How AI Agents Are Transforming Credit Control

Credit control has always been one of the most people-intensive parts of the finance function. Credit controllers spend their days chasing overdue payments, sending reminders, making phone calls, responding to customer emails, logging promise-to-pay dates, and escalating when invoices remain unpaid. Most of this work is conversational in nature — emails and calls — and is therefore still carried out manually by humans.

While robotic process automation (RPA) has brought efficiency gains in areas like accounts payable and reconciliations, credit control has remained largely manual. Traditional dunning software can send reminders, but it lacks context and cannot hold real conversations. Customers recognise these messages as automated, and many ignore them. That is why companies still hire teams of credit controllers to do the heavy lifting.

AI agents are now transforming this landscape. By automating the repetitive and conversational work of credit control, they reduce Days Sales Outstanding (DSO), improve cash flow, and free finance teams to focus on exceptions, disputes, and strategic tasks.

Automating Credit Control Workflows with AI

AI agents can now manage both inbound and outbound sides of credit control.

Inbound: Finance inboxes are often flooded with requests such as invoice copies, payment confirmations, PO updates, and queries about billing details. AI agents can respond automatically to these messages, pull information directly from ERP systems, and send accurate replies within seconds. They can also flag disputes or sensitive cases for human review and suggest draft replies for approval.

Outbound: The outbound side of credit control is dominated by collections and dunning workflows. AI agents can send personalised reminders, continue conversations in existing email threads, and adjust tone and timing based on customer payment behaviour. They can capture promise-to-pay dates, pause reminders until the agreed date, and follow up automatically if payment is missed. They can also escalate communication step by step, making each follow-up feel like part of a natural human conversation rather than an automated sequence.

This ability to hold two-way conversations is what makes AI agents different from traditional rule-based tools. They can answer questions, resolve objections, and adapt to the customer's response, just like a human credit controller would.

The Impact on Collections and DSO

By automating repetitive tasks, AI agents allow companies to:

• Reduce DSO by ensuring overdue invoices are chased consistently and effectively
• Improve cash flow and working capital through faster collections
• Scale credit control operations without increasing headcount
• Reduce reliance on external collection agencies that often charge high fees

Instead of relying solely on human credit controllers for every reminder and follow-up, businesses can let AI agents handle 80–90% of the workload, with humans stepping in only for exceptions and escalations.

The Evolving Role of Credit Controllers

AI does not replace credit controllers — it transforms their role. In the past, much of their time was spent on manual reminders and administrative updates. In the future, their focus will be on managing a team of AI agents, handling escalations, and resolving disputes.

They will become supervisors of digital agents, ensuring policies are followed, refining workflows, and focusing on the high-value conversations where human judgment is essential. The job moves from repetitive execution to exception management, relationship building, and strategy.

Conclusion

AI agents are reshaping credit control by automating the conversational, repetitive work that has long dominated the role. They can respond to inbound queries, run outbound dunning and collection workflows, and hold two-way conversations that drive faster payments.

For CFOs, the benefits are clear: lower DSO, stronger cash flow, fewer overdue invoices, and finance teams freed from repetitive tasks. For credit controllers, the job evolves from manual chasing to managing intelligent agents and focusing on the disputes and decisions where humans add the most value.