Agentic AI: The Future of AI in Payments

In our previous blog post, we discussed the current state of AI and automation in the payments industry, where automated systems can often tell your customers why their payment failed but are unable to fix it. A good starting point, but one that is limited when interactions stray from predetermined paths.

For example, when something goes wrong—a declined payment, a policy exception or a hardship request—many current automated systems default to a human. That creates friction in the payment experience and can lead to delays, and in some cases, lost revenue.

But what if an AI agent with the right tools, information and guidelines could resolve many of those friction points as they happen?  

Executive summary

Right now, automation is embedded across service and operations environments to drive efficiency through self-service tools such as chatbots and intelligent virtual assistants. We’re also seeing task-specific autonomy begin to take hold in high-volume, rules-driven workflows in payments, financial services and collections.

These systems work well when interactions follow expected paths, but exceptions often require human intervention. The next generation of autonomous AI agents will be different, going from reactive automation to decision-capable systems that can take action across workflows. When properly designed, they won’t just respond, they’ll interpret context, access real-time data and standard operating procedures, apply business rules and execute the next best action. 

This is where agentic AI and Payment Experience Management (PEM) come together. PEM focuses on continuously improving the entire payment journey, and AI provides new ways to understand what’s happening and take action within that journey. The caveat is that AI will only be as good as the systems they’re embedded in, which is why the underlying modern Payment Experience Management platform matters so much. 

Agentic AI solves complex payment issues

What would this look like in action? Imagine a scenario where a borrower attempts to make a loan payment, but the transaction is declined due to insufficient funds. 

Instead of AI explaining the decline and routing the issue to a servicing queue, a payment-native AI agent identifies the decline reason, evaluates available payment methods, offers a compliant repayment alternative, updates the servicing workflow and completes the payment interaction in real time. And this doesn’t just happen over the phone or on the web. The agent can coordinate the interaction across SMS, phone and your website to help the interaction reach a successful conclusion during the moment of intent. 

The potential result is more successful payment resolutions with less unnecessary human intervention. When human support is required, personnel can also have better visibility into what has already been done and how to move forward efficiently. 

Not long ago, this would have sounded like science fiction. Today, it’s where the market is heading. 

It’s not just AI, it’s the systems AI agents have access to

Like any good science fiction story, this also comes with a cautionary tale. From a technology standpoint, the capability exists or is close to existing today, which is why many businesses are already being pitched AI point solutions. But AI is only as good as the systems it is embedded in and the governance, observability and escalation guardrails in place.

There is real risk when autonomy is bolted on top of disconnected systems or deployed without the correct guidelines in place. At best, an AI agent without access to data, policy engines or transaction context can create more frustration than value for your customers. At worst, it can make erroneous decisions that then have to be discovered and manually corrected. 

Two factors will be particularly important for deploying agentic AI effectively in payments: 

1. Security and oversight: Agentic AI needs to be designed for the security, compliance and reliability requirements of regulated industries. It’s not about who deploys AI fastest, but who integrates it most deeply into operational systems of record, with the right governance, observability and escalation guardrails in place.

2. Integration with the right systems: Agentic systems become more effective when they are deeply integrated with the payment data, business rules, tools and workflows needed to understand context and take action. The more modern your payment platform is, the more effective your AI will be. 

Our very own John Minor, Chief Product Officer at PayNearMe, said it best: “Autonomy without infrastructure is just a smarter chatbot. Autonomy wired into core operational systems is where real value emerges.”

Why this matters

For folks in the payments world, the objective is simple: lend money or provide services and collect payments. Friction across the payment experience can create delayed payments, higher operational costs and lost revenue.

That’s where many AI systems fall short today. They optimize for call containment or efficiency metrics, but aren’t connected deeply enough into the payment experience to drive the desired business outcome: getting paid. 

With the forthcoming wave of agentic AI, AI agents will become more capable of handling exceptions and complex cases within clearly defined guidelines. This could allow more cases to be concluded successfully without human intervention, reducing costs, enabling more timely payments, and providing a better experience for businesses and consumers alike.

No longer just science fiction, but business fact.