How Can Finance Teams Use AI in Accounts Payable, Reconciliation, Forecasting and Reporting?
Finance teams get the most from AI in accounts payable data capture, reconciliation matching, forecast preparation and drafting report commentary. AI should suggest and prepare while people approve, with controls that keep segregation of duties and audit trails intact.
Written and reviewed by The Technology Office · Independent technology advisory
Key takeaways
- Accounts payable and reconciliation are usually the best starting points because volumes are high and results are easy to measure.
- In finance, AI should suggest, extract and draft; a person approves anything that moves money or changes the ledger.
- Existing finance controls such as segregation of duties, approval limits and audit trails must still work once AI is in the process.
- AI forecasting and commentary are only as reliable as the underlying data, so clean master data and a consistent chart of accounts come first.
The short answer
Finance teams get the most value from AI in four places: accounts payable data capture, reconciliation matching, forecast preparation and drafting report commentary. The working rule is simple: AI suggests and prepares, people approve, and existing controls such as segregation of duties, approval limits and audit trails must still hold once AI is part of the process.
Why finance is a strong fit for AI
Finance work combines high volumes of documents, repeatable rules and recurring deadlines. Much of a typical month is spent keying invoice details, chasing unmatched transactions and rebuilding the same reports. That is exactly the kind of work modern AI and automation handle well.
Finance is also where errors are expensive and visible. A misread invoice can mean a duplicate payment; a wrong journal can flow into board reporting. So the opportunity has to be pursued with the control mindset finance teams already have: clear approvals, documented processes and evidence that every change actually works as intended.
Accounts payable
Accounts payable is usually the best place to start because volumes are high and outcomes are measurable.
Where AI helps:
- Reading invoices in any layout and extracting supplier, ABN, invoice number, dates, line items, GST and totals.
- Suggesting GL codes and cost centres based on past coding for that supplier.
- Matching invoices to purchase orders and receipts, and flagging variances.
- Spotting possible duplicates, unusual amounts or changed bank details for review.
- Drafting responses to routine supplier payment enquiries.
Controls to keep:
- Approval workflows and delegation limits unchanged.
- Supplier bank detail changes always verified out of band, never accepted on AI extraction alone.
- Exceptions routed to a person rather than auto-resolved.
What to measure: time per invoice, percentage processed without manual correction, exception rate and days to pay.
Reconciliation
Bank, intercompany and balance sheet reconciliations consume a lot of month-end effort.
- Matching suggestions – AI can match bank lines to ledger entries even when references, amounts or dates do not line up exactly, such as a batch payment covering several invoices.
- Exception investigation – for unmatched items, AI can gather related transactions and suggest likely explanations.
- Reconciliation notes – drafting the explanation for each reconciling item for reviewer sign-off.
The reviewer still approves. The benefit is that their time shifts from searching to judging.
Keep an eye on how matching rules evolve. If the AI learns from accepted matches, a run of poor approvals can teach it bad habits. Periodic sample reviews of auto-suggested matches, especially around period end, keep the process honest.
What to measure: days to complete month-end reconciliations, number of unreconciled items carried forward, and reviewer hours per reconciliation.
Forecasting
AI can help prepare forecasts by analysing historical patterns in revenue, costs and cash, and by producing a baseline that finance teams then adjust.
| Forecasting task | How AI helps | Where judgement is essential |
|---|---|---|
| Cash flow forecast | Predicts customer payment timing from history | Known large receipts, disputes, funding events |
| Revenue forecast | Identifies seasonality and trends | New contracts, pricing changes, market shifts |
| Expense forecast | Projects recurring costs | One-off projects, restructures |
| Scenario analysis | Quickly recalculates scenarios | Choosing realistic assumptions |
Forecasts are only as good as the data behind them. If your chart of accounts has changed several times, customer master data is duplicated or transactions are miscoded, fix those first. Our data and technology strategy work often starts here.
Management and board reporting
Many finance teams still assemble monthly packs manually from exports and spreadsheets. Two improvements usually work together:
- Automate the data flow – connect systems to a reporting layer so numbers update without copy and paste. Our article on reducing manual reporting with Microsoft 365 and Power BI explains how.
- Use AI for first-draft commentary – AI can describe movements against budget and prior period, highlight variances and draft narrative for the CFO to edit.
The risk with AI commentary is that it sounds confident even when it is wrong, for example attributing a variance to the wrong cause. Treat it as a draft that a finance professional verifies against the numbers.
Where should a finance team start?
A practical sequence for most finance teams:
- Baseline – record current time per invoice, days to close, and hours spent on reporting each month.
- Check existing tools – many accounting and AP platforms already include AI capture and matching features you may not be using.
- Pilot AP capture – run a subset of suppliers through AI extraction with full review, and measure accuracy.
- Add reconciliation matching – once AP is stable.
- Automate reporting data, then add AI commentary – in that order.
- Review controls – confirm with your auditors that approvals, access and audit trails remain sound.
For a method to evaluate results, see measuring AI productivity.
Data protection in finance
Finance data includes supplier and customer details, employee bank details and sometimes personal information about individuals. Only use AI tools approved by the business, with contractual terms that cover how data is stored and used. The OAIC's guidance on using commercially available AI products is a good reference for privacy considerations, and finance leaders should involve their IT governance and risk advisers before connecting AI to core finance systems.
How The Technology Office helps finance teams use AI
The Technology Office works alongside CFOs and finance managers to apply AI where it will genuinely shorten the month-end and reduce manual effort, without weakening controls. We are independent and sell no finance software, so we will tell you whether features in your existing platform are enough before you consider anything new.
Through our AI consulting service, we baseline your finance processes, design guardrails your auditors will recognise, and run measured pilots so savings are evidenced in hours and days to close. We work with finance teams in businesses of any size and industry across Australia.
Frequently asked questions
Can AI replace accounts payable staff?
AI can remove much of the manual keying and matching in accounts payable, but people are still needed to handle exceptions, supplier relationships, approvals and fraud checks. Most finance teams use the time saved to close faster and improve controls.
Is it safe to use AI on financial data?
It can be, if you use approved tools with appropriate data protection terms, restrict access to finance data, keep human approval over payments and journals, and retain an audit trail of what the AI suggested and what was accepted.
Can AI do bank reconciliations?
AI and rules-based matching can suggest matches between bank lines and ledger entries, including fuzzy matches with different references. A person should still review and approve matches, especially unusual or high-value items.
How accurate is AI for cash flow forecasting?
It depends on the quality and history of your data and how stable your business is. AI is useful for spotting patterns and preparing a baseline forecast, but finance leaders should still apply judgement about known events and assumptions.
What will auditors want to see if we use AI in finance?
Expect questions about how AI outputs are reviewed and approved, who can change AI configuration, whether segregation of duties is maintained, and whether there is a trail showing what the AI proposed and who accepted it.
Sources and further reading
- Guidance on privacy and the use of commercially available AI products — Office of the Australian Information Commissioner (OAIC)
Mentioned services
These service and regional NSW pages expand on the topics covered in this article.
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