Topic starter
11/07/2026 8:33 pm
TL;DR: Fraudsters and legitimate shoppers both become harder to distinguish during peak season, as merchants see bigger baskets, faster shipping, atypical addresses, and account warm-up behaviors that can mislead rule-based review, according to Riskified. Identity-based fraud intelligence and automated decisioning are now essential to separate malicious mimicry from legitimate holiday shopping.
Editorial analysis by NHI Mgmt Group, based on content published by Riskified: “Holiday hustle or holiday hassle? What to do when order behavior gets blurry”.
Key questions
Q: Why do holiday shopping patterns make rule-based fraud review fail more often?
A: Because peak-season behaviour changes the baseline that rules depend on.
Q: What should merchants do first when account warm-up is suspected?
A: Start by correlating browsing, small purchases, returns, and device or network changes across the account timeline.
Q: How can retailers tell legitimate holiday shoppers from account takeover activity?
A: The strongest approach is to compare the transaction against the customer’s usual identity pattern, not just the item being bought.
Practitioner guidance
- Prioritise identity-aware fraud scoring Use behavioural, account, and network context together so that seasonal shopping patterns are judged against the customer profile, not against a fixed rule set.
- Tune review thresholds for holiday seasonality Adjust manual review and step-up thresholds before peak demand so bigger baskets, faster shipping, and unfamiliar addresses do not trigger avoidable friction.
- Detect account warm-up patterns Flag accounts that gradually browse, return items, and make small purchases before placing higher-risk orders, because that activity can stage trust over time.
Bottom line: Holiday fraud is harder to control when legitimate customer behaviour starts to resemble the patterns that once signalled risk.
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11/07/2026 9:01 pm
Holiday fraud creates a trust calibration problem, not just a volume problem. The real issue is that merchant controls are being asked to distinguish intent when legitimate customers and fraudsters temporarily look alike. Manual review and static rules were built for cleaner signal separation, so they become less reliable when seasonal behaviour shifts the baseline. Practitioners should treat peak-season fraud as a governance test for decisioning quality, not simply a surge in case volume.
A question worth separating out:
Q: When should organisations prioritise automated fraud decisioning over manual review?
A: Organisations should prioritise automation when transaction volume is rising faster than review capacity, or when manual queues are causing inconsistent decisions and delayed fulfillment. Manual review still has value for edge cases, but it does not scale well in high-growth channels like travel and mobile commerce. Automation should absorb the routine cases so analysts focus on the highest-risk exceptions.
👉 Read our full editorial: Holiday fraud spikes expose the limits of rule-based review