Identity Theft

Stop Identity Thieves at Account Opening

Identity theft, whether it’s via synthetic identity creation or true name fraud, has been growing at an exponential rate. Fraudsters are using partial or real identities to appear legitimate when opening new accounts to avoid detection by traditional fraud detection solutions. Using an unsupervised machine learning approach, DataVisor has the most advanced technologies to distinguish real or fake identities at the account registration time to prevent any downstream damage. Reduce chargebacks, uncover money laundering, stop check forgery, and more when you uncover identity theft at account opening time. DataVisor can catch the entire crime ring of accounts involved in a large scale, coordinated attack.

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Common Attack Techniques

IP obfuscation

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

Popular email registration

Fraudsters use popular email services to register accounts to appear legitimate

Scripted logins

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

Device obfuscation

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

DataVisor Online Fraud Report

Learn about the techniques fraudsters use to evade detection

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Why Unsupervised Machine Learning for Stopping Identity Thieves?

The wide availability of personal identifiable information makes identity theft and synthetic identity theft much more prevalent today. Synthetic identity theft creates an almost perfect crime as there are no specific consumer victims to complain about the fraud. Coupled with sophisticated mass registration techniques, these fake accounts appear legitimate and remain under the radar when reviewed one at a time in isolation. DataVisor’s unsupervised machine learning approach looks at all the accounts globally and finds highly correlated, suspicious clusters of accounts, detecting accounts as fraudulent even if their identities look legitimate.

Unparalleled accuracy and coverage

Catch all members of the crime ring without negatively impacting good users

Unknown threat protection

Uncover evolving attack campaigns without any training data or labels

Early detection

Detect fraudulent accounts early to reduce the window of exposure and prevent downstream damage

Key Features

Account linkage visualization

Show linkage and shared attributes among malicious accounts to visualize the entire crime ring and reduce manual review time by 800%

Account investigative console

Investigate bad registrations, create detailed forensic reports and see threat trends with campaign visualizations

Real-time detection

Integrate using our real-time Results API to alert you on fake accounts before they can conduct any damage

Account linkage view to discover hidden links between malicious accounts

Bringing Real Value to Our Customers

CASE STUDY

DataVisor Uses AI to Stop Identity Thieves in F500 Bank

This F500 Bank experienced strong growth in new bank account openings. However, the rapid growth was accompanied by fake accounts created using stolen identities. Read this case study to learn how DataVisor detected an additional 30% of fraudulent accounts on top of the bank’s existing in-house detection solution.

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Getting Started

Want to get started and find out how DataVisor can help find malicious accounts hiding inside your online service? Request a security assessment today!

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