Automating Bank Reconciliations with Machine Learning & RPA

For many companies, automating bank reconciliations with Intelligent Automation presents the biggest opportunity for saving time during the financial close process. This post discusses how RPA and Machine Learning can dramatically improve efficiency in the bank reconciliation process and save your team hundreds of hours of manual processing time.

Bank reconciliations are the most ubiquitous type of transactional matching reconciliations across all business types and typically require reconciliation between disparate systems (e.g. bank vs ERP). Small businesses that do all of their finances through one institution (bank or credit union) can accomplish matching through simple excel or embedded system tools.

However, mid-size and enterprise organizations have a much larger challenge with many account types, payment types, institutions, systems, time zones and payment complexities, plus vastly larger scale that make matching reconciliations far more difficult. This is especially true for companies that are growing rapidly through M&A, organic growth or international expansion.

If you are involved in the the bank reconciliation process, you’ve probably encountered one or more of the following:

• Duplicate entries — whether through human or system error.
• Different data formats between systems — date is one example where format varies widely across systems. Another is how credit card data is stored and presented.