Jumio Disrupts Identity Verification Market with Groundbreaking New Fraud Prevention Technology that Leverages Cross-enterprise Data

Jumio 360° Fraud Analytics uses AI-driven predictive analytics to detect fraud rings and other coordinated attacks

Jumio, the leading provider of automated, end-to-end identity verification, risk assessment and compliance solutions, unveiled at Money20/20 USA Jumio 360° Fraud Analytics, its new fraud-fighting technology that uses AI-driven predictive analytics to identify fraud patterns with more sophistication and accuracy than ever before.

According to Jumio’s analysis, 25% of fraud is interconnected — either perpetrated by fraud rings or by individuals using the same information or credentials to open new accounts on banking sites, ecommerce platforms, sharing economy sites and more. A single organized crime operation or fraud ring can cause damage into the hundreds of millions of dollars. And with rapidly evolving AI at their disposal, fraudsters are becoming more sophisticated than ever before, leaving businesses scrambling to stay ahead.

“Because explainability is a key requirement when using automation, we provide insights on why the identity transaction was rejected”

Jumio 360° Fraud Analytics tackles this problem with a whole new approach to stopping fraud. It looks beyond simple linkages, such as flagging someone because they are connected to a known fraudster. Instead, it looks at billions of data points across Jumio’s cross-industry network to identify patterns based on behavioral similarities and other indicators. Using graph database technology with a layer of machine learning, it groups identity transactions into clusters across the network and determines the fraud risk of each cluster. This provides a multi-dimensional view of each transaction and the cross-customer ecosystem as a whole.

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“Jumio revolutionized online identity verification when it pioneered the ID + selfie approach,” said Robert Prigge, Jumio CEO. “Today we are pushing the industry to evolve once again by harnessing the power of predictive analytics to identify fraud patterns across our vast network. This will allow us to identify and stop fraud rings and other coordinated attacks with more accuracy than ever before.”

Today, when a user goes through the ID verification process, Jumio performs a wide variety of advanced, AI-driven fraud checks. With the addition of Jumio 360° Fraud Analytics, the identity transaction will also be compared to the clusters and generate a predictive fraud score that can be used to automatically reject the transaction if it exceeds a certain threshold. Jumio’s initial studies show that this approach improves its existing, highly accurate fraud detection rate by at least 30% — without increasing the false rejection rate. Business users can audit the reasons behind the decision in the Jumio Portal or via APIs.

“Because explainability is a key requirement when using automation, we provide insights on why the identity transaction was rejected,” said Philipp Pointner, Jumio chief of digital identity. “We also provide a graphical representation of the high-level linkages we found, which makes it easy to see connections between people, documents, devices and more. The combination of sophisticated analytics and easily consumable insights makes this new technology a game-changer for fighting fraud.”

Key features include:

• Graph database technology and machine learning classify identity transactions into clusters based on behavioral similarities, which is especially powerful for identifying fraud rings
• AI-driven predictive analytics determine the likelihood of fraud risk for each identity transaction based on its cluster association
• The predictive fraud score can be used to automatically reject identity transactions, stopping fraudsters from accessing your platform in real time
• Dashboards provide transparency and help you visualize connected data

Jumio 360° Fraud Analytics is currently available in early release to select customers and will be generally available in early 2024.

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