Financial processing
Incoming payments are manually matched to open invoices, with the same deviation patterns identified from scratch each time.
The same client has paid without a payment reference for two years. Yet every month-end close starts with the same search. That is solvable.
The problem
Wholesalers, distributors, and service businesses with a substantial invoice volume spend considerable time closing the month. Payments arrive with missing or incorrect references, rounded amounts, or sender names that do not exactly match the accounts receivable ledger.
Each deviation requires manual investigation before the payment can be posted. At one hundred payments per month, this is manageable. At five hundred, it is a structural backlog.
What the audit reveals
An audit typically shows that a large proportion of deviations are recurring and predictable: the same customers who consistently pay without a reference, the same rounding differences, the same name variants that have been in the system for years.
Time is not lost on genuinely complex cases. It is lost on recognising familiar patterns from scratch each time — patterns a system only needs to learn once.
The approach
Implementation of a matching algorithm that learns from historical payment behaviour. The system recognises known deviation patterns and matches automatically. Payments that cannot be matched with sufficient confidence are flagged for human review — including the context needed to make a quick decision.
In practice, 90 to 95 percent of payment volume is processed automatically. Month-end close shrinks from multiple days to a fraction of that.
What to expect
Organisations that address this pattern typically report:
Automatically matched
Typically 90–95% of volume
Actual source of delay
Familiar patterns — not complex cases
Effect on month-end close
Significantly shorter turnaround