolution


What will the bot do?