Fraud & risk analytics · SardineAI Corp.

Sardine

Fraud and AML platform leaning on behavioral biometrics, device intelligence and payment/on-chain risk signals.

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Sardine is a risk platform built for fintechs, banks and payment companies that unifies fraud prevention, KYC/KYB onboarding and AML transaction monitoring. Its distinguishing signal is behavioral biometrics: it analyzes typing, mouse and touch patterns during onboarding and transactions to flag social engineering, scripted bots and coerced ('scam') behavior, combined with device fingerprinting and a proprietary de-anonymization technique for masked IPs/locations. It also screens payment and on-chain (crypto wallet) activity for money-laundering typologies. Delivered as a cloud API/dashboard, it adds a rules engine, a connections/network graph for link analysis, case management, and consortium data shared across customer institutions. Compared with Sift's consortium-first approach or SEON's digital-footprint focus, Sardine centers on how a user physically interacts with a device as its primary tell.

At a glance

Vendor SardineAI Corp.
Pricing model Quote only
Free tier —
Deployment Cloud
Open source No
Best for Fintechs and banks that need onboarding, payment fraud and AML monitoring in one platform, weighted toward behavioral-biometric detection.

Pricing

Enterprise pricing quoted per customer based on volume and modules used; no public rate card.

Pricing has not been verified yet — see the vendor's site.

Features

  • Behavioral biometrics (typing, mouse, touch patterns)
  • Device fingerprinting and emulator detection
  • IP/location de-anonymization ('True Piercing')
  • KYC/KYB identity verification and document checks
  • Payment fraud detection across ACH, wires, cards and RTP
  • On-chain/crypto transaction risk scoring
  • Connections graph for network/link analysis
  • Sanctions and PEP screening with case management

Profile last reviewed September 21, 2026

Head to head

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. Unit21 No-code rules and case-management platform that lets fraud/AML ops teams build detection logic and investigate without engineers. Alloy Identity risk orchestration platform that routes onboarding decisions across 270+ KYC/KYB and fraud data providers.

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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. Fraud detection Identifying transactions, claims, or behavior that are likely fraudulent, using rules, statistics, or machine learning. Risk scoring Assigning a numeric score to a customer, transaction, or entity that estimates the likelihood of a specific negative outcome.

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.