If discrepancies are not identified early enough, there could be catastrophic repercussions. Begging the question, is manual reconciliation reliable enough to sustain a modern firm? What happens if your data is so complex that only you and your colleagues know what should match, based on your knowledge of your business process?
Aurum combats these complex issues by using two simple techniques, 'Groupings' and 'Rulesets', which are both based on your team's existing working models. By working with our software to absorb your current knowledge, the hard part is over. A task that would take hours can now be completed in minutes.
What are 'Rulesets'?
As the name suggests, Rulesets are a set of rules which we can implement into the software. These rules filter the data before the matching process begins. This means by creating unique rulesets we can filter the data to only be able to group on a specific section, making complex transactions easy.
What are 'Groupings'?
Groupings take the criteria which we want to match on both sides of our data sets. Providing both data sets share similar attributes, the data can be matched. The output of the software will then, only deliver the discrepancies that necessitate your team's attention, saving precious time and effort.
Complex matching and difficult data sets
As we know, real-world data doesn't always conform to an ideal set. What works for you and your team often requires the use of novel problem-solving techniques, which traditional software finds notoriously difficult. This often leads teams back to square one, relying on Excel spreadsheets that were not designed to handle reconciliation tasks and results in periodic crashes and general unreliability.
Errors can often be triggered from unpopulated cells or perhaps populated cells but with missing information. Aurum can run custom scripts to append the correct data for both situations. This can be done for both the cashbook and statement sides, wherever the issues may occur, leading us well on our way to a consistent solution.
While handling new data coming in, you can also run custom rules to enhance existing data which prevents the meticulous work dissecting past transactions before adopting a new software solution.
Typical edge case
Let’s compare two sets of data, one 'Cashbook' (C) and one 'Statement' (S) that need to be reconciled. The only field shared between the two sets is their respective amounts. It’s not possible to simply run the software based on ‘amount’ because these values will not be unique.
In this case, it is necessary to use a script to create a common, unique field by utilising your knowledge of the data. For example, based on business knowledge we understand that when the transaction type in the statement side is ‘pay-out’, this should be matched to the transaction type ‘refund’ in the cashbook side. However, we couldn’t add transaction types into the grouping because the software wouldn’t know how to match 'pay-out' with 'refund'. If in the software ‘extra’ is an empty field on the statement side, we could populate this field with the word ‘refund’ whenever the field transaction type is populated with ‘pay-out’. Now, the matching word ‘refund’ is in the extra column on the statement side, allowing the grouping to match perfectly.
We can resolve this issue for the entire data set by running multiple scripts and automating the reconciliation process. Discrepancies will be highlighted for your team to solve, potentially saving days of manual work and revisions.
Interested in knowing more?
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Consultant @ Aurum Solutions
16th December 2021