Graph neural network (GNN) fraud detection

Expose criminal networks
with AI-powered fraud detection

Discover interconnected patterns of suspicious activities on your platform with Sumsub’s AI-powered Fraud Network Detection solution so you can outsmart advanced criminal threats

Predict fraudulent attacks

Use proactive suspicious pattern detection that predicts fraud attempts before they are actioned

Streamline investigations

Analyze your entire client network for suspicious patterns throughout 100% of the user journey with one solution

Detect network patterns

Examine historical connections and relationships among entities with AI and ML-powered algorithms

Turn complex criminal patterns into
simple anti-fraud solutions

Sumsub’s Fraud Network Detection revolutionizes anti-fraud countermeasures by uncovering hidden connections, detecting anomalies, and analyzing user behavior continuously at every user journey stage.

Onboarding Orchestration

Sign Up

  • Email Risk Assessment
  • Phone Risk Assessment
  • Device Intelligence

User
Verification

  • ID Verification
  • Address Verification
  • Liveness and Face match
  • Live Agent Video Call
  • Doc-free Verification
  • Public Digital Identity Systems
  • QES/eIDAS (coming soon)
  • NFC
  • Known Face Search
  • Blocklist Check
  • Duplicate Check

AML
Screening

  • PEP + Sanctions
  • Adverse Media

Ongoing Monitoring

Login

  • Device Intelligence
  • Behavioral Fraud Detection
  • Face Authentication
  • MFA

Fraud
Monitoring

  • Device Intelligence
  • Behavioral Fraud Detection
  • Face Authentication
  • Ongoing AML Monitoring

Transactions

  • AML Transaction Monitoring
  • Bank Account Verification
  • Fraud Transaction Monitoring
  • Behavioral Fraud Detection

Malicious behavior prediction

Graph neural network (GNN) analysis detects behaviors, patterns, and historical data with machine-learning algorithms. GNNs will even help you predict criminal activities before they happen!

Multi-accounting prevention

Fraudsters love using repeated methods for multi-accounting. Outsmart them with IP address analysis, behavioral biometrics, and device fingerprinting to ensure user authenticity.

Bot farm detection

Bot farms coordinate malicious attacks like account takeovers, phishing campaigns, and crypto fraud. Easily detect these threats by analyzing device fingerprints, completion speeds, and other non-human nuances.

Incentivized traffic monitoring

Advanced algorithms scrutinize all user interactions, completion speeds, and geographical patterns so that you can distinguish genuine user engagement from artificially motivated traffic.

Evolving beyond crime

The Fraud Network Detection updates its algorithms by leveraging advanced machine learning and artificial intelligence so you can stay ahead of emerging criminal tactics before they harm your business.

Fast and hassle-free integration

You only require a one-time integration with Sumsub’s SDK, and most clients begin checks within one week. All further settings are code-free and available in the dashboard.

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Web SDK

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Works on all devices

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FAQ

  • What is the most common fraud detection method?

    Dynamic AI-powered anomaly detection is the most common fraud detection method. It analyzes patterns, behaviors, and transactions, swiftly identifying anomalies to protect businesses from evolving financial threats.

  • What is a neural network algorithm (NNA) for fraud detection?

    A neural network algorithm for fraud detection utilizes artificial intelligence, mimicking the brain's neural structure. It processes data to learn and detect patterns, enhancing accuracy in identifying and preventing fraudulent activities.

  • What are graph neural networks (GNN) in fraud detection?

    In fraud detection, graph neural networks (GNNs) analyze complex relationships within data networks, identifying patterns and anomalies. They excel at uncovering hidden connections and enhancing accuracy in detecting sophisticated fraud networks. Fraud detection with graph neural network usage is at the cutting edge of anti-fraud countermeasures.