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
Use proactive suspicious pattern detection that predicts fraud attempts before they are actioned
Analyze your entire client network for suspicious patterns throughout 100% of the user journey with one solution
Examine historical connections and relationships among entities with AI and ML-powered algorithms
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.
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!
Fraudsters love using repeated methods for multi-accounting. Outsmart them with IP address analysis, behavioral biometrics, and device fingerprinting to ensure user authenticity.
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.
Advanced algorithms scrutinize all user interactions, completion speeds, and geographical patterns so that you can distinguish genuine user engagement from artificially motivated traffic.
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.
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.
Works on all devices
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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.
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.
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.