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11/07/2026 2:02 pm
TL;DR: As AI agents conduct more commerce, merchants lose interaction signals such as device, location, and behavioural context that fraud models rely on, weakening transaction assessment and increasing the risk of false positives and missed fraud, according to Riskified. The practical shift is toward networked intelligence, agent detection, and monitoring for transaction spikes rather than relying on human-style behaviour signals.
NHIMG editorial — based on content published by Riskified: agentic commerce and the loss of fraud detection signals
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes and as quickly as 9 minutes in some cases.
Questions worth separating out
Q: What breaks when fraud systems lose interaction data in agentic commerce?
A: Fraud systems lose one of their strongest context layers.
Q: Why do AI agents complicate transaction risk assessment?
A: AI agents complicate transaction risk assessment because they can act on a customer’s behalf without producing the human behavioural cues that fraud models expect.
Q: How do security teams know whether agentic commerce controls are working?
A: Look for stable fraud loss rates, reduced false positives on agent-driven orders, and consistent classification of legitimate automation versus malicious bots.
Practitioner guidance
- Recalibrate fraud models for low-interaction sessions Test how scoring changes when device, click, geolocation, and connection signals are absent or generic.
- Add explicit agentic transaction classification Tag transactions that originate from AI agents so review rules can distinguish authorised automation from abusive bot traffic.
- Use consortium intelligence as a compensating control Incorporate cross-merchant intelligence to recover context when local behavioural evidence is weak.
What's in the full article
Riskified's full article covers the operational detail this post intentionally leaves for the source:
- How its fraud models separate interaction, customer, and order data in agentic commerce flows.
- Examples of the transaction signals merchants lose when AI agents place orders on behalf of customers.
- Why consortium intelligence can supplement weaker local telemetry for fraud review.
- Operational monitoring ideas for spikes in agentic transactions that may indicate abuse.
👉 Read Riskified's analysis of how agentic commerce changes fraud detection →
Agentic commerce and fraud detection: what changes when signals disappear?
Explore further
11/07/2026 2:11 pm
Agentic commerce creates a transaction-verification gap, not just a fraud gap. When the customer is mediated by an AI agent, the evidence set used by fraud teams becomes materially thinner. That changes how identity, device trust, and behavioural assurance intersect in payment flows. Practitioners should treat this as a governance issue that spans fraud, identity verification, and delegated access decisions.
A question worth separating out:
Q: Who is accountable when AI agents place fraudulent transactions?
A: Accountability usually sits across fraud operations, identity governance, and the business owner of the delegated workflow. The key question is whether the organisation had a control model that recognised agent-mediated activity and applied appropriate verification, review, and monitoring before allowing commerce at scale.
👉 Read our full editorial: Agentic commerce removes interaction signals that fraud models rely on
