As digital banking, fintech, and social commerce businesses flourish, so do fraudsters and their sophisticated attacks. Gone are the days where a single attacker uses a single stolen credit card to make a quick score. Financial fraud has become a professional enterprise, with a complete ecosystem of stolen credit cards, personally-identifiable information, knowledge-based authentication scripts, and more. DataVisor’s Unsupervised Machine Learning Engine can detect suspicious connections between accounts and events, allowing it to stop transaction fraud in real time.
How Attackers Hide Fraudulent Transactions
Proxies, VPNs, and cloud-hosting services allow attackers to evade IP or location blacklists and digital-fingerprint solutions.
Attackers use mass registration techniques to create armies of emails to test stolen credit card information before attempting to purchase their primary targets.
Fraudsters age fake accounts for long periods of time while simulating realistic user activity to evade detection when they launch their primary attack.
Why UML for Detecting Fraudulent Transactions
Modern fraudsters have learned to evade advanced fraud detection solutions that rely on supervised machine learning or rules engines by carefully probing their targets before attacking and continuously changing their techniques. Powered by the latest big data technologies, DataVisor’s Unsupervised Machine Learning Engine analyzes all accounts and events simultaneously to uncover highly correlated, suspicious clusters of fraudulent activities. When viewed in the full context of all accounts and events, transactions that are not suspicious in isolation stick out like a sore thumb. Even better, DataVisor’s unsupervised approach doesn’t require training data or labels to detect new attack techniques, drastically reducing the response time to these attacks.
Stop fraudulent transactions before they are approved by uncovering the suspicious connections between transactions and accounts.
Unknown Threat Protection
Detect new and evolving attacks without waiting for training data or labels by analyzing the connections among all accounts and transactions.
Accuracy and Coverage
Increase the number of fraudulent transactions detected while at the same time reducing the number of false positives for good customers.
Learn How DataVisor Fights Transaction Fraud
The DataVisor Detection Solution
Unsupervised Machine Learning Engine
Supervised Machine Learning Engine
Automated Rules Engine
Global Intelligence Network
What’s Happening with Transaction Fraud
The recent announcement from Activision Blizzard to acquire King Digital Entertainment, maker of the hit game Candy Crush, for a staggering $5.9 billion certainly turned some heads. Is there really that much money in the