Match bank money to the right invoices.
Suggestions you can trust, with clear reasons — even when one transfer pays many invoices. Close the month with a smaller exception queue.
Lam Kee Trading Ltd
One payment, many invoices
Customers batch payments across weeks of invoices. Untangling by hand is slow and error-prone.
Messy bank descriptions
Invoice numbers are partial, missing, or buried under bank prefixes and reference codes.
Month-end stalls on unmatched cash
Every open bank line delays the close and forces a scramble on the last day of the month.
Bulk payments, handled as a first-class case.
Click a bank line. HeyBen shows the suggested documents on the right — including bundles that add up to a single transfer.
Lam Kee Trading Ltd
A path for every kind of payment.
- 1
Exact & reference matches first
Bank lines with exact amounts or clean invoice references are matched right away.
- 2
Installments and deposits
Part-payments against a larger invoice are recognised and offered as a partial match.
- 3
One payment → many invoices
Open invoices for the same customer whose outstanding amounts add up to the bank transfer are grouped into a single suggestion.
- 4
Learns from your confirms
Similar descriptions get easier over time as your team confirms matches.
- 5
Smart suggestions for the leftovers
Hard cases go to a review queue with clear reasons — never silent guesses on low confidence.
Where the difference actually shows up.
Cleaner bank match, smaller exception queue.
When cash matching handles bulk payments up front, the period close is one clean review instead of a scramble.
A quiet, accountable workflow.
You stay in control
Every suggestion can be confirmed, adjusted, or rejected. Nothing posts silently.
Audit-friendly reasons
Reasons stay on record with the match, so reviewers know why each posting was made.
Safe with little history
Rule-based matches carry new companies from day one; learning deepens as the team confirms more.
Your data stays yours
Learning is scoped per company. Your matches are never used to train shared models.