Love it or hate it — and most businesses hate it — the month end process is very predictable. We know what we must produce and where our raw materials are, and we can spot the usual potholes. Even so, month end never seems to be executed as simply as it could be. How can this single business process deliver such despair time after time?
Regulations, systems and business requirements are constantly changing, so it can feel as though each month differs from the next. However, there is no reason to see the month end process as an isolated event. It should however be viewed rather like a manufacturing process, complete with defect tracking, quality control and customer feedback — all of which can help to avoid costly product recall.
As with any production line, we must begin with the raw materials, but also must recognise the need for constant quality assurance. At the moment, however, this only tends to happen when reviewing the results. The familiar month-end cycle involves the CFO examining the numbers, spotting an issue, making a change and then resubmitting the data. Problems like these can often be traced back to data issues in the General Ledger (GL) or other upstream systems, which can be time consuming to correct. As such, resolution is often left until the following month, which creates a wildly inefficient system of clearing up.
Nip it in the bud
By comparison, the new production line mind-set involves detecting and tackling issues before they arise, and avoiding the reoccurrence of avoidable defects. In order to achieve this goal, a tracking system must be implemented to ascertain how effective any remedial action is. Such systems will need to be ongoing to account for any new issues that arise and/or to spot others that have crept back after initial removal.
Here are a few areas that warrant continual testing to ensure a comprehensive month end process to avoid a back-log:
1) Posting errors
Within some GL systems it can be difficult to prevent posting to old (previously used) profit or cost centres, particularly when data is fed from upstream business systems. We can find that the same errors recur month after month. A formal testing process can identify these amounts with certainty, allowing them to be corrected quickly and easily.
2) Dimension relationship issues
We may identify combinations of dimensions (for example, cost / account) that are invalid (even temporarily). These subtleties are the product of in-depth business knowledge, but without formalising this knowledge into a set of rules, there is a good chance they may not be detected.
3) Account relationship issues
In some sectors it makes sense to perform “referential integrity tests” for monthly and quarterly submissions. We might consider, for example, whether relationships between Income Statement and Balance Sheet account movements are in line — movements in provisions and FX balances are two examples of this.
4) Items for review
In every Finance Controller’s mind, there is a checklist of transaction groups that warrant ongoing review. These usually indicate a possibility of error: certain accounts (particularly reserves), manual journals to cash accounts, unusually large transactions, etc. A formal diagnostic process presents these transactions to the right person at the right time (in other words, not during month end).
Tests should be split up into logical units within firms so that specific employees own the checks that are required for their areas. After all, they will generally be the people that “own” the data quality challenge, and therefore need know what the errors are in order to fix them.
By paying attention to these areas, firms can run detailed diagnostics at regular intervals throughout the month. This creates a comprehensive system that eliminates a built-up of issues and time-consuming correction at the month end. Adding workflow into the scheme enables GL diagnostics to become a continuous practice, and allows problems to be pre-empted rather than corrected after the damage is done. By the end of the month, the pathway should be clear of all known defects, resulting in a decreased number of reporting iterations, and allowing the company’s focus to be re-directed onto more valuable activity.