New tools, technologies and approaches can help F&A teams reduce their manual work, improve the speed and accuracy of their close process and enable staff to work on higher value, more interesting work. Dedicating time to understanding the account reconciliation process at a deeper level, creating alignment around strategy and generating momentum with small wins can roll into big wins over time and strengthen the organization.
The need to close the books more quickly, accurately and with less risk is a constant pressure that can only be relieved through re-engineering critical components of the financial close process. One of the most critical, manual and time consuming tasks is account reconciliation.
Fortunately, there is new hope for account reconciliation process improvement given the following:
When we want to improve the process to create more efficiencies, there are two basic strategies:
The first step of process improvement is to realistically map out the current process. This would include all of the decisions, actions, tools and delivery artifacts from every step of the workflow. Here is a good description of a process mapping process which includes the 7 types of flow in accounting.
The process mapping process helps us understand where we are spending time and on what tasks. Based on this understanding we can look for solutions to automate the tasks in the process to take out repetitive, manual work or find ways to do the task more efficiently to achieve a similar impact.
Automating repetitive manual tasks decrease overall process time when the manual tasks take a significant amount of time. They are particularly helpful when the tasks span across systems and the user interfaces are static. This normally happens with legacy type systems which are slower to evolve vs newer, cloud-based platforms that tend to update more often since they are not on-premises. Since discrete manual tasks tend to be a modular step in the process, the updating of these automations as data or systems change tend to be much easier as well.
Finance and accounting teams have traditionally relied on using Excel to help with many of the tasks in account reconciliation. Excel Macros, Visual Basic scripting and data manipulation features like pivot tables and advanced filtering have all contributed to improving efficiency in the process, but have limitations across non-Excel systems.
RPA has taken the notion of recording user actions (similar to a macro in Excel), and expanded it software outside of spreadsheets. RPA sits on top of multiple systems and acts as a personal agent that is relatively easy to setup to do things such as:
New companies like UI Path and Blue Prism have built easy to use RPA platforms for building rules-based bots that replicate your actions in an automated manner. These platforms can save staff an enormous amount of time by automatically replicating what you are already doing.
However, RPA does not improve the process - it only speeds it up what you already do.
In the Gartner Magic Quadrant report on RPA vendors, they make several points about the downside of RPA. Specifically;
The use of Artificial Intelligence - specifically Machine Learning - is an opportunity to use next generation technology to improve steps in the process. In the case of account reconciliation, one of the tedious tasks is the matching process of clearing transactions that are good and identifying the items that need to be reconciled.
For simple data sets and matching transactions use cases, you can use a rules-based approach that leverages amounts, invoice numbers or dates and matches them up. One-to-one matches for modest volumes are an example of a simple use case that can benefit from this approach. However, as matching scenarios become more complex, creating rules to match transactions (especially at a large scale) becomes very expensive to implement, the rules tend to break easily, and provide a sub-par matching accuracy.
Scenarios that make matching reconciliations more challenging:
These scenarios can all be handled much more efficiently with a Machine Learning approach like Sigma IQ's. An ML approach can detect data types and relationships from multiple historically-matched datasets, eliminating the need for creating rules that need a FTE (or more) for maintenance.
While complex matching reconciliation process can be dramatically improved through the use AI and Machine Learning, a re-thinking of the entire process of account reconciliation is a bigger, more organizationally expensive endeavor that may provide larger, longer-term benefits.
The bigger the process change, the more measured the implementation.
Every business has its own account reconciliation process in place. Each is unique in their business model, maturity level, size and organizational complexity. The following steps can help in planning your process improvement:
New tools, technologies and approaches can help F&A teams reduce their manual work, improve the speed and accuracy of their close process and enable staff to work on higher value, more interesting work. Dedicating time to understanding the process at a deeper level, creating alignment around strategy and generating momentum with small wins can roll into big wins over time and strengthen the organization. Hopefully, this article has sparked some ideas and given you some inspiration for your own account reconciliation process improvement program.
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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