Who it's for: anyone in a SaaS or tech finance team who wants to take repetitive work out of the month-end close, without breaking controls along the way.
What you'll get: a simple scoring method for picking what to automate, a shortlist of common first candidates, a worked example with the journals involved, how to set up and check each automation, and a key risks and controls table.
Reading time: about 11 minutes. Have a rough list of your close tasks and how long they take.
Why the order matters
Most teams have more things they could automate than time to set them up. Starting with the shiny option rather than the painful one is common, and it leaves the slowest parts of the close untouched. A quick scoring exercise points you to the tasks that save the most time and reduce the most risk for the least effort. The robots can wait their turn.
Key terms
- Automation: a system doing a task to a set rule without a person doing each step, for example a bank rule or a scheduled journal.
- Bank rule: a rule in the accounting system that codes or matches bank lines that meet set conditions.
- Recurring or scheduled journal: a journal set up once that posts automatically each period.
- Auto-reversing journal: a journal that reverses itself on the first day of the next period. Common for accruals.
- Clearing account: a temporary account that money passes through, such as payment processor receipts before payout. It should clear to zero or an explained balance.
- Exception: an item the automation couldn't handle, which goes to a person.
- AI-assisted: a task where AI drafts, matches or flags, and a person reviews. Different from a fixed rule, because the output can vary.
Step 1: list and score your close tasks
Write down every task that happens each close, with the hours it takes. Then score each one. A simple method many teams use:
| Factor | Score | What it means |
|---|---|---|
| Hours per month | Actual hours | Time spent by everyone on the task each close |
| Error risk | 1 low, 2 medium, 3 high | How likely a mistake is, and how much it would matter |
| Effort to automate | 1 low, 2 medium, 3 high | Set-up time, cost, and how much data cleaning is needed first |
Priority score = hours × error risk ÷ effort. Higher scores go first. It's rough, but it turns a debate into a list.
Step 2: check each candidate is ready
| Check | What "done" looks like | |
|---|---|---|
| ☐ | The process is written down | Steps, inputs and outputs documented; automating an unclear process just makes mistakes faster |
| ☐ | The data is clean enough | Consistent customer names, supplier records and account codes |
| ☐ | The rule is clear | You can describe the logic in one or two sentences |
| ☐ | Exceptions have an owner | Someone is named to deal with items the automation can't handle |
| ☐ | There's a check on the output | A reconciliation or review shows the automation worked |
| ☐ | Access is controlled | Only approved people can change rules and recurring journals |
Common first candidates
| Task | What automation looks like | Where AI helps | The check that stays |
|---|---|---|---|
| Bank matching | Bank feeds plus rules for regular payments and receipts | Suggested matches for irregular items | Monthly bank reconciliation, reviewed |
| Payment processor payouts | Integration posting sales, fees and refunds separately | Explaining unmatched payout differences | Clearing account reconciled to zero or explained |
| Billing to ledger | Billing system syncing invoices and credit notes | Listing differences by customer and invoice | Billing-to-ledger reconciliation each month |
| Prepayment releases | Scheduled journals from a prepayment register | Reading contracts to set up the schedule | Prepayment balance agreed to the register |
| Accrual reversals | Auto-reversing accrual journals | Suggesting accruals from supplier history | Accruals schedule reviewed each month |
| Receipt capture | Expense tools reading receipts at the point of spend | Reading and coding receipts | Card account cleared; sample coding checked |
| Variance commentary | Standard reports refreshed from the ledger | Drafting first-pass commentary | Reviewer edits and signs off |
AI shortcut: give an approved AI tool your close checklist with the hours for each task, and ask it to score each one using the hours × risk ÷ effort method, explaining its risk and effort scores. Use it as a starting point for a team conversation, not the final answer: the people doing the work know where the effort really is.
Worked example: MonthEndClub Co Ltd picks its first three
MonthEndClub Co Ltd is a fictional UK SaaS company with a two-person finance team. Its close takes seven working days. The team scores its tasks:
| Task | Hours / month | Error risk | Effort | Score |
|---|---|---|---|---|
| Bank matching | 6 | 2 | 1 | 12.0 |
| Billing to ledger reconciliation | 5 | 3 | 2 | 7.5 |
| Stripe payout reconciliation | 4 | 3 | 2 | 6.0 |
| Prepayment releases | 2 | 2 | 1 | 4.0 |
| Variance commentary | 4 | 1 | 1 | 4.0 |
| Accrual reversals | 1 | 3 | 1 | 3.0 |
| Chasing card receipts | 3 | 1 | 1 | 3.0 |
| Total | 25 |
The top three are bank matching, the billing-to-ledger reconciliation and the Stripe payout reconciliation. Together they take 15 hours a month. After set-up, the team expects them to take about 5 hours (1.5 + 2 + 1.5), all spent on exceptions and review. That saves around 10 hours a month, or about 120 hours a year.
The Stripe payout, before and after. In October, customers paid £50,000 of invoices by card through Stripe. Stripe deducted £1,000 of fees and £1,200 of refunds (credit notes already raised), and paid out £47,800 (£50,000 − £1,000 − £1,200).
