Enterprise Payment Errors Report 2026
Too Much Room for Error
$1 in every $48
of payment value involved an error Trustmi prevented.
Payment errors have long been treated as a cost of doing business. But how much of that cost is actually unavoidable?
The scale
Counting errors doesn’t tell the whole story
Traditional accounts payable error statistics tell us how often mistakes happen, but not how much payment value is involved. This analysis looks at the dollar value of errors prevented, then goes inside one global enterprise to examine how probable duplicates moved through established payment processes. What emerges is a picture of errors that aren’t an inevitable cost of doing business.
About half of affected payment value involves duplicates
The same invoice entered twice, or two payments against one invoice
An incorrect amount, invoice number, or other keyed detail
An invoice issued in one currency and paid in another
Payment by the wrong business unit, or a missing invoice or PO
| Payment error category | Estimated share of affected payment value |
|---|---|
| Duplicate invoices and payments: The same invoice entered twice, or two payments against one invoice | ~50% |
| Data-entry errors: An incorrect amount, invoice number, or other keyed detail | ~20% |
| Currency mismatches: An invoice issued in one currency and paid in another | ~20% |
| Wrong entity and skipped steps: Payment by the wrong business unit, or a missing invoice or PO | ~10% |
Note: Estimated distribution of the $5 billion in payment value associated with errors,
based on Trustmi’s error classifications.
But why do duplicates get through established controls in the first place?
To find out, we looked inside one global enterprise’s payment records.
Inside one global enterprise
What the payment records revealed
It’s one thing to know that duplicate payments happen. It’s another to see how they move through a global enterprise’s payment processes.
We had a chance to do just that. The company had established AP processes and ERP duplicate-detection controls, and was already using Trustmi for fraud protection, but not for pre-payment error prevention. So we went back through a full year of real ERP records to trace what happened to probable duplicates along the way.
Duplicate payments were paid twice in
- 20 business entities
- 15 countries
- 1 year of ERP records
Probable duplicates identified
~85 cases
~$790K in value
Paid twice
59 cases
~$560K in duplicate payments
This wasn’t a one-off problem or a particularly bad month. Probable duplicates showed up throughout the year, mostly among the smaller, everyday invoices AP teams handle all the time. But it wasn’t just the small stuff getting through. Two cases involved six-figure invoices, and both copies were paid.
Note: This deep dive concerns probable duplicate invoices only, not the enterprise’s total payment-error risk. Flagged amounts are not confirmed financial losses.
How it starts
A single character can make a duplicate look new
Duplicate invoices don’t always have identical invoice numbers. A vendor revision, an OCR error, or even a suffix added to get past an ERP duplicate warning can make the same invoice look like a new one.
We can’t tell from the records why the numbers differed. But we can see how often those differences appeared, and how many probable duplicates still cleared a payment run.
One added character. Two different invoice numbers.
of cases paid twice had invoice numbers that weren’t identical.
| Invoice-number pattern | Probable cases | Both copies cleared a payment run |
|---|---|---|
| Letter added to number | 24 | 24 |
| Exact same invoice number | 45 | 23 |
| Number formatted differently | 15 | 12 |
When invoice numbers matched exactly, 23 of 45 probable duplicate cases were paid twice. When a letter had been added, both copies cleared a payment run in all 24 cases. An ERP can check for duplicate invoice numbers and still miss two records representing the same bill.
What happens next
Often, there is time to catch the duplicates
Many probable duplicates showed up in the ERP within days of the original invoice, often with the same invoice date. They weren’t necessarily hard to spot because too much time had passed. In many cases, both the original invoice and its probable duplicate were in the system well before payment.
Original invoice enters ERP
The first invoice record is created.
Probable duplicate enters
Often within days of the original.
Both clear a payment run
In many cases, the same payment run.
Note: Illustrative sequence based on patterns identified in the retrospective analysis.
Entered within one week of the original
Entered on the same day
Carried the same invoice date
Typical time the second copy remained in the ERP before payment
More than half cleared the same payment run
The original and probable duplicate were moving through the same payment cycle. That’s an opportunity to catch the problem before funds are released, rather than spending time afterward investigating what happened, contacting vendors, and trying to recover duplicate payments.
And many of those payments followed the standard PO-matched invoice process.
Why it matters
The controls were there. So were the duplicates.
duplicates paid twice came through the standard PO-matched invoice process.
These findings don’t suggest that established AP controls are unnecessary. Matching, approval, and duplicate checks each serve an important purpose. But going through the standard PO-matching process didn’t prevent these duplicate payments.
An invoice can pass its checks and still be a duplicate.
The way forward
The fix isn’t another check. It’s the bigger picture.
When errors get through, the instinct is often to add another check. But another check working with the same information may not catch what the others missed.
The bigger opportunity is to evaluate each payment against the vendor’s complete transaction history, not just the records available to one system.
One vendor. Four partial histories.
ERP 1Entity A
ERP 2Entity B
ERP 3Entity C
ERP 4Entity D
Connected vendor payment history
Payments, invoice patterns, amounts, and accounts across systems
Compare each payment in context
Identify suspicious changes and probable errors before release
Illustrative. Each mark is a payment to the same vendor; height shows amount.
In a global enterprise, the same vendor may be paid by multiple business entities, sometimes under different vendor records and through different ERPs. Each system may have an accurate record of its own transactions without seeing the vendor’s full payment history across the organization.
In fraud, the warning might be a payment that doesn’t fit the vendor’s established behavior.
In payment errors, it might be a transaction that looks too much like one already processed.
Connecting those records makes it possible to compare payments against a broader history and identify probable duplicates even when invoice numbers or other details differ. This approach helped identify the probable duplicates in the enterprise investigation, and it was also used to prevent $5 billion in payment errors across $240 billion in payments.
The broader lesson applies to payment risk, whether fraud or errors. Both require context across systems and Behavioral AI that understands normal vendor and payment behavior to recognize what individual checks can miss before money moves.
Different problems. Same need for context.
We’ve spent years treating payment fraud and payment errors as two separate problems. But both come down to understanding what’s normal for a vendor. With behavioral AI, you can spot the payment that doesn’t belong and the payment that shouldn’t be there twice. It’s one approach to solving two problems.”
Putting it into practice
Catch payment errors before money moves
Explore how behavioral AI helps finance teams identify payment errors across systems, bringing the evidence together so they can act before payment.
Methodology
Unlike traditional accounts payable error statistics based on incident counts, this analysis examines the dollar value affected. It covers $240 billion in payment value processed over one year, including $5 billion in errors prevented before release. The category distribution reflects estimated affected payment value, not the number of error incidents.
The enterprise case study is a separate retrospective review of ERP records, focused specifically on probable duplicate invoices. The review used vendor, amount, and invoice-number similarities, while excluding recurring monthly charges. Flagged amounts represent potential duplicate-payment value, not confirmed financial losses. The analysis does not establish whether payments were subsequently recovered.
Behavioral AI-powered security 
Protection on day one 
10-15x ROI 