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 Robotic Process Automation (RPA) and Machine Learning (ML) can dramatically improve efficiency in this 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:
Traditional solutions to create more efficiencies in the bank reconciliation process include:
The first step to automating any process is to clearly identify the steps and activities in the process in order to understand where steps can be omitted, improved or combined with other steps - whether that uses advance intelligence technologies or not. The following is a simplified version of the bank reconciliation process with areas of opportunity for automation by type of technology.
You don't have to use just one type of technology to solve the bank reconciliation challenge. In fact, using best of breed point solutions that fit into a workflow and solve the right problem in the right way is often a better approach than investing in one larger platform that claims to “do it all”.
If you are on an accounting or ERP system that has an effective process for capturing and extracting the right data sets for reconciliation, and it works for you, then you could continue to use it for that purpose. Robotic Process Automation might be a good addition to this process if there are numerous manual steps for managing the extraction and loading of the data on either side of the matching process. RPA is also helpful for tasks like routing documents for review and approvals.
Machine Learning is the right approach for large scale, complex data sets that require more than a simple matching of a couple of fields or if you are dealing with a large volume of transactions each month. ML is also very effective when you have many different bank reconciliations for matching or are growing rapidly and are adding new businesses, accounts and processes on a regular basis.
The emergence of Artificial Intelligence - specifically Machine Learning - as an advanced analytical tool is the ideal approach to solve the matching reconciliation challenge because:
Sigma IQ's platform has a state of the art matching engine that learns from feedback, is automated, and cloud based. As experts in account reconciliation, we have built in support for complex, multi-file, multi-source, many-to-1 or 1-to-many reconciliation scenarios. As a SaaS platform, the implementation process for new use cases can be done in less than a day without the need of IT. As experts in machine learning, our platform is data agnostic and can perform matching beyond bank reconciliations, including for hundreds of other use cases both in F&A and in operations.
The reduction in cost of cloud computing, the flexibility of SaaS solutions and the improvement in Artificial Intelligence approaches have created a new era opportunity for F&A staff. The matching reconciliation process is a complex, traditionally manual set of tasks that is perfect for a Machine Learning based solution.
If you deal with large scale, complex bank reconciliations and believe your staff's time is better spent on higher value tasks then reach out and let's discuss how Sigma IQ might be able to help.
According to the APQC General Accounting Open Standards Benchmarking survey (2,300 companies participated) - Cycle Time for Monthly close ranges from 4.8 days or less for the top 25% of companies to 10 days or more for the bottom 25% of performers.Learn How To Be a Top Performer
According to a study by Robert Half & the Financial Executives Research Foundation (FERF), only 13% of F&A teams have utilized advancements in technology solutions, with the majority of CFO’s admitting they still struggle with painful aspects of account reconciliation.Read About AI-Driven Cost Savings
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