Reconciliation is not simply finding two equal amounts and clicking accept. One deposit may cover several invoices, a fee may be netted from a settlement, and a vendor may collect on a date far from the bill. Artificial intelligence can review volume and locate candidates, but it does not know every commercial agreement or turn a likely match into conclusive evidence.
What AI can prepare
After a statement import, Tario finds pending lines and can score candidate invoices or bills using amount, date proximity, and name similarity. It may also propose handling a line as a transaction, category, or skipped item. These are reviewable proposals. Documents and context remain necessary before confirmation.
The benefit is a shorter manual search. Instead of opening a hundred documents, the user begins with options supported by reasonable signals. A high score is not approval. Two customers may pay the same amount, an internal transfer may resemble income, and a partial payment will differ from the total. Enough detail must remain visible to catch those situations.
A controlled workflow
Import the correct file and verify account, currency, period, opening balance, and closing balance. Separate obvious matches from exceptions. Review counterparty, reference, date, amount, and proposed documents. Confirm only when the story makes sense. If evidence is missing, leave the line pending with a note rather than forcing it into a convenient invoice.
Begin with small batches. Twenty lines reveal how the bank labels mobile transfers, card settlements, fees, and account movements. They also expose reversed debit and credit columns. Accepting hundreds of proposals before understanding the format magnifies a bad configuration. Speed becomes useful after the circuit is proven.
Cases that deserve a pause
A ₡565,000 deposit could settle one invoice, combine two invoices, or represent an advance. A recurring debit might be rent, debt service, or a transfer between company accounts. A small difference can arise from a fee, exchange rate, or withholding. Retrieve the support and review accounting treatment before accepting the suggestion.
Partial payments need particular care. Linking a payment to the correct invoice should reduce the open balance without closing the whole document. The same applies to suppliers. If a system marks an invoice paid merely because it found a similar movement, aging becomes unreliable and the next collection effort starts from a false premise.
Operating memory, not magic
Bank descriptions often repeat. A rule or merchant memory can support future classifications once the business has reviewed it. Limit automation when generic text covers different vendors. Merchant name, account, sign, currency, and amount range add context, but exceptions still need to remain visible.
Track old pending lines, unsupported entries, differences, and reversed decisions. Frequent correction of one category is a reason to refine or remove its rule. The aim is not an impressive acceptance percentage. It is a period close supported by documents and a short list of cases somebody is genuinely investigating.
Permissions and review
The person importing a file need not approve every outcome. Define permissions according to risk. One person may prepare matches while another checks high-value or unsupported lines. AI does not remove internal control; it can organize information so reviewers spend their time on exceptions rather than repetitive searches.
Test the workflow against a statement previously reconciled by hand. Compare candidates, outcomes, and ambiguous cases. Document fees, internal transfers, grouped collections, and partial payments. Tario keeps reconciliation decisions under user confirmation. The boundary is intentional: software can suggest a path while an accountable person decides whether the documents tell the same story.