Security Experts:

Fraud Prevention Firm Sift Science Raises $53 Million

Fraud prevention and risk management solutions provider Sift Science today announced that it has closed a $53 million Series D funding round, bringing the total raised to date by the company to $107 million.

The latest funding round was led by New York-based growth equity firm Stripes Group, with participation from SPINS, Remitly, Flatiron Health, Udemy, GrubHub, and previous investors Union Square Ventures, Insight Venture Partners, and Spark Capital.

Sift Science plans on using the newly acquired funds to expand its global footprint in the fraud detection and prevention market, which is estimated to reach roughly $42 billion by 2022.

Sift’s Digital Trust Platform relies on machine learning to protect businesses against fraud and abuse, including payment fraud, fake accounts, account hijacking, and abusive user-generated content.

The platform uses data from thousands of websites and apps to identify fraud patterns based on connections between users, behaviors, locations, devices and more. Sift says its customers include Airbnb, Twitter, Twilio, Shutterstock, Yelp, Wayfair and Jet.

“We believe Sift is uniquely positioned to leverage its best-in-class software platform and data network to fundamentally reshape the way businesses and consumers interact online – with more confidence, transparency and security. We are thrilled to be partnering with Sift as it accelerates its already exceptional growth trajectory,” said Ron Shah, partner at Stripes Group.

Related: Virsec Raises $24 Million in Series B Funding

Related: ThreatQuotient Raises $30 Million in Series C Funding

Related: RELX Group to Acquire Fraud Fighting Firm ThreatMetrix for $815 Million

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Eduard Kovacs (@EduardKovacs) is a contributing editor at SecurityWeek. He worked as a high school IT teacher for two years before starting a career in journalism as Softpedia’s security news reporter. Eduard holds a bachelor’s degree in industrial informatics and a master’s degree in computer techniques applied in electrical engineering.