The Federal Reserve Bank of Atlanta says a new class of autonomous artificial intelligence that could transform how money moves through the US payment system opens a new attack vector for hackers.
In a new post on its Take on Payments blog, Atlanta Fed Payments Forum Director Lali Shaffer says AI agents have the potential to change the payments landscape in the US.
According to Shaffer, AI agents appeal to firms because of their ability to optimize payment routing, liquidity and settlement.
“[AI agents] refer to autonomous systems that can work together toward a goal. They learn to make decisions that spark actions and adapt to situations based on their programming and data gathering… For payment systems, this could include gateway optimization or intelligent routing. The routing may entail choosing workflows based on predefined objectives, like cost, speed, liquidity or fraud risk. Agentic AI can choose between rails like FedNow, real-time payments, or Automated Clearing House and initiate the transaction based on analysis of its objectives. For example, agentic AI can trigger recurring settlements when liquidity thresholds are met. Optimizing payment workflows reduces manual reconciliation and batch scheduling for improved settlement and liquidity management.”
On the consumer side, the Fed highlights how agents can track histories, behaviors, and dispute patterns in real time to prevent fraud with adaptive risk scoring.
But amid the potential of AI agents to rewrite how money moves in the US, the Fed warns that hackers are ready to pounce on the innovation.
“As exciting as the possibilities of agentic AI innovations are, the autonomous decision-making may also open the door to illicit activities by bad actors. Account takeovers are one example and are typically tied to identity fraud. Criminals may breach systems to reconfigure agent objectives, such as purchases or payment information. As agentic AI rises, so do these ethical concerns. Some organizations maintain robust risk management programs when implementing agentic AI in consideration of the potential cybersecurity aspects of their processes.”
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