Accounts payable accuracy used to be something finance teams hoped to achieve through careful manual checks and spreadsheets. This requires concentration, rigorous working practices, and constant attention to detail.
Many businesses still do the majority of their AP work manually. But more and more are turning to automated AP invoice processing to save time, save money, and increase accuracy.
Today, with rising invoice volumes, multiple entities, and stricter audit expectations, hoping to achieve AP accuracy through manual-only workflows isn’t enough.
Small errors in the AP process, whether that’s one extra zero, a duplicated vendor, or a mis-coded invoice, quickly add up. The impact can be any of the following:
- Incorrect financials
- Unnecessary overpayments
- VAT and audit issues
- Damaged supplier relationships
The root cause of these errors is almost always the same — manual work. Manual data entry alone is responsible for the majority of invoice errors in many organisations. According to APQC↗, 60% of all invoice errors come from manual data entry.
AI doesn’t just make AP faster. When used well, it systematically improves accuracy and reduces errors at each stage of the process, from invoice capture to reconciliation and reporting.
This article focuses specifically on AI accuracy in accounts payable, where errors come from, how AI tackles them, and what ‘good’ looks like in practice.
The main sources of AP errors and why they suit AI so well
In a typical finance team, AP errors usually come from a familiar mix of factors:
1. Data entry and interpretation
The most common source of error in AP is simply getting the invoice data wrong. Fully manual invoice data entry is hugely time consuming, requiring someone to manually transfer invoice data to booking software.
Even traditional OCR tools rely on templates or can only interpret data in fixed positions on the page. If a supplier changes their invoice layout even slightly, fields can be misread or missed entirely. This means that someone from the finance team has to go and correct the error manually.
Single instances here and there don’t require much work. But when this is happening across hundreds or thousands of invoices a month, the time investment really adds up. With this additional human input, mistakes are guaranteed.
How does AI help?
AI-based invoice capture does something different. It uses modern OCR combined with natural language understanding to interpret the invoice. This allows it to:
- Recognise supplier names and addresses
- Distinguish invoice number from PO number
- Understand line items, VAT, discounts and payment terms
Because these models are trained on many different formats, they can handle new suppliers and layouts with much higher accuracy. They assign confidence scores to each field, meaning that only uncertain data needs human review. This combination of high baseline accuracy plus smart exception handling dramatically reduces data entry errors.
2. Coding and classification
Even when the raw invoice data is correct, it still needs to be mapped to your chart of accounts. This is where misclassification creeps in. Examples include:
- The same type of spend being coded to different accounts by different people
- Costs being allocated to the wrong cost centre or project
- Multi-entity setups leading to invoices being posted to the wrong entity
The tricky thing about these errors is that they don’t always trigger an exception. They sit quietly in your P&L, making it harder to compare costs between teams or track budget versus actuals.
How does AI help?
AI accuracy in accounts payable comes into play in these instances via machine learning. By learning from your historical postings, including supplier, description, amount, and department, the system can predict the most likely GL account, cost centre, project and entity for each new invoice.
Over time it can build a consistent ‘memory’ or how your organisation treats specific vendors and spend types. The result is fewer mis-coded invoices, more consistent classifications across entities and higher-quality data for reporting and analysis.
Finance teams of course still have final say and can override suggestions, but the default is now accurate and consistent, rather than simply “depends on who keyed it in”.
3. Matching, validation and duplicate detection
Another major source of AP errors is weak matching logic. With manual matching, or matching based on very simple rules, it’s easy to miss the following:
- Duplicate invoices with slightly different references
- Invoices that don’t match approved POs
- Incorrect totals, VAT amounts, or currencies
How does AI help?
By comparison, AI-based AP tools can apply far more sophisticated checks than a human reviewer can manage in the same amount of time. These tools can, for example:
- Compare multiple fields (supplier, amount, date, bank details, currency, PO) to spot potential duplicates
- Perform tolerant PO and GR matching, understanding partial deliveries or rounding differences
- Validate VAT and total amounts based on rules and historical patterns
Crucially, these checks run before posting and payment. That means AI accuracy in accounts payable translates directly into fewer duplicate payments, fewer supplier disputes and cleaner ledgers.
4. Approvals and policy enforcement
Finally, approvals are another common weak spot. When invoices are approved via email or chat, or when approval rules vary between teams, you see:
- Invoices going to the wrong approver
- Approvals happening outside defined limits
- Poor or missing documentation for auditors
- Invoices getting lost in unmonitored inboxes
How does AI help?
Here, AI doesn’t replace your approval policy. But it can enforce it more accurately. This could be:
- Routing invoices based on department, entity, amount, category and historical patterns
- Suggesting alternative approvers automatically when someone is on leave (this would require an HRIS integration for full functionality)
- Flagging invoices that fall outside normal behaviour, e.g. unusually large invoices from a rarely used supplier
Instead of relying on individuals to remember complex rules, AI embeds those rules in the process and applies them consistently, making your approval trail much more robust.
How Moss helps finance teams improve AP accuracy
At Moss, we’ve seen that the biggest gains from AI in AP come when you focus on accuracy and control, not just automation.
Our platform is built to help UK and EU finance teams:
- Capture invoices automatically and have key fields pre-filled with AI-powered data extraction (OCR), which can extract supplier details, invoice numbers, due dates and other core invoice data.
- Improve coding accuracy with AI-powered Smart Automation that predicts expense accounts, VAT rates, cost centres and other accounting attributes based on your historical postings.
- Enforce approval policies consistently across teams, departments and entities with customisable rules and built-in tracking and audit trails.
- Support accurate payments by combining OCR-based invoice capture, AI-powered pre-coding and structured approval workflows before export.
- Maintain tracking and audit trails across your AP workflow – from invoice upload, through review and approval, to export and payment – helping you keep documentation organised for VAT and other reporting.
Because Moss brings together invoices, corporate cards, reimbursements and budgets in one platform, finance teams can see and control company spend in a single place instead of across multiple tools and spreadsheets.
