Our first post in this series laid out what unapplied cash costs you: distorted AR metrics, collectors chasing customers who've already paid, audit exposure, and real escheatment risk if the balance ages far enough. This post will help you understand how to solve the problem using AI.
Your company probably has a place in your ledger where cash goes to wait. A client’s payment reached your bank, but no one can tell which invoice it relates to, or why the amount paid doesn’t equal the amount of the invoice, so it sits in suspense until someone manually matches it to a bill. Sometimes resolution takes a week. But all too often, money waits for a month or longer. For most teams, reconciling that cash is a cleanup task that rarely reaches the top of to-do lists. That instinct deserves a second look, because the error costs you more than people think.
1. Clearing the backlog
Clearing the backlog is usually the easier win, and it's often an ideal first AI project for the finance and accounting teams because you can realize value quickly. This helps make the case for further investment to address the long-term challenge and paves the way for securing investment to solve more problems as well. The cash is real, it's on your books today, and applying it doesn't depend on anyone changing how they pay you.
An analyst doing this manually cross-references bank files against the aging report line by line, checks each invoice against contract terms, then digs back through billing history and prior pay applications hunting for payments made twice. An AI matching engine holds all of it — contract terms, billing detail, remittance data — in one standardized format and runs those comparisons itself, flagging the exceptions for review. These sources can include:
- Sales orders and customer purchase orders
- Billing invoices and customer account statements
- Lockbox and OCR scans
- EDI 820s,
- Bank statements
- Remittances buried in email
- Downloads sitting in customer portals
An AI solution can roll forward all of the customer billing and payment history. It can match invoice terms against sales and purchase order terms. It can apply proven rules for billing and invoice application. It can identify duplicate payments. And it can match remittances in unapplied cash to open invoices with fuzzy logic and machine learning.
This is a critical element because reference numbers are frequently missing and amounts often do not align with invoices to the penny. A model can weigh the payer name, the amount, the date of payment, and a partial invoice number together. It can identify the correct invoice where a strict rules engine would stop and route these items to a person.
The value of confidence scoring
Confidence scoring keeps your people involved where their judgment counts.
- High-confidence matches post automatically. No human intervention is required. Often, this can clear the lion’s share of the backlog quickly and efficiently.
- Ambiguous matches are automatically routed to an analyst with the suggested match and the supporting reasoning, so your team member confirms a recommendation instead of starting from a blank screen and a bank statement.
- Short-pays are flagged as likely deductions or disputes and directed to the team that owns them, so they don't sit unresolved for a month while everyone assumes someone else has them.
- Duplicate payments are identified with supporting details to show the underlying cash receipts and prior settlement so you can approve refunds or issue credit memos.
Applying this triaged approach to your existing backlog means that stuck cash begins converting into applied revenue quickly.
Proving ROI within the quarter
In most engagements, using AI to clear a backlog delivers the fastest, most visible return of the entire effort, which is why it makes such a sensible starting point. Because you're applying cash that already exists, the economic value of recovery is quantifiable within a quarter, and it often funds the remainder of the program on its own. That's a far easier case to make than committing budget now for a benefit your team won't see for a year.
One element warrants specific attention: resolving customer identity. If your company grew through acquisition, you are likely to carry the same customer under three or four names across systems that were never fully merged. When your matching works on transactions but ignores identity, cash stays scattered across records that should net to a single balance, and your aging looks messier than the underlying relationship warrants.
A strong engine can consolidate those name variations into one customer view, using the same fuzzy matching, so no one is required to hand-map “Acme Corp,” “Acme Corporation,” and “ACME Inc.” into a single entity one row at a time.
2. Keeping the problem from returning
The second challenge is creating an ongoing solution that ensures you keep unapplied cash to a minimum. The difference between daily matching and a quarterly cleanup is larger than the cadence suggests. A backlog you clear four times a year still spends most of the year distorting your metrics and pointing collectors at the wrong accounts. When matching happens the day the cash lands, your aging report stays close to accurate throughout, and the exceptions that do surface are recent enough that someone still recalls the context. Additionally, delays in resolving open accounts receivable can create customer relationship challenges if your dunning system continues to request payment from a customer who believes they have already paid their bill!
Several other things change when you treat this as ongoing work. A root-cause view tracks why items go unapplied each month, whether the cause is a particular remittance format, a particular customer, or a particular bank channel, so your team fixes the source upstream instead of re-cleaning the same balance in perpetuity. Every correction an analyst makes feeds back into the model, so your match rate climbs over the quarters instead of holding flat. And a standing governance review of aging trend, auto-match rate, days-to-apply, and escheatment exposure puts a real checkpoint on the calendar, so the program never goes dark.
Zero Suspense from RapidCanvas and Virtas Partners
To help you address unapplied cash challenges, RapidCanvas and Virtas Partners have developed Zero Suspense. This Hybrid Approach™ combines human expertise from Virtas’s Office-of-the-CFO advisory side with RapidCanvas’s cutting-edge agentic AI to deliver a solution tailored specifically to your needs, processes, and technology stack.
The engagement opens with a look-back sprint that finds and applies your historical suspense cash in sixty days or less, quickly enough that the recovery typically covers the cost of the work before the ongoing phase begins. From there, it moves into continuous assurance: daily matching, root-cause elimination, and quarterly reviews you take part in directly.
If your suspense account has been growing for a while and no one quite owns it, the look-back sprint is a low-commitment way to test AI for resolving this challenge. You’ll learn how much is genuinely recoverable and how much was headed toward an escheatment problem, before committing to additional investment.
Importantly, none of this requires replacing your ERP or reorganizing your AR team. It's a matching problem with a data problem beneath it, and both are the kind of work AI handles well, provided a person stays in the loop for the decisions that need real judgment.
If you’d like to discuss your cash, A/R, or other finance challenges, the teams at RapidCanvas and Virtas Partners would love to help. For more information, get in touch for a consultation with us today.
