The problem
An accounts receivable collections agent handles the part of credit control that nobody enjoys and everybody depends on: chasing invoices that have slipped past their due date. The work is simple to describe and hard to keep up with. Send the reminder on time, read the reply, write down what the customer said, follow up when the promised date passes.
The replies are where it gets messy. “Paid on Friday.” “We never received the goods.” “Can you resend the invoice to our new AP address?” “Next week, once our customer pays us.” Each one needs a different next step, and a dispute buried in a friendly email can sit unnoticed for weeks.
Meanwhile, the customer is usually someone you want to keep. Reminders have to be firm, polite and consistent, not a burst of messages one month and silence the next.
What we would build
We would build an AI agent that works through your open invoices toward one goal: get each one paid or into the right person's hands. It uses a short list of tools your team approves: the receivables ledger, a shared credit mailbox, your bank feed for read-only payment checks, and a set of reminder templates.
Scheduled reminders before and after each due date, following a cadence you set per customer group.
Reply sorting into paid, promise to pay, dispute, question or out of office.
Payment checks against the ledger and bank feed when a customer says they've paid.
Ledger notes with promise dates, remittance references and a one-line summary of each exchange.
Hand-offs to your credit controller for disputes, part payments and anything unusual.
How it works
Each morning the agent reads the ledger and finds invoices due for a reminder under your rules. It sends the approved message for that stage from the credit mailbox, during contact hours you set, and records it against the invoice.
When a reply arrives, the agent reads it and picks a label. A promise to pay becomes a dated note, and reminders pause until that date.
A “paid” reply triggers a check for a matching receipt; if none turns up, the agent thanks the customer and asks for a remittance reference. A request for a copy invoice is answered with the document from the ledger.
Anything that sounds like a dispute about price, quantity or quality goes straight to a person, with the email thread and a short summary attached. Reminders on that invoice freeze, and only a person can restart them.
What the AI does, and what your team decides
Under the hood it is an ordinary agent: it works toward one goal with a fixed toolset, pauses at an approval gate and writes everything to a log. The lines between what it may do and what a person decides are written down before it sends a single email.
The agent may act alone to send approved reminders, resend invoices, read and label replies, check for payments, record promise dates and write ledger notes.
A person must approve any message outside the templates, a payment plan, a credit note, interest or late fees, placing an account on hold, and any reply to a dispute.
The agent never changes amounts in the ledger, issues refunds, edits bank details or threatens legal action.
When the agent is unsure how to label a reply, it treats it as needing a person. A wrong “paid” label costs more than a short delay.
What the research says
Finance teams already use AI, mostly on payables. In a Gartner survey of 183 CFOs and senior finance leaders in mid-2025, 59% said their finance function used AI. Among those users, accounts payable automation (37%) was the second most common use after knowledge management (49%) (Gartner). This agent brings the same kind of automation to the receivables side.
Payment emails are a target for fraud. The FBI's Internet Crime Complaint Center logged 24,768 business email compromise complaints in 2025, with reported losses of about $3.05 billion (FBI IC3 2025 report). That is one reason the agent sends only approved templates and passes anything unusual to your credit team.
Governance frameworks exist. The US National Institute of Standards and Technology published its AI Risk Management Framework for voluntary use in January 2023, built around four functions: govern, map, measure and manage. A Generative AI Profile followed in July 2024.
Guardrails
Data access. The agent can read open invoices, customer contacts and the bank feed, and can write notes. It cannot change balances, post credit notes or touch payment details.
Human approval. Templates, cadence, contact hours and escalation steps are approved by your credit team and stored as settings. Anything outside them waits for a person.
Logging. Every reminder, reply, label and note is kept with a timestamp, so you can show a customer, an auditor or your own team exactly what was said and when.
Scope. This design is for business customers. Consumer debts carry stricter rules and would need a separate legal review.
Is an accounts receivable collections agent right for you?
It fits teams with many open invoices and a steady stream of routine replies. Ask yourself:
How many invoices pass their due date each month, and who chases them today?
Do you have reminder templates and an escalation path your team already trusts?
Can your accounting system share invoice status and receipts through an API or export?
Which customers should never get an automated message?
Collections feed your cash position, so see how cash flow forecasting with machine learning uses the same ledger data. Our AI agent development and workflow automation pages describe our approach to building it, and our fintech software page shows related work.
