Online fraudsters are endlessly inventive and aggressive, sometimes going as far as to create complete, authentic profiles with comprehensive personal identifiable information (PII) and fool-proof document images. And it's working: according to the Identity Theft Center, there have been 491 U.S. data breaches in the second quarter of 2021, up 38% from just the first quarter of 2021. 

While identity document verification is a common defense against this type of identity theft and fraud, traditional services can’t compete against the more sophisticated fraudsters who evolve to leap over every new document verification barrier. The problem is, even when a government-issued ID is verified as authentic and matches a selfie, that doesn’t reveal much about the actual person taking the selfie. It’s still the digital equivalent of a supermarket clerk comparing a beer-buying-customer’s face to their driver’s license photo. The clerk can confirm the customer is who they say they are, and are old enough to buy beer, but can’t predict if the customer will handle that beer responsibly.

Machine Learning Drives Predictive DocV

In the face of these sophisticated fraudsters, siloed ID-to-selfie identity matching tools have become obsolete. Machine learning-powered predictive document verification is the next evolution of identity verification. With machine learning integrations, predictive document and identity verification is more than just a binary check of document authenticity: in mere seconds, it predicts a matched identity’s behavior and delivers a holistic decision on if they are trustworthy and should be let into the business ecosystem. 

All of this is possible due to machine learning techniques that rely on data-driven algorithms to pull from historic outcomes data. This moves data into predictive mode, so you get results not based solely on inputs, but on historical outcomes. Traditional document verification might say, “based on the inputs, this ID is valid,” while machine learning can say, “this ID is valid, and based on historic patterns similar to this situation, here is this customer’s level of risk.” 

Of course, machine learning is only as good as the data fed to the machine. Here are three key aspects of machine learning that bring it from performative into predictive.

  • Performance feedback: With integrated performance feedback, machine learning is continuously evolving to become better at predicting fraud. By “feeding back” the results for documents validated or not by the system, machines can fine-tune their algorithms so the decisions get smarter and smarter. This active, continuous feedback data collection enables real-time adjustments to predict risks and stay ahead of evolving threats.

  • Clustering: Machine learning pulls in extensive data points for more than just selfie matching. By incorporating document patterns, biometric insights, a huge identity pool, and more, “clusters” of data are formed. And from that clustering comes more holistic insights for a final predictive determination. 

  • Conversion rates: Machine learning is an automated, end-to-end process that doesn’t add friction to the user experience. Signals are passively captured and assessed in the background and then incorporated into the decision with no impact to the end-user attempting to be verified. With machine learning-based predictive document verification, a single selfie fulfills both facial recognition and liveness detection. With other non-predictive systems, machine learning is replaced by human-powered manual reviews, which are prone to biases and errors, and where decisions take days. By applying a holistic, automated approach, machine learning evaluates full dimensions of an identity in 10 seconds or less. Friction goes down while accuracy goes up, for unparalleled auto-decisioning and higher customer conversions.

Socure DocV: Machine Learning for a Holistic Authentication

Machine-learning powered document verification from Socure merges all dimensions of a digital identity, including the risk of the user’s device, address, and phone number, with the extracted document PII, then checks it against authoritative data sources, such as utility providers and credit headers. Therefore, the depth of risk signals informing the decision goes beyond simple document authentication, delivering the most predictive and accurate outcome on whether the customer is who they say they are and whether you should do business with that person. And all this identity attribute correlations and verifications occur within milliseconds, in parallel to the only seconds-long document authentication process.

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Siloed document verification processes leave fraud gaps. The document can be authenticated and the selfie may match, but there’s little-to-no understanding of the risk of the identity itself. To close these gaps, Predictive DocV enriches ID document verification with native fraud signals to deliver a holistic customer identity profile resulting in 98% auto-decision rates. 

Let us show you how it works: schedule a demo today.

Topics: Machine Learning, document verification

Brenda Gilpatrick

Brenda Gilpatrick

Brenda Gilpatrick is senior director of product marketing at Socure. She helps to lead go-to-market strategies for Predictive DocV. Previously, she was an independent consultant in the payments and fintech industry, working with companies of all sizes on marketing, technology, operations, and business development initiatives.