Agentic checkout: what fraud teams need to change now

TL;DR: SEON says traffic from genAI tools to U.S. retail sites rose 693% year over year in the 2025 holiday season, while shoppers arriving via AI assistants converted 31% higher than other sources, forcing fraud teams to rely less on session behaviour and more on the identity behind the agent.

Editorial analysis by NHI Mgmt Group, based on content published by SEON: “Why Agentic Checkout Moves Fraud Detection Back to the Identity Layer”.

By the numbers:

• Traffic to U.S. retail sites from genAI tools rose 693% year over year during the 2025 holiday season.
• Shoppers arriving via AI assistants converted at a rate 31% higher than other traffic sources.

Key questions

Q: How should fraud teams score agentic checkout without relying on human behaviour?

A: They should move the primary decision away from session cues and toward identity evidence that survives automation.

Q: Why do verified agents still need customer-level fraud checks?

A: Because an agent can be authenticated while the person behind it remains fraudulent, stolen or synthetic.

Q: What are the warning signs that agentic traffic is being used for retail fraud?

A: Look for one device, identifier or session hitting many cards, accounts or promo codes in a short window, especially if the values rise or the redemptions arrive at machine-like intervals.

Practitioner guidance

• Reweight fraud models toward pre-session identity evidence Reduce reliance on mouse movement, typing cadence and form timing for automated checkout flows, and increase the weight of email reputation, phone quality, device history and payment context.
• Separate agent trust from customer trust Use signed requests or protocol checks to confirm the agent, then run a second decision layer on the human account, payment method and identity history behind the transaction.
• Hunt for machine-speed promotional abuse Look for one identifier, device or session touching many accounts, cards or redemptions in a short period, especially where the cadence is too consistent for human activity.

Bottom line: Agentic checkout weakens the value of classic behavioural fraud signals because software can now produce a convincing session without a human at the keyboard.

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