DataVisor raises $40 million Series C for machine-learning fraud detection

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DataVisor, a company that uses machine learning to detect fraud, announced a $40 million Series C funding round Monday led by Sequoia Capital China.

The company, founded by two former Microsoft Research employees, uses unsupervised machine learning to discover malicious behavior. Unsupervised learning allows machines to track patterns across data sets in order to make decisions on their own, as opposed to supervised learning, which trains computers through data feeds provided by engineers.

DataVisor detects various types of fraud and abuse, including fraudulent transactions, fake content, spam and abuse, identity theft, application fraud and money laundering. The company says its technology protects 2 billion users globally, with a client list that includces Pinterest, Yelp, Alibaba Group, Dianping, Toutiao, Cheetah Mobile and Tokopedia.

“Enterprises today are facing constantly evolving threats from sophisticated and tech-savvy fraudsters who continuously experiment and find ways to evade detection,” said Yinglian Xie, CEO and co-founder of DataVisor. “This new round of financing will enable us to further improve our technology, expand our services globally and transform the way that businesses fight fraud.”

Fraud detection and prevention continues to be a flourishing market, expected to be worth $41.59 billion by 2022, according to research firm MarketsandMarkets. Government and public utilities are expected to be the highest growth sectors for the market, according to the research.

The round also included participation from existing investors New Enterprise Associates and GSR Ventures.

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cybercrime, DataVisor, fraud, fraud detection, identity fraud, identity theft, machine learning, Sequoia Capital, spam, unsupervised learning, venture capital
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