Transaction Fraud

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

IP obfuscation account takeover

IP Obfuscation

Proxies, VPNs, and cloud-hosting services allow attackers to evade IP or location blacklists and digital-fingerprint solutions.

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Disposable Emails

Attackers use mass registration techniques to create armies of emails to test stolen credit card information before attempting to purchase their primary targets.

Device obfuscation for account takeover

Device Obfuscation

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

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.

Early detection of account takeover

Real-Time Detection

Stop fraudulent transactions before they are approved by uncovering the suspicious connections between transactions and accounts.

Stop evolving account takeover threats

Unknown Threat Protection

Detect new and evolving attacks without waiting for training data or labels by analyzing the connections among all accounts and transactions.

high accuracy and coverage to detect account takeover

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

Case Studies

In-App Purchase Fraud

DataVisor recently partnered with one of the largest gaming companies in the world to protect them from fraudulent in-app purchases and promotion abuse.

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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.

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.

What’s Happening with Transaction Fraud

Threat Blogs

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

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Wave of Money
Threat Blogs

The Changing Tide of Financial Fraud

On October 1, the financial payments world was abuzz with talk about how the increased adoption of the new EMV standard for credit card purchases was going to bring about dramatic changes to financial fraud.

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