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Bectran Product Team
I
December 8, 2025
7 minutes to read
A customer calls. They're angry. They sent a remittance for specific invoices two weeks ago, but their portal still shows those invoices as open. You pull up the account, and the balance matches. The payment was received. But when you drill down into the line items, you see an invoice number that makes no sense. It doesn't match your standard numbering convention. It doesn't correspond to any shipment or service date.
The AI didn't apply the cash to the open invoices the customer intended to pay. Unable to find a perfect match, the system improvised. It created a phantom record, a "funky" invoice number, to balance the books. Now you have a three-way reconciliation nightmare: the bank has the cash, the customer thinks they paid Invoice A, and your ERP thinks they paid Non-Existent Invoice B.
This is a systemic failure of over-enthusiastic automation. The promise of "touchless cash application" collides with the reality of financial accuracy.
The appeal of Artificial Intelligence in cash application is obvious. The volume of transactions, combined with the messy reality of decoupled remittances (checks arriving separately from email PDFs, portal downloads, and EDI transmissions), makes manual posting unsustainable. We are promised systems that learn and match automatically.
A dangerous edge case exists in many modern AR automation tools. When the AI encounters a payment it cannot confidently match to an open item, it faces a binary choice: leave it as unapplied cash (requiring human intervention) or force a match to clear the queue.
Systems are configured to prioritize clearing the queue over data integrity. Credit teams see this constantly: when the system can't match a payment, it decides to put it on account using some random invoice number.
The system actively creates new work. By inventing a "funky other invoice number" to park the cash, the AI is effectively hallucinating. It fabricates a transaction to satisfy a programming logic that abhors a vacuum.
For the AR team, this transforms a simple "unapplied cash" task into a complex forensic accounting project. You must now un-apply the cash from the fake invoice, void the fake invoice (if the ERP allowed it to be created), find the real invoices, and re-apply the cash correctly. The automation that was supposed to save time has tripled the workload for that specific line item.
To prevent this, we must understand why the software behaves this way. It is rarely a glitch in the traditional sense. It is usually a result of logic gaps in how the cash application engine interacts with the ERP.
Many cash application tools measure success by hit rate, the percentage of incoming payments that are posted without human touch. To inflate this metric, algorithms are sometimes biased toward posting something rather than nothing. If the system OCRs a reference number on a check stub that looks like an invoice number (even if it isn't in the open item list), it might assume the ERP is out of date and post it anyway, creating a payment on account referenced by that bad data.
In a standard accounting workflow, if a payment doesn't match an invoice, it should sit on the account as unapplied cash (a credit balance). Some ERP integrations require a reference field to be populated to post the batch. If the AI cannot find a valid invoice number, and the integration doesn't have a default "UNAPPLIED" code, the AI might grab the nearest string of text from the remittance advice (a purchase order number, a date, or a truncated memo field) and shove it into the invoice reference field.
Optical Character Recognition (OCR) reads the remittance advice. It might read a messy scan of "INV-1001" as "INV-7001." If the system lacks a validation step that queries the ERP to ask, "Does INV-7001 actually exist?" before posting, it will happily post the cash to the non-existent invoice. The result is a funky number that exists in the cash app history but nowhere in your sales ledger.
Sometimes, the funky number is the system's attempt to handle a discrepancy. If a customer pays $900 on a $1,000 invoice, the system might apply the $900 and create a residual item. If the configuration is poor, that residual item might get assigned a generated ID (like the payment reference number) rather than the original invoice number with a suffix. To the Credit Manager, this looks like a random, unmatched string cluttering the account.
The goal of AR automation is 100% accuracy with maximum possible automation. When these two goals conflict, accuracy must win. Four conceptual frameworks ensure your cash app solution aids your team rather than creating clean-up work.
Your cash application logic should operate under a strict rule: Do No Harm.
Before any cash is posted to the ERP, the system must perform a valid existence check.
The Rule: Never let the AI write to the ERP unless it is writing to a validated, pre-existing object.
Credit Managers should demand that their systems categorize matches into confidence tiers.
The funky invoice number problem arises when Tier 3 items are treated with Tier 1 privileges.
When you find a misapplied payment, you need to know why the AI made that decision. Was it a configured rule? Was it a machine learning hallucination? Was it a bad OCR read?
Modern cash app systems must provide a decision log. You should be able to click on the transaction and see:
If you cannot see the logic, you cannot fix the root cause. You are left fighting the symptoms forever.
Culturally, many AR teams are pressured to keep Unapplied Cash metrics low. This pressure encourages the configuration of systems that force-match payments to clear the unapplied bucket.
We need to flip this mindset. Unapplied cash is better than misapplied cash.
Your strategy should be to optimize the speed of resolving unapplied cash, not to eliminate the bucket by allowing the AI to guess.
Nothing erodes trust faster than calling a customer to collect on an invoice they have already paid, only to discover your system applied their money to a phantom invoice number. It makes your organization look disorganized and incompetent. In B2B relationships, where credit limits and payment terms are based on trust, this friction can cause customers to withhold future payments or migrate to competitors.
If cash is applied to a "funky" number, the real invoice remains open and aging. Eventually, this real invoice will trigger credit holds or dunning letters. If you have automated your credit holds based on aging buckets, you might inadvertently stop a shipment for a loyal customer because your cash app AI failed to clear the correct line item. Conversely, if the AI applies cash to the wrong account, you might release an order to a customer who is actually over their limit.
Fixing a misapplied payment takes 3x to 5x longer than applying it correctly the first time.
If your AI solution is creating "funky" numbers, it is a noise generator. The labor savings you calculated in the ROI phase are being eaten up by the remediation phase.
Automation in cash application is necessary, but it cannot be unchecked. When a system decides to put payments on account using random invoice numbers, it indicates a tool that has been granted too much autonomy without enough guardrails.
To ensure your cash app process is helping rather than hurting, take these steps:
Ask your vendor or IT team: If the system cannot match a payment to an existing open item, what exactly does it do? If the answer is anything other than "Flag for review" or "Post to Unapplied Cash," change the configuration immediately.
Pull a report of all payments posted in the last 90 days. Filter for invoice numbers that do not match your standard ERP nomenclature (e.g., wrong length, wrong prefix). This will reveal the scale of the hallucination problem.
Deliberately feed the system a payment for a non-existent invoice. Watch what it does. Does it reject it? Does it create a new record? This stress test is the only way to verify your safety nets are working.
When evaluating new tools or updates, look for Explainable AI. You need to know why a match was made. If the system is a black box, it is a liability.
Cash application is the final mile of the revenue cycle. It is where revenue becomes realized cash. You cannot afford to let algorithms compromise the integrity of that finish line.
Tired of AI inventing invoice numbers? Bectran's cash application includes validation protocols that prevent posting to non-existent invoices, with full audit trails showing why each matching decision was made. See Bectran's Cash Application at work.
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