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
- Non-doc verification
- Public Digital Identity Systems
- QES/eIDAS (coming soon)
- 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.
Web SDK
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.