BioCatch, a provider of behavioural biometrics for fraud prevention and detection, has closed a $30m financing round.
The round was led by Maverick Ventures, with additional participation from American Express Ventures, NexStar Partners, Kreos Capital, CreditEase, OurCrowd, JANVEST Capital and other existing investors. The round comes nearly fours years after the company closed a $10m round, led by Blumberg Capital.
With identity fraud and cybercrime continuing to grow rapidly, the concept of digital identity is broken according to BioCatch. Founded by experts in big data, machine learning and artificial intelligence, the company set out to address the next generation of cyber threats by focusing on the behavior of the fraudster as opposed to adding new endpoint security layers.
BioCatch collects and analyses more than 2,000 parameters to generate user profiles and model different types of genuine and malicious behavior. Its platform claims to address a wide range of threats at login and beyond by identifying malware, robotic activity, social engineering (phishing, etc.) and other cyber threats.
By monitoring more than 5 billion transactions per month, it generates real-time alerts when behavioral anomalies are detected, stopping fraud at the source and reducing the significant operational costs associated with managing the fraud.
“BioCatch helps to answer the question, ‘who are you’ in an online world where fraudsters operate with the legitimate credentials of others, making it very hard to distinguish them from authorized users,” said Howard Edelstein, BioCatch CEO. “We take pride in the track record we have amassed and the role that we play as an integral part of our clients’ identity strategy. This strategy cuts across the digital ecosystem, from stopping fraud in real-time to preventing fake accounts from being opened in the first place, all while enabling a seamless user experience.”
BioCatch behavioral biometrics offers a new dimension to fighting fraud through online identity verification. The system distinguishes between a real user and an impostor by recognizing normal user behavior versus fraudster behaviors, even when no profile exists.
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