RHN: payment time cut in half

The starting situation

RHN provides hotels with the service that connects them to the GDS, the distribution systems through which a large share of their bookings arrives. Its receivables therefore consist of a high number of recurring invoices, spread across as many properties.

Reminders were sent manually, property by property. That kind of work holds up as long as there is time for it, and slips as soon as activity picks up: reminders went out irregularly, and tracking what had already been chased relied on memory and hand-kept files.

Average payment time suffered accordingly.

What we built

An automated reminder scenario on n8n: overdue invoices detected from actual payment status — the precondition for never chasing a hotel that has already paid — escalating reminders sent, and a systematically recorded history.

Triggering no longer depends on anyone: around 40 invoices are chased each month, at the right time, with no manual step. Tracking stops being a file held by one person and becomes a state anyone can consult.

The hard part: connecting to the existing system

The difficult part was not the reminder logic but connecting to the invoicing system already in place. That is almost always where a project like this is decided: the reminder scenario comes together quickly, while getting usable access to the invoicing data, understanding its data model and making sure the payment status being read is actually up to date takes time.

This is what determines whether automated reminders are feasible at all, far more than the choice of automation tool. We now check it during framing, before committing to any timeline.

Results

Average payment time was cut in half. On receivables made up of recurring invoices, a shift of that size changes the cash available at any given moment.

The time given back to the team amounts to roughly one working day per month — the day that used to go into working through the overdue list property by property.