FRAUD PREVENTION

Device and Behavior Intelligence

The full story
behind every user

Device and behavior signals in a single, lightweight SDK. Analyze every customer touchpoint to identify advanced fraud patterns, without adding friction for legitimate users.

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Gain a complete view of the customer

Device and behavior signals combined in one SDK to distinguish real users from fraudsters. Insights from 5.4B+ profiled devices strengthen detection and reduce false positives.

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Integrate signals and rules

Translate raw device and behavior signals into 1,000+ risk features, including aggregations, that power the same rules engine across every customer session.

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Improve detection accuracy

Use ML trained on device and behavioral risk features to establish customer baselines and identify anomalous activity.

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Unmask fraudsters hiding their activity

Detect remote access tools, bots, and browser automation, uncover activity hidden behind proxies and VPNs, identify location/IP/OS mismatches, and detect AI deepfakes.

Capture real user behavior across devices and sessions to flag risk early

Device Intelligence

Detect bots, deepfakes, and obfuscation tools with real-time device, network, and behavioral intelligence.

Device intelligence
Behavioral biometrics

Device and behavior intelligence adapts to every risk, wherever users interact.

Multi-accounting
Bot-driven account creation
Fake or synthetic accounts

Onboarding Fraud

Validate the context behind every onboarding attempt using real-time device and behavioral signals.

Account Takeovers

Identify when users log in from an unfamiliar device, use obfuscation tools, or exhibit behavioral patterns inconsistent with the legitimate account owner.

Account takeovers
Transactions from high-risk users
Card testing and enumeration
Social engineering scams

Payment fraud

Block fraudulent transactions by flagging devices previously linked to fraud and behavior that deviates from the customer’s normal transaction patterns.

Uncover fraud rings and repeat abusers behind promotion farming, refund abuse, and other coordinated schemes by linking accounts through shared devices and consistent activity across identities.

Policy Abuse
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Cross-account linking

Surface linked accounts through shared identifiers across users and devices.

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In-session abuse signals

Detect risky user behavior during browsing, claims, or refunds.

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Consortium enrichment

Use cross-merchant intelligence to detect repeat abusers and emerging abuse patterns.

Learn why teams move their risk stack to Sardine

prezzee
experian

“One of the key benefits of Sardine is the sheer volume of network data in their consortium. We can tap into a much broader pool of fraud insights, which helps us improve detection and stay ahead of new threats.”