ID R&D, a builder of AI-based voice and behavioural biometrics technology, has closed its Series A round on $5.7m.
China-based venture capital GSR Ventures led the round, marking its first investment into ID R&D. Participation also came from previous backer Gagarin Capital.
This investment comes after a strong year of growth, culminating in the doubling of its sales contracts and the opening a headquarters on the US West Coast. Over the course of Q1 2019, ID R&D managed to increase its customer base by 25 per cent.
Funds from the round are earmarked for growing the engineering teams and bolstering the marketing and international sales efforts.
Launched in 2016, ID R&D offers multi-modal biometrics security software. Its technology suite includes tools for voice and face biometrics as well as behavioural biometrics. Behavioural biometrics works by AI technology monitoring how a user interacts with a web and their devices, such as keystroke dynamics.
In addition to this, the New York-based company offers anti-spoofing capabilities which can identify synthesised or recorded speech from a live user’s voice and detect fraudulent login attempts using a picture, video or model of a user’s face.
Its services are available for mobile apps, web apps, chatbots, conversational interfaces, and messaging platforms.
GSR Ventures partner Sunny Kumar said, “As we talk to CTOs and CISOs, it’s clear that rapid expansion in the prevalence and functionality of voice-enabled applications and devices has increased the need to protect these access points and the services they provide.
“ID R&D has developed the best voice biometric authentication solution, providing a critical layer of security to these devices. We are thrilled to be partnering with ID R&D to make voice biometrics the new standard in authentication.”
Last year, GSR Ventures participated in the $50m Series B+ round of China-based anti-fraud solution Advance.AI. The company builds a profile for consumers based on their personal background, income and other details. This is cross-referenced with interactions a company has with the consumer and machine learning technology can identify any outliers or fraud attempts.
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