Overview
Non-document fraud signals that assess identity risk from an IP address, email address, and phone number — adding a passive, low-friction layer to document and biometric verification.
Document and biometric verification confirm that a person holds a genuine identity document and matches it. Fraud prevention signals answer a different question: does the surrounding context look legitimate? These checks analyze the digital attributes a person presents during onboarding or authentication — their IP address, email address, and phone number — to estimate risk before, during, or alongside document verification.
Because they run on data the user already provides, these checks are typically passive and low-friction: they add little or no extra effort for legitimate users while surfacing risk that a document scan alone cannot detect, such as synthetic identities, location spoofing, or account-takeover attempts.
How non-document signals fit into a verification journey
Non-document signals are commonly used in three ways:
Pre-checks — Run early in a journey to filter out obvious fraud (for example, a disposable email or a datacenter IP) before spending time and cost on document capture.
Risk scoring — Combined into an overall risk assessment that informs an allow, deny, or manual-review decision.
Step-up triggers — When a signal looks risky, the journey can require additional verification (such as document capture or biometrics) instead of failing outright.
The signals covered in this section
Why a layered approach matters
No single signal is conclusive on its own — a VoIP phone number or a VPN connection is not proof of fraud, and many legitimate users have both. The value comes from correlation: when several signals disagree with each other or with the identity being claimed (for example, an IP in one country, a phone number registered in another, and a recently created email with no history), confidence in the risk assessment rises. Layering these signals reduces both fraud losses and false rejections of genuine users.
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