Every fraud system depends on one core capability: recognizing who’s actually behind an interaction. 

Device fingerprinting is one way to do that. It’s a technique that creates a unique identifier (fingerprint) for a device based on its combination of hardware and software details, such as the user-agent, which is a string of text that a device sends to websites that identifies the browser, OS, and device.

Device fingerprinting: A technique that creates a unique identifier (a "fingerprint") for a device by collecting its specific hardware and software information, like , browser, OS, plugins, and screen resolution

Device fingerprinting can help recognize returning devices across sessions and, in some cases, after cookies are cleared, but it’s sensitive to environmental changes and easy to disrupt.

That sensitivity creates a tradeoff. If the fingerprint is too strict, small changes in a browser or environment can split one real user into many identities, called divisions. If the fingerprint is too loose, multiple people can collapse into the same identity, resulting in a collision. 

Both outcomes hurt accuracy. Divisions fragment behavior and hide repeat activity, while collisions mix unrelated users together, increasing false positives and eroding trust in downstream rules and models. Most teams end up optimizing for one at the expense of the other.

Sardine’s Visitor Fingerprint was built to solve this problem. We’ve evolved device fingerprinting by adding location and network-level context and stability features, giving fraud teams a more accurate way to distinguish between real-world users that’s resilient to common resets.

Summary:

• Traditional device fingerprinting forces a tradeoff between collisions and divisions, which degrades accuracy and trust in fraud decisions.
  
• Visitor Fingerprint evolves fingerprinting by combining device, browser, and network signals with added stability features and a Confidence Score, a more reliable method of rating accuracy.
  
• In real-world testing, Visitor Fingerprint reduced collision rates by up to 97% and division rates by up to 95% compared to legacy approaches.
  
• Cleaner identity leads to better rules, stronger models, earlier fraud detection, and less friction for legitimate users.

What is a Visitor Fingerprint?

Visitor Fingerprint is a persistent identifier that recognizes users across sessions, devices, and networks with higher accuracy and stability than device fingerprinting alone.

Instead of relying solely on device or browser attributes, Visitor Fingerprint looks at the full environment a user operates in, including location, network behavior, and other stability features.

Together, these signals create a stickier identifier that is difficult to evade using common techniques like cookie clearing, app reinstalls, or other environment changes.

Because no identifier is perfect, Sardine pairs each Visitor Fingerprint with a Confidence Score. A more reliable method of rating accuracy, this score helps teams understand how unique and reliable a fingerprint is, so it can be used appropriately in rules, models, and investigations.

How Visitor Fingerprint is different from traditional device fingerprinting

Traditional device fingerprinting focuses primarily on what the device looks like. Visitor Fingerprint extends that view with network context and identity stability over ttime.

When tested against legacy device and location-based approaches in real-world conditions, Visitor Fingerprint showed meaningful reductions in both division and collision rates, with improvements of up to 95% and 97%, respectively.

By reducing collisions and divisions, your team improves accuracy, lowers false positives, and gives downstream rules and models cleaner inputs.

How to use Visitor Fingerprint in fraud detection 

Once activity is correctly tied together, teams can apply aggregation rules to surface patterns that don’t appear at the session level, helping identify clusters of risky behavior and respond earlier to emerging attacks. This can look like: 

Detecting coordinated abuse on web traffic

Using high-confidence Visitor Fingerprints, teams can flag risky behavior such as:

• A fingerprint associated with many devices over a short window
• A fingerprint shared across multiple customer accounts in a single day
  

A simple rule might look like:

• Visitor Fingerprint Confidence ≥ 70
• Fingerprint tied to more than four devices over seven days 
• Fingerprint shared by more than five users over one day
  

When combined with signals like non-residential IPs or emulator usage, these patterns can be strong indicators of coordinated fraud rings.

A new standard in user identification

Visitor Fingerprint is part of Sardine’s broader device and behavior capabilities, built to give fraud teams durable identity, clearer signals, and more control over risk decisions.

To discover how Visitor Fingerprint can enhance your fraud detection capabilities, request a demo here.

FAQs

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• How does Visitor Fingerprint reduce identity collisions?
  Visitor Fingerprint creates a persistent user identifier by aggregating device, browser, network, and client signals. This dramatically reduces identity collisions and divisions, ensuring more reliable session-to-user attribution across models and systems.
• What prevents fraudsters from bypassing Visitor Fingerprint?
  The system combines low-level device intelligence with behavioral and network data. It resists common evasion techniques like “antidetect browsers”, cookie clearing, incognito browsing, VPN use, and mobile emulator environments by anchoring identification in signals that are difficult to replicate.
• How does Visitor Fingerprint contribute to fraud ring detection?
  By linking related sessions and devices through shared signals, Visitor Fingerprint uncovers coordinated activity patterns. It helps teams identify clusters of accounts tied to a common user or device network, which is essential for detecting fraud rings and money mule operations.
• Can Visitor Fingerprint improve machine learning model inputs?
  Yes. Visitor Fingerprint stabilizes the identity layer feeding into fraud models. This reduces noise from fragmented user journeys, improves signal quality, and increases model accuracy for real-time decisioning.
• How does uniqueness scoring affect risk workflows?
  The system assigns a visitor fingerprint a uniqueness score between low and high confidence. A high uniqueness score suggests strong device attribution and supports frictionless progression, while a low score can trigger additional verification or step-up actions.
• Does Visitor Fingerprint work for thin-file or first-time users?
  It captures environmental and behavioral context from the first session. Even when traditional identity data is sparse, it delivers early risk signals that help differentiate legitimate users from synthetic or high-risk profiles.
• How is this different from legacy fingerprinting methods?
  Traditional fingerprinting relies on static attributes and is vulnerable to reset or spoofing. Visitor Fingerprint is dynamic, adaptive, and resilient. It persists across sessions and environments, providing a more accurate and stable identity reference for fraud prevention.