Case Studies

Financial Institution Implements Unsupervised Machine Learning to Stop Application Fraud

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DataVisor recently partnered with one of the largest banks in the U.S. to help them reduce fraudulent applications created using synthetic or 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.

How DataVisor Helped:

The identified linkage between malicious accounts to catch all members of the fraud ring.
Caught fraudulent accounts at account opening time to reduce the window of exposure for the bank.
Detected new types of attacks without labels or training data.

Trusted by Leading Brands

"DataVisor’s machine learning solution is the most critical component of our fraud defense as we grow in digital space, helping us minimize customer friction while defeating fraud risk."

Richard Cooney

Head of Fraud Strategy

About DataVisor

Bots attacks cost companies roughly $80 billion a year. How do they work, and how can you stop them? In our latest ebook, DataVisor CTO and co-founder, Fang Yu, explains why coordinated bot attacks are so difficult to detect and prevent, and shares proactive strategies for protecting your organization.