Before, the team posted one bank line of £47,800 against debtors, leaving debtors overstated and fees missing. After setting up the integration with a clearing account, the entries are:
| Customer receipts collected by Stripe | Dr | Cr |
|---|---|---|
| Stripe clearing | £50,000 | |
| Trade debtors | £50,000 |
| Refunds paid to customers | Dr | Cr |
|---|---|---|
| Trade debtors | £1,200 | |
| Stripe clearing | £1,200 |
| Stripe fees | Dr | Cr |
|---|---|---|
| Payment processing fees | £1,000 | |
| Stripe clearing | £1,000 |
| Payout to bank | Dr | Cr |
|---|---|---|
| Bank | £47,800 | |
| Stripe clearing | £47,800 |
Stripe clearing: £50,000 − £1,200 − £1,000 − £47,800 = £0. The clearing account reconciliation is the check that stays. If it doesn't clear, there's a missing fee, refund, dispute or payout to find.
The VAT treatment of payment processing fees depends on the provider and the nature of the charge, so check how your fees are invoiced before setting the integration's tax codes.
The prepayment release (next on the list). MonthEndClub Co Ltd pays £18,000 for annual insurance. Instead of a manual journal each month, a scheduled journal posts automatically:
| Monthly scheduled journal | Dr | Cr |
|---|---|---|
| Insurance | £1,500 | |
| Prepayments | £1,500 |
£18,000 ÷ 12 = £1,500. The control is the monthly agreement of the prepayments balance to the register, so a cancelled policy or a schedule that runs too long gets spotted.
Step 3: set up, test and hand over
| Task | What "done" looks like | |
|---|---|---|
| ☐ | Run old and new side by side for one close | Both give the same result, or every difference is explained |
| ☐ | Document the rules | A short note of each rule, recurring journal or integration setting, with the owner |
| ☐ | Restrict who can change them | Rule changes approved and logged |
| ☐ | Keep the reconciliation | The check on the output is still on the close checklist |
| ☐ | Review rules regularly | Quarterly look at bank rules and recurring journals for anything out of date |
| ☐ | Measure the saving | Hours before and after recorded, and the next task picked |
AI shortcut: export your bank rules and recurring journals and give them to an approved AI tool. Ask it to flag rules that haven't matched anything in three months, rules that overlap, and recurring journals with end dates in the past. It turns the quarterly review into a short list.
Common mistakes and audit watch-outs
- Automating a broken process. If the manual version is unclear, fix it first.
- Dropping the check. An automated step still needs a reconciliation or review. Auditors typically look for evidence the automation's output was checked.
- Bank rules that are too broad. A rule matching on a partial name can code the wrong payments for months.
- Recurring journals that never end. Prepayment schedules that keep running after the asset is used up.
- No change control. Anyone can edit rules, and nobody knows what changed.
- Net posting from integrations. Payouts posted net hide fees and refunds. Set integrations to post gross through a clearing account.
- Treating AI like a fixed rule. AI output can vary from one run to the next. Keep a person reviewing every AI-assisted step, and only use company-approved AI tools with financial and customer data.
Tools that can help
Accounting software such as Xero, QuickBooks, Sage and NetSuite typically includes bank feeds, bank rules, recurring journals and auto-reversing journals. Billing and payment platforms such as Stripe and Chargebee often have integrations with the ledger. Spend tools such as Pleo help with receipt capture. Reporting tools such as Fathom or Syft can refresh standard reports. A company-approved AI assistant helps with matching suggestions, reviews and drafting. None of these replace a documented process and the checks on the output.
Key risks and controls
| Risk | Control | How to evidence it |
|---|---|---|
| Automation posts wrong amounts | Monthly reconciliation of the affected account | Signed reconciliation with supporting report |
| Rules changed without approval | Restricted access and change log | Change log reviewed quarterly |
| Out-of-date rules or schedules | Quarterly review of rules and recurring journals | Review checklist with date and reviewer |
| Exceptions ignored | Named owner for exceptions, cleared each close | Exceptions list at zero or explained |
| Integration posts net | Gross posting through a clearing account | Clearing account reconciled to zero or explained |
| AI errors or data leaks | Approved AI tools only; person reviews all AI output | Reviewer sign-off on AI-assisted steps |
Key takeaways
- List your close tasks with hours, then score them: hours × risk ÷ effort.
- Common first picks are bank matching, billing-to-ledger syncing and payment processor payouts.
- Fix and document the process before automating it.
- Keep a reconciliation or review on every automated step.
- Control who can change rules and review them regularly.
- AI drafts, matches and flags; a person reviews and signs off.
FAQs
What's usually the quickest win?
For many small SaaS teams, bank feeds and bank rules. They're low effort to set up and take a lot of time out of Day 1.
Does automation make the close less controlled?
It can make it more controlled, as long as each automated step still has a check on its output and rule changes are approved and logged.
Is AI the same as automation?
Not quite. A fixed rule does the same thing every time. AI makes suggestions that can vary, so it fits tasks like matching, flagging and drafting where a person reviews the result.
How do I know if automation is working?
Track hours per task before and after, the number of exceptions, and whether reconciliations still agree. If exceptions keep growing, the rules need attention.
Related guides
- The SaaS Finance Tech Stack, by Company Stage
- How to Cut Your Close from 10 Days to 5
- Stripe Payout Reconciliation: Fees, Refunds, Disputes and FX
- Bank Reconciliations Done Properly
More in Tech and Systems guides.
From the Club
Some things shouldn't be automated. Putting the kettle on is one. The Team Mug · The Low Battery WD3 Case
Join the Club. New guides, early access and members-only batches. Join here.
This guide is general information about common practice. It isn't tailored to any business, and standards, tax rules and figures change. Check your own circumstances, the latest rules and, where needed, a qualified professional before acting.
Last reviewed: October 2026.