Agentic AI Set to Transform Global Payments with Speed, Efficiency and New Risks

Agentic AI is set to transform payments by enabling systems to autonomously decide and execute transactions, making financial processes faster, smarter, and more efficient. However, this shift also introduces risks around control, security, and regulation, requiring strong safeguards to maintain trust and stability.

The next major shift in global finance may not come from a new currency or payment app, but from invisible digital assistants making decisions for us. A recent IMF analysis, supported by research from institutions like the Bank for International Settlements and the World Economic Forum, highlights how "agentic AI" could transform payments. These are advanced AI systems that can understand goals, plan actions, and carry out tasks with minimal human input.

Until now, payments have always followed a simple rule: people decide, machines execute. You choose to pay, and the system processes it. Agentic AI changes this model. Instead of clicking "pay," users could rely on AI agents to decide when, where, and how to pay. This marks a shift from "click-to-pay" to "decide-to-pay," in which transactions occur automatically in the background.

From Assistants to Decision Makers

Artificial intelligence has been part of payments for decades, mainly helping detect fraud or verify identities. Earlier systems followed fixed rules, while later ones used machine learning to spot patterns. But they all had one limitation: humans stayed in control.

Today's AI is different. With advances in large language models, systems can now act more independently. For example, an AI agent could track product prices, wait for a discount, and complete a purchase without asking you. In finance, it could choose the best payment route, manage currency exchanges, or optimize how money moves across accounts.

This new ability makes payments faster and smarter, but it also introduces uncertainty. Unlike traditional systems that always produce the same output, AI decisions can vary, creating challenges for systems that depend on predictability.

Smarter Shopping and Faster Global Payments

The benefits of agentic AI are already becoming visible. In online shopping, AI agents can handle the entire process, from finding products to completing purchases and even managing returns. This saves time for consumers and helps businesses improve sales by making transactions smoother and faster.

Cross-border payments could also improve significantly. Today, sending money across countries can be slow and expensive. AI agents can analyze multiple routes, currencies, and fees in real time to choose the most efficient option. They can also manage foreign exchange risks and ensure funds are available when needed.

Another area seeing change is compliance. Instead of checking transactions after they happen, AI can monitor them continuously. It can flag suspicious activity instantly and apply rules automatically, reducing errors and improving efficiency.

The Risks Behind Automation

Despite its promise, agentic AI comes with serious risks. One key concern is loss of control. If an AI agent misunderstands a user's intent, it could make unwanted payments. Because these systems are complex, it can also be hard to understand how decisions are made or who is responsible when something goes wrong.

There are also broader risks to the financial system. If many AI agents behave in similar ways, they could act at the same time, creating sudden spikes in payments or market activity. This could strain systems and increase instability.

Cybersecurity is another challenge. AI agents interact with multiple platforms and handle sensitive financial data, which increases the chances of hacking or data leaks. At the same time, current laws are not fully prepared for a world where machines act on behalf of humans, leaving gaps in accountability and regulation.

Building a Safe Path Forward

To manage these challenges, experts suggest a clear separation of roles in payment systems. AI should handle decision-making, but actual payment execution must remain controlled by strict rules. This ensures that even if AI makes a mistake, there are safeguards before money is transferred.

Financial institutions are already working on solutions, such as creating verified identities for AI agents and adding spending limits or approval systems. Regulators are also exploring new frameworks, including real-time monitoring and testing environments, to safely introduce these technologies.

The future of payments will depend on how well this balance is maintained. Agentic AI has the potential to make financial systems faster, more efficient, and more accessible. But without proper safeguards, it could also introduce new risks.

In the end, the real challenge is not adopting the technology, but building enough trust around it so that people feel comfortable letting machines handle their money.