Collects over 100 device and browser signals — hardware, canvas, fonts, WebGL, audio context — to identify returning fraudsters even after cookie clearing.

Captures mouse movement, scroll patterns, keystroke dynamics, and interaction timing to differentiate genuine users from bots and scripted attacks.

Integrates deliverability, type classification (free / disposable / private), and risk signals from the email address to strengthen the overall fraud score.

Enriches every event with IP reputation, connection type (VPN, Tor, proxy, cloud), geolocation, and blocklist status to flag suspicious origins instantly.

Validates phone number format, carrier, and line type (VoIP, mobile, landline) and fuses these signals into the unified risk assessment.

Every risk assessment includes structured reason codes — no black-box output. Understand exactly which signals triggered the score for compliance and debugging.

Drop in a single lightweight JS snippet to instantly enable device fingerprinting and behavioral analytics with zero backend changes required.

Low-latency synchronous REST API for inline risk assessments at any critical user action — results in milliseconds.

Push fraud events and score updates to your backend in real time via configurable webhooks, enabling automated downstream workflows and alerting.

Submit large datasets for bulk fraud scoring — ideal for retroactive reviews, historical audits, and high-volume offline processing pipelines.

Dedicated support queue with faster response SLAs and access to fraud specialists for technical integration guidance and escalations.

Formal uptime and response-time SLA with financial-backed commitments, ensuring the reliability your production systems depend on.

Fully isolated compute and data pipeline for maximum throughput, compliance isolation, and custom configuration — no resource sharing with other tenants.