Fraud & risk analytics · Ravelin Technology Ltd.

Ravelin

ML fraud-scoring platform for e-commerce that leans on per-merchant custom models plus graph/link analysis of fraud rings.

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Ravelin is a fraud and abuse-prevention platform built specifically for high-volume e-commerce, marketplace and on-demand merchants. Its core method is custom machine learning: models are trained and tuned to each merchant's own transaction data and KPIs rather than a single generic model, and it layers graph/network analysis that maps hidden connections between customers, cards and devices to expose fraud rings and coordinated attacks in real time. It also checks activity against a consortium of anonymized identity elements shared across its merchant base. Coverage spans payment fraud, refund and promo abuse, account takeover and marketplace/supplier fraud, plus emerging detection for AI shopping-agent and bot traffic. Delivered as a cloud decision engine integrated at checkout, login and returns, with 3D Secure and PSP-agnostic routing. Compared with Sift or SEON, Ravelin's differentiator is merchant-specific model training combined with link analysis, aimed squarely at e-commerce rather than banking or crypto.

At a glance

Vendor Ravelin Technology Ltd.
Pricing model Quote only
Free tier —
Deployment Cloud
Open source No
Best for High-volume e-commerce and marketplace merchants that want fraud models trained on their own data plus fraud-ring detection.

Pricing

Enterprise pricing negotiated per merchant based on transaction volume; no published rate card.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features

  • Custom machine learning models trained per merchant
  • Graph/link analysis to expose fraud rings and coordinated attacks
  • Consortium data across anonymized identity elements
  • Payment fraud detection and chargeback reduction
  • Refund and promo/voucher abuse detection
  • Account takeover prevention
  • Marketplace and supplier fraud detection
  • 3D Secure Server and PSP-agnostic transaction routing

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. SEON Fraud prevention and AML platform built around digital footprint analysis and device fingerprinting rather than ML alone. Sardine Fraud and AML platform leaning on behavioral biometrics, device intelligence and payment/on-chain risk signals. 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. Bot traffic Website activity generated by automated programs rather than human visitors, ranging from crawlers to fraudulent scripts. Fraud detection Identifying transactions, claims, or behavior that are likely fraudulent, using rules, statistics, or machine learning.

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