AI startup Deep Labs lands $16M in funding commitments

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Deep Labs, a developer of what it calls “persona-based intelligence,” has $16 million in commitments from a group of financial technology investors that includes Serendipity Capital, Gramercy Ventures and Gunnar Overstrom of Corsair Capital.

The San Francisco company’s technology analyzes transactions in context, taking into account factors such as someone being home with his or her family versus in the office or traveling for work, said founder Scott Edington.

That analysis is applied to transaction authentication and authorization, digital marketing, fraud detection, money laundering prevention, account onboarding and identity verification.

Early customers include global payments networks Visa and American Express, Clear and the federal government. Mastercard, NICE Actimize, Booz Allen Hamilton and General Dynamics Information Technology are among Deep Labs' partners.

Deep Labs uses data gleaned from behavioral biometric systems such as BioCatch and Secured Touch, facial recognition systems, iris recognition systems, device identification and identity and transaction data. Its software is meant to help companies make decisions, using those different data points as signals. It can be used to detect fraud, authenticate new users and authorize transactions.

“It's quite easy to defeat fraud if all you want to do is simply decline every single transaction ... but that introduces friction to the consumers and leads to things like card abandonment and grinds commerce to a halt," Edington said.

"There's a very difficult balancing act that practitioners have to do in terms of limiting fraud but also making sure you're limiting what we call false declines, which are transactions that should have been approved but were declined," he added.

Clients use Deep Labs’ software to bring on new customers, detect fraud and make marketing decisions.

“If I know what persona I'm expecting from a customer, I can figure out how I might want to, from a loyalty program perspective, serve him something that would be appealing to him,” Edington said.

There’s a greater need for this type of technology during the current quarantine, Edington said, because people aren't shopping in person. Point-of-sale transactions have dropped and card-not-present transactions have increased.

“Because of that and because the fraudsters never stop, they use opportunities like this to really accelerate some of their bad actions," Edington said. "We certainly have gotten a big bump in interest in our solutions.”

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Artificial intelligence Transaction laundering Fraud detection AML Authentication
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