Security companies are using technology to stop more complex fraud based on a smaller amount of data, and investors are taking notice.

The San Francisco-based Commerce Ventures just led a $13.9 million round in Socure, a New York-based digital identity technology company. Other investors include Flint Capital, Workbench, Santander InnoVentures and Two Sigma Ventures.

Socure, which has raised close to $28 million since December 2016, applies machine learning to more than 300 data sources that link to a user's identity with the ultimate goal of halting account-related fraud.

"Fraud is moving online as commerce is moving online," said Dan Rosen, a general partner at Commerce Ventures Fund, adding that in this environment "true name" fraud is one of the most devastating crimes, given the scammer's ability to use stolen IDs to open new accounts, then rack up huge bills they never pay off.

Dan Rosen, a general partner at Commerce Ventures Fund
Dan Rosen, a general partner at Commerce Ventures Fund.

"Socure is interested in stopping fraud before the account is created," said Rosen, whose firm has also made investments in InAuth, a device fingerprinting company that American Express acquired late last year; and Omnyway, a company that helps brick and mortar stores migrate to e-commerce. Omnyway counts Kohl's among its clients, providing the technology that powers the retailer's mobile payments app.

By using more and alternate sources of data, Socure aims to handle authentication for younger consumers and other demographics that don't have robust credit histories.

"Traditional financial institutions have a hard time serving younger consumers," Rosen said. "That funnel is increasingly digital, and consumers don't have a traditional credit profile in some cases, or traditional data sources."

By using neural networks and emerging modelings techniques, machine learning can move beyond the "if/then" construct of most rules-based systems that check user's identities in a static manner, according to Sunil Madhu, CEO and founder of Socure, whose clients include a top 5 U.S. bank, a top 10 U.S. card issuer and a top 5 global money transfer provider (the company did not name them).

"Machine learning can go beyond human intuition, while the rules-based systems are more passive," Madhu said.

When a consumer fills out a form for an account, a loan or another financial product, that triggers the ID check, with data passing through Socure to the corporate user via an application programming interface.

Machine learning is just one of a variety of emerging technology options to combat digital crime. Machine learning is relatively new, though it does have advantages, namely that it's flexible and can be used for more than just security.

"Like software, machine learning can be applied to an extremely wide range of specific problems that cut across all business domains," said Tim Sloane, vice president of payments innovation at Mercator, adding the method by which machine learning tools are taught often delivers a first mover advantage. "Machine learning is used today to greatly improve fraud detection while simultaneously reducing false positives," Sloane said. "It has improved the ability to predict consumer behavior and is fundamental to behavioral biometrics, which is disrupting the traditional authentication market."

Machine learning has also changed how consumers interact with their smartphones by enabling natural language interfaces, contextual commerce and automated agents that interact with voice or speech. "Consumers increasingly expect their phone will answer their questions, give them directions and warn them when accidents will slow them down," Sloane said.

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