You open your aging report and see them: dozens of overdue accounts screaming for your attention.
Some of those customers might pay after one more reminder. Some will definitely need a phone call. And a few are probably getting less recoverable with every week that passes.
The tough part is figuring out which is which.
I can tell you that most businesses still make that decision using a combination of invoice age, balance owing, staff experience, and instinct.
But AI presents you with an interesting new possibility: using the information already sitting in your receivables system to assess which accounts deserve attention first, which ones can remain in-house, and which ones should quickly be sent to expert debt collectors.
That could make one of the hardest decisions in accounts receivable a lot easier and more systematic. (Meaning your cash flow improves, you move on to making new money, and reduce bad debt write-offs.)
For a business managing its own accounts receivable, AI can provide another way to organize and prioritize the information already available to its credit and AR team.
You Already Have the Data
Think about what your accounts receivable system ALREADY knows about an overdue customer.
You probably have:
- Invoice amount and age
- Normal payment terms
- Historical payment behaviour
- Previous late payments
- Partial payments
- Broken payment promises
- Number of collection attempts
- Last successful contact
- Open disputes
- Credit limit information
- Notes from your AR team
Individually, none of those data points necessarily tells you what to do.
But together, they can tell quite a story.
This is where AI becomes your new best friend for improving cash flow.
A properly configured system can examine patterns across accounts and help identify which ones deserve immediate attention, which might justify another internal attempt, and which are disclosing characteristics that should trigger a collection review.
Let AI Help Build the Queue
Imagine beginning Monday morning with 250 overdue accounts.
Your AR team could work down the list by age or balance. Many businesses just pick one, and get at it methodically.
AI provides another, more strategic option.
It could help organize those accounts looking at multiple factors simultaneously.
For example, a $4,000 invoice that is 45 days overdue from a historically reliable customer might deserve totally different treatment from a $40,000 account that has passed 75 days, missed two promised payments, and stopped responding. (Ya think? 🧐)
Right. So, you could use AI-assisted analysis to create THREE working categories:
Continue internally: Accounts where payment behaviour and communication suggest simple follow-up is probably appropriate.
Escalate for review: Accounts showing enough change or deterioration to warrant immediate attention from an experienced AR professional.
Consider collections: Accounts where advanced age, a larger amount, failed contact attempts, broken commitments, or other factors suggest further delay will harm the likelihood of recovery.
The categories themselves not really revolutionary.
But your advantage is AI’s ability to process considerably more information, consistently and quickly, while bringing unusual patterns to your attention.
And the more consistently you apply the framework, the more useful the process can become.
Ask Better Questions
You don’t need sophisticated predictive software to start thinking this way.
Even basic AI tools like Claude, ChatGPT or Gemini can help an AR manager develop a stronger framework for evaluating overdue accounts. Gemini can work natively inside of a Google Sheet.
Using anonymized or appropriately protected information (more on that in a bit), you might ask AI to help identify:
- Which factors should increase the urgency of an overdue account?
- Which accounts have changed most significantly from their historical payment pattern?
- Where is staff time being consumed by repeated unsuccessful follow-up?
- Which large balances deserve management attention today?
- What combination of age, amount, communication history, and broken commitments should trigger escalation?
That last one is especially useful.
A surprising number of organizations don’t have one or more clear triggers for when an account leaves internal AR and goes to a collection agency.
Without one, accounts tend to linger.
Someone promises payment next Friday. Somehow it slides into next month. But then your AR manager is snowed under. Another statement goes out, and maybe someone tries calling again.
By the time the account reaches a collection agency, several valuable months have slipped away. Recoverability declines.
AI can help businesses make that process far more systematic.
Be Careful What You Give It
There is an important caution here.
Before using AI with receivables data, check your obligations first. Your contracts, customer policies, privacy requirements, and internal AI policies may restrict which tools can process account information and what data can be provided to them.
Never assume that because an AI tool is readily available, customer or client data is appropriate to upload. Consumer AI tools may be prohibited entirely for certain information. Where sensitive data is involved, use only approved systems with appropriate privacy, security, data-retention, and confidentiality controls.
Accounts receivable information can by its nature contain confidential, personal, and commercially sensitive data. Before uploading customer information into any public AI system, understand exactly how that system stores, processes, and uses the information.
In most cases, the safer starting point is anonymized data, approved enterprise AI tools, or technology operating within a controlled environment. Do not take chances on this part. It’s easy to play safe by uniquely identifying rows and deleting sensitive data fields prior to analysis if you are not already using private tools.
And of course, you should expect AI to make mistakes. It’s already a lot better than even just a year ago, and numbers tend to be less error-prone than third-party facts, but treat it like an employee whose work has to be checked for accuracy. Due diligence is still your responsibility.
It can also miss context. An AI system could recognize a pattern without understanding the business or human relationship behind it. It may assign too much weight to one variable. It may overlook information that an experienced credit professional would notice immediately.
Use it to surface information and improve decisions, rather than allowing it to make (or action) consequential collection decisions on its own.
The Stuff AI Doesn’t Know
Suppose an account looks horrendous on paper.
Let’s say the customer is 70 days overdue. They have missed two commitments and their payment history has gone to the dogs.
AI naturally pops them to the top of the escalation list.
Then your controller says, “Hang tight. I spoke with the owner yesterday. Their largest customer went into receivership. But they have financing closing next week, and they’ve given us a realistic payment schedule.”
That context changes the whole decision. You’ll watch that schedule, but the account is no longer heading straight to collections.
The reverse can happen too.
An account could look relatively sound by the numbers while someone on your AR team knows that shortly after their leader was replaced, they stopped returning calls, got penalized for a major breach of regulations, laid off a big chunk of their revenue-generating workforce, and have started disputing every invoice.
Data provides part of the picture.
And experienced, switched-on people add the all-important context.
Where Tech and Human Judgment Meet
We’ve long invested heavily in technology at MetCredit, because the right tools make experienced people more effective.
Tech tools can help prioritize accounts, identify patterns, improve workflows, and put useful information in front of recovery professionals faster.
And then the human part begins. Always.
A skilled collector can listen, negotiate, recognize hesitation, understand circumstances, determine when flexibility is productive, and know when firmer action is called for.
Those abilities are still enormously valuable. In some ways, more than ever.
As AI keeps getting more capable, I expect businesses to get much better at identifying collection risk earlier. The companies that benefit the most will combine deep AI analysis with clear policies and experienced human beings who use emotional intelligence, smarts and common sense to make best use of all the information.
AI can help prioritize and flag accounts for review. A qualified person should then make the decision to change account treatment, escalate an account, or send it to collections, particularly where the decision could materially affect an individual or business.
Start With ONE Question
You definitely don’t need an elaborate AI project to begin.
Take your overdue accounts and ask:
What information would help us recognize, sooner, that an account should no longer remain in our internal collection process?
Then determine how AI can help your team organize and interpret that information more effectively. Think about what it gives you, and use your own intelligence to make it better.
The technology is already capable of helping your business make better use of the data you have.
The biggest opportunity, though, is acting on what it tells you before a collectible account becomes a difficult one.
We’re here to help. If you have questions, reach out to our debt collection specialists anytime!
