Fraud & risk analytics · SEON Technologies Ltd.

SEON

Fraud prevention and AML platform built around digital footprint analysis and device fingerprinting rather than ML alone.

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SEON combines fraud prevention, identity verification and AML compliance in one workflow-orchestration platform. Its core signal is digital footprint analysis: it enriches an email address, phone number or IP with over a thousand real-time data points scraped from social networks, e-commerce sites and other public services to gauge whether an identity is real and established, layered with device fingerprinting and behavioral biometrics. Rules and ML-driven scoring sit on top, with a drag-and-drop workflow builder, score explainability, case management and network analysis to surface fraud rings sharing identifiers. It is a cloud SaaS product with a REST API and no-code rule building. Unlike Sardine or Sift, SEON's differentiator is treating publicly-derivable digital footprint signals, not consortium data or biometrics, as the primary fraud tell, and it publishes self-serve list pricing rather than pure enterprise quoting.

At a glance

Vendor SEON Technologies Ltd.
Pricing model Quote only
Free tier Yes
Deployment Cloud
Open source No
Best for Growing merchants and fintechs that want fast self-serve setup and digital-footprint-driven fraud checks before paying for enterprise scale.

Pricing

Published Starter plan at a fixed monthly rate with capped API calls; Premium tier is unlimited and custom-quoted. Free trial available.

Plan Price Notes
Free Trial $0 Time-limited trial of core capabilities
Starter $699/month 2,500 fraud-check API calls/month, 10 users, 50 custom rules, standard reporting
Premium Custom pricing (contact sales) Unlimited API calls and users, Case Management, AML Compliance, dedicated implementation and 24/7 support

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Digital footprint analysis from email, phone and IP (1,100+ signals)
  • Device fingerprinting and behavioral biometrics
  • AI agent/bot detection
  • Drag-and-drop, no-code workflow and rule builder
  • Score explainability showing which signals drove a decision
  • Network analysis mapping fraud rings across shared identifiers
  • AML screening and regulatory report filing (SAR/STR/8300)
  • AI-assisted case management and alert triage

Profile last reviewed September 21, 2026

Alternatives

Sift ML fraud-scoring platform that leans on a cross-customer consortium network plus device and behavioral signals. Sardine Fraud and AML platform leaning on behavioral biometrics, device intelligence and payment/on-chain risk signals. Ravelin ML fraud-scoring platform for e-commerce that leans on per-merchant custom models plus graph/link analysis of fraud rings. Fingerprint Device fingerprinting and identification API that recognizes returning browsers/devices even without cookies or logins.

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Terms to know

Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Browser fingerprinting Identifying a device from browser and device characteristics, such as screen size or fonts, without setting a cookie.

Related guides

How to choose a fraud detection tool Fraud platforms split by primary signal — biometrics, device data, consortium networks, compliance case management — and by who they're built for.