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The unified orchestration layer for wallets and alternative payments
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The secure repository for all your payment methods
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Workflow-driven payments intelligence for smarter routing and higher auth rates
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A flexible fraud and authentication layer. Instantly add advanced fraud tools and 3DS
One integration to sell inside ChatGPT, Gemini, and every agentic platform
The visual workflow builder that ties fraud, identity, and payment routing together in one place
The unified orchestration layer for wallets and alternative payments
The secure repository for all your payment methods
Workflow-driven payments intelligence for smarter routing and higher auth rates
A flexible fraud and authentication layer. Instantly add advanced fraud tools and 3DS
One integration to sell inside ChatGPT, Gemini, and every agentic platform
The visual workflow builder that ties fraud, identity, and payment routing together in one place
Device Fingerprinting
Device fingerprinting is the process of collecting and combining technical attributes of a user's device, browser type and version, operating system, screen resolution, installed fonts, time zone, language settings, IP address, and dozens of other characteristics, into a composite identifier that can distinguish one device from others, even when cookies are cleared or the user is logged out.
In payments fraud detection, device fingerprints serve as a persistent identifier that links transactions across sessions, accounts, and time. A fraudster who creates multiple accounts to circumvent single-account fraud rules will likely use the same device across those accounts, and a device fingerprint will reveal that link even when usernames, emails, and payment methods are different. Device fingerprints are also used to detect anomalies: a returning customer whose device fingerprint has changed significantly may be using a new device (normal) or may be the victim of account takeover (concerning), and context distinguishes the two.
Device fingerprinting is a core component of the 3DS2 / EMV 3-D Secure authentication flow. 3DS2 collects device fingerprint data as part of its 100+ data element exchange with the issuing bank, giving the issuer rich device context for its Risk-Based Authentication (RBA) decision. Merchants that implement 3DS2 are effectively leveraging device fingerprinting as part of their authentication flow whether or not they have a separate fraud tool.
Standalone device intelligence platforms, such as Fingerprint (formerly FingerprintJS), ThreatMetrix, and Sardine, provide richer device fingerprinting and analysis than is available through 3DS2 alone, including bot detection, emulator detection, and VPN/proxy identification.
Frequently Asked Questions
Is device fingerprinting affected by privacy-focused browsers and VPNs?
Privacy-focused browsers (Firefox with enhanced tracking protection, Safari with Intelligent Tracking Prevention, Brave) and VPNs do reduce the effectiveness of device fingerprinting by limiting the attributes available for collection and masking the IP address. This is an ongoing technological tension: device fingerprinting vendors continuously find new attributes to collect, while browser vendors and privacy advocates work to limit attribute availability.
What is the difference between device fingerprinting and device binding in 3DS2?
Device fingerprinting collects device attributes to create an identifier used for fraud scoring and authentication context. Device binding in 3DS2 v2.3 is a specific authentication feature where a cardholder can register a trusted device with their issuing bank, so that future transactions from that device can be approved with higher confidence, the issuer knows the device is the registered one belonging to the legitimate cardholder.
Can device fingerprinting identify fraud in mobile apps?
Yes, though the implementation differs from web environments. Mobile apps use SDKs rather than browser APIs to collect device signals, hardware identifiers (IMEI, device model, operating system version), app-level signals (install time, usage patterns), and network characteristics. Mobile device fingerprinting can also detect rooted or jailbroken devices (a fraud risk signal), emulators (often used by fraudsters to simulate multiple devices), and device spoofing.
Related Terms
