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15/08/2026 6:14 pm
TL;DR: High-activity devices let fraudsters scale account creation, credential stuffing, promo abuse, and metric manipulation from a small number of browsers, according to Fingerprint’s analysis. The core problem is not the volume of devices but the persistence of abuse across sessions, which makes surface-level controls easy to evade.
NHIMG editorial — based on content published by Fingerprint: High-activity device detection for fraud teams
By the numbers:
- Only 5.7% of organisations have full visibility into their service accounts.
- 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.
- 80% of identity breaches involved compromised non-human identities such as service accounts and API keys.
Questions worth separating out
Q: How should security teams respond to high-activity device signals in fraud flows?
A: Teams should treat high-activity device signals as a pattern-level abuse indicator, not as proof of one bad account.
Q: Why do high-activity devices create more fraud risk than single suspicious sessions?
A: Because the abuse is repeatable and scalable.
Q: What do fraud teams get wrong about device data?
A: The biggest mistake is treating device data as a standalone truth source.
Practitioner guidance
- Implement longitudinal device correlation Track the same browser or device across sessions so repeated account creation, login, or transactional behaviour is visible as a pattern rather than isolated events.
- Tune velocity thresholds by use case Set different limits for sign-ups, logins, promo redemption, and payments, then validate them against legitimate peak periods such as launches or campaigns.
- Combine tampering checks with step-up controls Use browser tampering detection to raise risk, then route the activity into progressive verification, manual review, or temporary action limits.
What's in the full article
Fingerprint's full article covers the operational detail this post intentionally leaves for the source:
- How persistent visitor IDs are constructed from device, network, and behavioural signals
- Threshold examples for high-activity device scoring across SaaS, fintech, marketplace, and gaming use cases
- Operational guidance for tuning graduated responses without over-blocking legitimate users
- Examples of how Smart Signals separate automation, browser tampering, and normal power-user behaviour
👉 Read Fingerprint's analysis of high-activity device detection and scaled abuse →
High-activity device fraud: are your controls keeping up?
Explore further
15/08/2026 6:21 pm
Device persistence is now a fraud governance issue, not just a bot-detection feature. High-volume abuse often succeeds because teams still reason in single-session terms, while attackers reason in device lifetime terms. Persistent identifiers change the unit of analysis from a visit to a device history, which is the right shift when fraud operations reuse the same endpoint across multiple identities. Practitioners should treat device persistence as a core governance signal, not a supplementary telemetry feed.
A question worth separating out:
Q: What should teams do when a device is highly active but may still be legitimate?
A: Use progressive friction. Increase monitoring first, then require additional authentication, then limit high-value actions, and only block when the pattern remains suspicious after review. This preserves legitimate power users while still constraining abuse.
👉 Read our full editorial: High-activity device fraud reveals the limits of legacy detection
