Largely yes. Matching bank transactions to outstanding items is exactly the type of work that automated matching is suited to: a fixed structure, high volumes, and an error that can usually be corrected quickly. Where the bank connection is active and the matching rules are clear, this is already work today that is largely handled by software, with an employee dealing with the remaining exceptions.
A bank statement is a structured file: fixed fields for amount, date, counterparty account, and description. An outstanding item in the bookkeeping has the same kind of fields. Matching is then largely a matter of comparing amount and reference against each other, and that is a task that runs according to rules, not a task that requires a fresh judgment for each case. On top of that, the volume is often large: a company with hundreds or thousands of transactions per month has a lot to gain from a process that does not stop for a coffee break.
The cost of errors sits in the middle. An incorrect match does not directly affect a customer and rarely leads to a fine, but it can make the bookkeeping appear to add up on paper while reality is different. That is why checks remain necessary, not because the technology cannot handle it, but because a single mismatch only becomes apparent later, for example during a reconciliation at the end of the month.
A transaction of 1,240 euros with the description "invoice 20345" is automatically matched to the outstanding item with that invoice number and amount. That goes flawlessly as long as the amount and reference correspond. If a partial payment comes in, an amount that has been rounded, or a description without an invoice number, the automatic match stalls and an employee has to assess which item it belongs to. That is precisely the distinction between what a system handles and what is left for a human: not because it is difficult, but because the data needed to apply the rule is missing.
The technology that handles this work today can be characterized as rpa: software that recognizes, compares, and matches transactions according to fixed rules. This is not a self-learning system that judges for itself what likely belongs together; it is the execution of agreements that have been laid down in advance. That also means the quality of the result depends on the quality of those agreements, not on the intelligence of the software.
Two conditions determine whether this works for a given company. First, an active bank connection: without automated delivery of statements into the bookkeeping, there is nothing to match. Second, clear matching rules: the more unambiguous the agreement on what constitutes a match, the less is left for an employee. If either is missing, the work automatically shifts back to manual checking, and that is then not a shortcoming of the technology but of the setup preceding it.
For a company that works with multiple payment providers alongside the bank, this is somewhat different: then it also matters how reconciling transactions from payment providers is set up, because that does not automatically follow from a bank connection. And for a company where many partial payments or deviating references occur, the share of manual work remains larger than for a company with fixed, recognizable payment flows.
This task illustrates a pattern that applies more broadly: work that runs according to rules and occurs in high volume shifts to software sooner than work that requires case-by-case judgment. We see that same pattern with recording purchase invoices in the bookkeeping system and with monitoring due dates of purchase invoices for timely payment, both tasks with a fixed structure and repetition. For a company where those same processes are less standardized, that shift proceeds more slowly, not because AI can do less there, but because the foundation for it still needs to be laid.
What remains is not random: it is the part that requires a judgment that does not fit into a rule. Anyone who wants to get a clear picture of this for their own bookkeeping would do well to also look at drawing up the outstanding items list for accounts receivable and payable, because in practice these two tasks are often linked.
This is not personnel advice and not a basis for a decision about positions or staffing levels. What an organization does with freed-up capacity is up to the organization itself. If a question arises regarding employee co-determination when redesigning this type of process, separate statutory requirements apply; see when the works council is involved.
Whether this shift has already progressed this far in your own bookkeeping depends on your bank connection and the precision of your matching rules. That differs per company, and a statement made without knowing that information is no more than a guess. The free quickscan from FTE TO AI gives an initial indication of this: twelve questions, no account required, with an indication of what share of the hours in this profile can be taken over by AI today. The full work scan, which breaks down the work of your entire company in this way, is still under development.
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Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.