Application Fraud

Unsupervised Machine Learning Engine analyzes the hidden connections between fraudulent applications to detect suspicious applications even if each application in isolation is not suspicious. This allows DataVisor to stop application fraud in real time, without training data or labels, stopping the fraudster at account approval.

Stop Sophisticated Attack Techniques

IP obfuscation for application fraud

IP Obfuscation

Fraudsters utilize proxies, VPNs, or cloud-hosting services to hide their tracks from IP blacklists and rules-based systems.

armies of free accounts for mass registration

Armies of Free Emails

Fraudsters use popular free email services to mass register realistic-looking accounts to use for their own attacks or to sell to other fraudsters.

Scripted logins for application fraud

Scripted Logins

Attackers use sophisticated scripts to carry out large scale attacks, appearing as though the sessions are from many distinct users.

Device obfuscation for application fraud

Device Obfuscation

Fraudsters utilize mobile device flashing, virtual machines and scripts to appear as though the login events are coming from different devices.

Why UML is Needed to Stop Application Fraud

The wide availability of personally identifiable information allows fraudsters to apply for accounts using stolen or synthetic identities. Synthetic identity theft, where fraudsters create an entirely new fake identity, is almost a perfect crime as there is no consumer victim to complain about the fraud. Coupled with sophisticated mass registration techniques, these synthetic accounts appear legitimate and remain under the radar when reviewed in isolation. DataVisor’s Unsupervised Machine Learning Engine analyzes all accounts simultaneously, allowing it to detect the hidden connections between fraudulent accounts, even if each account is not suspicious in isolation.

high accuracy and coverage to detect account takeover

Accuracy and Coverage

By detecting entire crime rings at once, UML is able to achieve unrivaled detection accuracy and coverage at the same time.

Stop evolving account takeover threats

Detect Unknown Threats

UML uncovers the hidden connections between accounts without training data or labels, allowing it to detect changing and entirely new attack patterns.

customer experience blue v0

Improve Customer Experience

UML’s accuracy allows companies to identify good customers and reduce authentication steps, streamlining customer experience.

Learn How DataVisor Fights Application Fraud

What’s Happening with Application Fraud

Ready to enhance your detection with unsupervised machine learning?