As AI agents begin to participate in transaction-related flows such as product recommendations, order creation, and payment initiation, merchants may need to answer a different set of questions when confirming transactions, handling disputes, and reviewing risk decisions. The question may no longer be limited to “Did the user make this payment?” Merchants may also need to ask, “What, exactly, did the user authorize the agent to do?”
When AI agents connect product recommendations, order creation, and payment initiation within the same conversation or task flow, payment risk management must consider more than whether a transaction looks abnormal. It may also need to examine the basis on which the transaction was initiated and whether it was supported by clear, valid user authorization.
Anomaly detection asks whether a transaction looks risky. Authorization verification asks whether the transaction can be traced to a verifiable source of user authorization.
As Transaction Entry Points Shift, Authorization Boundaries Matter More
A user may authorize an agent to purchase a set of camping and photography gear. If an item is unavailable or its price changes, the agent may automatically substitute a higher-priced accessory and proceed with the payment. If a dispute arises, the question is no longer simply whether the order is genuine. It is whether the substitution fell within the scope authorized by the user.
This differs from a conventional e-commerce flow involving add-ons or upgrades, where users typically confirm each additional item or price change themselves. In an agentic commerce scenario, users confirm a task objective, while the agent handles the intermediate steps of product selection, bundling, substitution, and execution.
When users are no longer manually confirming every SKU, the focus of a dispute may shift from whether the order had in fact been placed to whether the agent acted within the authorized scope.
For merchants, the challenge is not only whether the user granted authorization at some point. It is whether the scope of that authorization can be clearly explained afterward. For example, whether the user accepted price changes, permitted product substitutions, or agreed to add-on services may all affect how merchants explain transactions, handle disputes, and reconstruct the transaction flow.
Existing Risk Controls Still Matter, but They Cannot Answer the New Question
Payment risk management has traditionally assessed whether a transaction appears suspicious by examining signals such as transaction amount, IP address, device fingerprint, geolocation, and transaction velocity. These signals remain important, but they primarily answer one question: does this transaction look abnormal?
Once an AI agent participates in the transaction, merchants may also need to determine whether the payment was based on clear, valid user authorization.
- A normal transaction amount does not mean the authorization scope was clear.
- A trusted device does not prove that execution stayed within scope.
- A completed transaction does not mean the full authorization and execution trail can be explained in a later dispute.
AI agents, therefore, do not make traditional risk controls obsolete. Instead, they introduce a new layer of verification: whether the user’s authorization can be verified and whether execution remained within its scope. Payment risk management does not need to discard its existing rules. It needs to go beyond anomaly detection and incorporate the transaction source, authorization scope, and execution records into a verifiable transaction trail.
Assessing Transaction Trustworthiness Goes Beyond the Merchant Front End
Such an assessment cannot be completed using merchant front-end records alone. When AI agents enter recommendation, ordering, and payment flows, transaction trust depends on coordination across multiple layers: how payment credentials are protected, how agent identity is recognized, how user authorization is expressed, how transaction signals are transmitted, and how merchants can reconstruct this information during a later dispute.
Public initiatives from Visa and Mastercard show that the industry is looking beyond payment credential security. Attention is also turning to the verifiability of agent identity, user authorization, and accountability boundaries.
Visa Intelligent Commerce has publicly outlined work around tokenization of payment credentials, payment instructions and signals, and frameworks for consumer consent. Taken together, these areas point to a broader question: when an AI agent acts on a user’s behalf, how can a payment request enter the transaction flow within clearer frameworks for authorization, control, and transaction signals? [1]
Mastercard Agent Pay focuses on tokenized protection of payment credentials, trusted AI agent identification, and merchant capabilities for recognizing and accepting agent-initiated transactions. This indicates that card networks are looking beyond credential protection toward agent identity, user authorization, and accountability boundaries. [2]
These efforts are still evolving and have not yet converged on a broadly adopted, industry-wide standard. But they point to a clear shift: in agentic commerce, payment trust cannot be fully established through merchant front-end records or order screenshots alone.
For the payments industry, transaction trust is moving beyond “Is this transaction abnormal?” toward “On what basis was this transaction initiated?” This is one of the developments Oceanpayment continues to monitor as agentic commerce evolves.
Transaction Source, Authorization Scope, and Traceability Are Becoming More Important Risk Considerations
Agentic commerce is still evolving, but new transaction entry points and commercial initiatives are already appearing. For cross-border merchants, the immediate priority is not to rebuild the entire payment system. It is to understand which types of information may need to underpin future risk decisions.
Can the Transaction Source Be Identified?
Merchants need to know where a transaction came from: whether it was initiated directly by the user, triggered automatically by a system, or submitted by an agent on the basis of user confirmation. If the source cannot be distinguished, subsequent risk assessment and dispute handling may lack a reliable point of reference.
Can the Authorization Scope Be Verified?
Knowing that the user confirmed a request is no longer enough. The key is what that confirmation covered. This may include the maximum amount, permitted product combinations, add-on services, whether substitutions are allowed when products are unavailable or prices change, and which actions require renewed user confirmation.
Can the Execution Trail Be Traced?
When a dispute occurs, merchants may need to reconstruct more than the final order. They may need to trace the complete sequence linking user confirmation, agent execution, the system-generated payment request, and payment completion.
These three capabilities build on one another. Source identification provides the foundation. Authorization verification determines whether the transaction boundaries can be explained. Execution traceability determines whether the full sequence of events can be reconstructed afterward.
The Future of Risk Management Depends on Explainable Transactions
AI agents bring merchants more than a new source of traffic. They also introduce a new model of transaction initiation. In the past, merchants focused on whether a transaction was completed and whether it appeared abnormal. As agents become involved in product recommendations, ordering, and payment, merchants may also need to understand who initiated the transaction, what authorization supported it, and whether it was completed within the authorized scope.
This is where payment risk management capabilities need to continue evolving. The goal is not simply to improve the accuracy of risk decisions or add more verification steps. It is to turn transaction source, authorization scope, and execution records into a verifiable and auditable trail without compromising conversion.
As transaction models evolve, cross-border payment service providers need to look beyond payment integration itself. They must also consider how to help merchants identify risk, understand how a transaction came about, and preserve clearer evidence for subsequent dispute handling, refunds, reconciliation, and risk strategy refinement.
As transaction flows grow more complex, risk verification may need to look beyond risk itself and determine whether risk assessment, user authorization, and accountability are connected through a clear and traceable chain. Only when a transaction can be explained can risk management truly earn trust.
References
[1] Visa, The Future is Here: Visa Announces New Era of Commerce Featuring AI, 2025-04; Visa, Visa Intelligent Commerce: AI agents are already shopping — are you ready?
[2] Mastercard, Mastercard unveils Agent Pay, pioneering agentic payments technology to power commerce in the age of AI, 2025-04; Mastercard, Agentic token framework: Driving trusted AI transactions / Scaling agentic commerce with trust, 2025-10
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