Application Fraud

As data breaches proliferate, fraudsters are increasingly using stolen or synthetic identities to open fraudulent accounts. DataVisor’s Unsupervised Machine Learning Engine analyzes the hidden connections between fraudulent applications to detect them 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.

Request Trial

Stop Sophisticated Attack Techniques

IP Obfuscation

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

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

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.

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.

Accuracy and Coverage

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

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.

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

WHITE PAPER

Learn about the behaviors and attack techniques of the world’s largest online crime rings.

Download report

CASE STUDY

Learn how a major US Bank reduced fraudulent application with DataVisor’s UML Engine.

Download Case Study

DATA SHEET

Learn how UML can protect against application fraud and other forms of financial fraud.

Download Data Sheet

The DataVisor Detection Solution

Unsupervised Machine Learning Engine

Predict new, unknown threats without labels or training data by analyzing hundreds of millions of accounts and events simultaneously using the industry’s most advanced unsupervised learning technology.

Supervised Machine Learning Engine

Use industry leading supervised machine learning algorithms to augment the unsupervised machine learning detection with client-provided labels.

Automated Rules Engine

Generate and deprecate rules automatically, lowering maintenance costs and improving results explainability.

DataVisor Global Intelligence Network

Aggregate and analyze the industry’s broadest array of digital fingerprints and signals from billions of users across a variety of industries.

Account linkage view to discover hidden links among malicious accounts

Learn More

What’s Happening in Application Fraud

BLOG POST

Fake Accounts and Real Big Trouble

It’s a lot easier to create and hide fake accounts than you think. Whether it’s insiders creating new accounts to meet sales goals or outside fraudsters committing identity theft, fake account opening is a huge problem being faced by any company with users and it’s becoming increasingly more difficult to detect.

Read Blog

BLOG POST

Mobile Fraud Gone in a (Device) Flash

Device fingerprinting, i.e., collecting information from a device for the purposes of identification, is one of the main techniques used by online services for mobile fraud detection. The goal is to recognize “bad” devices used by fraudsters, such that they can be identified even when other attributes (such as user names or IP addresses) change.

Read Blog

BLOG POST

The Underground Market for User Accounts | Fake Accounts, Account Fraud

User accounts are extremely valuable – real accounts far more so than fake accounts. This is not only true for Internet properties, which are valued by the size and growth of their user base, but also for professional online criminals exploiting these platforms for a profit.

Read Blog

Read DataVisor’s Latest Posts

Go To The Blog

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!

Request Trial