First, it’s about time: Transaction matching, line by line, is a thankless time-consuming task to complete. Worse, when exceptions are found, the analyst (or other designated treasury authority) has to search and hunt down people, reports, payments and/or invoices—often desk by desk, spreadsheet by spreadsheet —to determine the cause of the discrepancy. Given the variability of scenarios and reporting —understanding which financial data is needed and with what kind of handling—it’s nearly impossible for even the most determined of analysts to find, remember, and apply, the appropriate exception rules and deliver what is needed to the business for automated account reconciliation.
A company’s financial close is typically a very repetitive and overwhelming task with accounting teams spending hours each day reconciling the general ledger balances with information from the bank statement. Typically, there is a rush to close the books at the end of the month to begin all over again and get going on the next month. If the company is managing this process in spreadsheets, they are pulling data from their accounting system or ERP and pulling statements from bank portals manually. The analyst will take the list of transactions and begin to comb through the data one by one. This means that the process is prone to errors, like mistyping and accidentally adding extra digits, which are not easy to track.
For companies with more automated solutions where general ledger entries are exported from the Enterprise Resource Planning (ERP) system, and imported a separate solution, this means that there are extra steps involved before the team can begin the reconciliation process.
An automatic solution using advanced machine learning will help them make sure they have a documented review and approval process and help them address any discrepancies in real-time instead of waiting until it becomes a major problem.