The month end close is a time-consuming process as it goes through multiple stages & functions, which include recording financial transaction summaries from external systems, account analysis, accrual calculation, reviewing transactions for errors and account reconciliation.
Reviewing all the transactions for the month, and ensuring that they are accurate and applied to the correct accounts is one of the most important functions that the finance teams perform every month.
Reviewing all transactions is a laborious task. And every month after closing books, the finance teams are uncertain and left wondering whether they caught all the errors and omissions. The reason for this uncertainty is the manual nature of the task, which poses a great burden on the finance teams, month after month. Let’s take a look at some of the key challenges that finance teams face while reviewing transactions during month end close:
While the task of reviewing all transactions can be daunting, finance teams can make it less challenging by following the below-mentioned best practices.
Apply the concept of materiality: The most common approach is to apply the concept of materiality, validate the high-dollar transactions, and some of the mid-level transactions, and randomly review some low-dollar transactions.
Ensure routine account analysis: Do routine analysis on all prepaid, purchase order, and accrual accounts to be sure the amounts are still valid. Accountants can do account analysis even during non-close times, this will give room to analyze more of the transactions rather than assuming all material items are timely identified and all smaller transactions will be caught eventually.
Keeping a constant vigilance: This is where technology and automation come in. Keeping a constant check to get 100% review can be an ordeal for the finance teams, but enabling automation through financial close software can reduce the stress as well as ensure increased efficiency.
The financial close process of reviewing all transactions can be automated by leveraging two technologies:
Identifying anomalies in pattern-based transactions can’t be done by rule based automation. It needs Artificial Intelligence to dynamically identify the data patterns that are both normal and abnormal. AI technology uses algorithms for pattern detection and applies dynamic rule discovery across and combined with different dimensions, tolerances, and focus. It isn’t a magic algorithm that finds everything but a series of algorithms that focus on specific AI-derived dimensionality and tolerances. It learns as data sets roll forward and the data is enhanced with the actual corrections applied vs. anomalies detected. The good news is you don’t have to think up all of these relationships and rules. Pattern-matching algorithms use your past transactions as patterns for the future.
Read our eBook, “Demystifying AI for Anomaly Detection” to understand more about why AI is the right technology for detecting errors and omissions, eventually improving the data accuracy in the financial close process.
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