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15/08/2026 6:11 pm
TL;DR: Payment platforms are being forced to balance fraud prevention, KYC and AML compliance, and customer conversion, while AI models struggle with noisy signals that drive false declines and missed fraud, according to Fingerprint. The practical shift is toward richer, persistent signals that span checkout, authentication, onboarding, and payout flows.
NHIMG editorial — based on content published by Fingerprint: device intelligence for payment fraud, false declines, and compliance
By the numbers:
- Merchants reject 6% of all e-commerce orders, but between 2% and 10% of those rejections are actually legitimate orders.
- False declines cost merchants $81 billion in revenue.
- By 2030, card-not-present fraud is projected to reach $49 billion annually.
Questions worth separating out
Q: What breaks when fraud controls rely on a single signal?
A: Single-signal decisions are easy to bypass because one indicator can be benign in isolation.
Q: Why do synthetic identities bypass many verification processes?
A: Synthetic identities blend real and fabricated details in ways that satisfy shallow checks while hiding fraud intent.
Q: How can payment teams reduce false declines without opening more fraud risk?
A: Use richer pre-decision signals so the system can distinguish legitimate variation from suspicious reuse.
Practitioner guidance
- Unify signals across all transaction flows Connect checkout, 3DS, login, onboarding, and payout telemetry to a shared risk layer so one device or identity footprint can be evaluated consistently across the customer journey.
- Separate trusted variation from suspicious reuse Tune decisioning so legitimate changes such as new locations, browsers, or devices do not automatically trigger declines when the broader behaviour remains consistent with a real customer.
- Bind onboarding risk to device persistence Use persistent device identifiers and tamper signals to route high-risk sign-ups into deeper review while letting low-risk users clear without unnecessary friction.
What's in the full article
Fingerprint's full article covers the operational detail this post intentionally leaves for the source:
- How its device intelligence model combines more than 100 real-time signals into a persistent visitor identifier
- How Smart Signals such as tampering, proxy use, bot behaviour, and velocity checks can be applied in decision flows
- How the platform positions device intelligence across checkout, onboarding, and fraud scoring workflows
- How its privacy and compliance claims are framed for regulated payment environments
👉 Read Fingerprint's analysis of device intelligence for payment fraud and compliance →
Device intelligence in payments: what it means for fraud teams?
Explore further
15/08/2026 6:18 pm
Persistent device trust is becoming a core payment governance control. The article shows that transaction risk is no longer decided at a single point. It is accumulated across onboarding, checkout, authentication, and payout flows, which means teams need continuity in their trust data as much as they need better fraud models. For practitioners, the governance challenge is linking repeated behaviour to a stable identity signal without creating new privacy or friction issues.
A question worth separating out:
Q: Which compliance controls matter most when fraud and KYC overlap?
A: The most important controls are identity verification, risk-based escalation, and evidence that persists across the whole transaction lifecycle. KYC and AML processes work better when low-risk users are fast-tracked and suspicious devices or identities are routed into deeper review. That balance reduces manual friction while improving detection.
👉 Read our full editorial: Device intelligence changes the fraud and KYC trade-off
