Socure, a provider of artificial intelligence for digital identity verification, fraud prevention and sanctions screening, recently launched Sigma Synthetic Fraud v4.

The company highlighted the product uses advanced machine learning and diverse, third-party and network feedback data to uncover patterns linked to insidious synthetic identity fraud.

According to a Socure news release, the Deloitte Center for Financial Services expects synthetic identity fraud to generate at least $23 billion in losses by 2030.

Socure explained that synthetic identity fraud is a financial crime where a real person’s information is stolen and combined with other falsified personal information to create a fictitious identity, further used for fraudulent purposes.

After a perpetrator opens an account using the synthetic identity, they typically build up a positive credit score, open multiple accounts, and often appear to be good customers while going undetected until they decide to cash in, or “bust out” by using up all available credit lines and disappearing, according to Socure.

Socure said it can detect and stop synthetic fraud at onboarding before the fraudster can act nefariously in the financial ecosystem.

According to a comprehensive study, Socure estimated that synthetics make up 1-3% of open accounts at U.S. financial institutions.

Sigma Synthetic Fraud v4 draws from diverse “proof of life” data sources including property records, driver’s licenses, and educational data adding a new dimension of accuracy so organizations can confidently verify younger and immigrant demographics with a limited digital footprint.

Without these types of proof of life data sources, Yigit Yildirim, senior vice president of fraud and risk products at Socure, explained these segments of the population may otherwise appear to be synthetic fraudsters and be shut out of the financial ecosystem.

“Synthetic fraud cannot be accurately detected with rules-based systems or third-party fraud solutions,” Yildirim said in the news release. “Socure’s AI engine analyzes anomalies to uncover multiplex synthetic-specific features that distinguish legitimate thin-file consumers from synthetic fraudsters with high accuracy in real-time — and without causing friction for good users.”

Socure also pointed out that synthetic identity fraud can occur when criminals blend genuine and falsified information to create new, fictitious identities to fraudulently apply for loans, credit, government benefits, or move illicit funds.

As fraudsters’ AI-supported schemes become more sophisticated, Socure acknowledged differentiating malicious synthetic behavior from that of good consumers is more tangled than ever and has made it the fastest-growing form of financial crime in the United States.

“Per incident, synthetic fraud can cost 10 times more than third-party identity fraud. The ‘profit’ per synthetic fraud opportunity is much higher, such as with benefits fraud, P2P fraud scams, or romance swindling,” Socure said.

To help finance companies of all stripes, Socure highlighted that Sigma Synthetic Fraud v4 enhancements include:

—Innovative email risk enhancements: Email tumbling, or when people create “alias” email addresses by adding punctuation marks like periods between letters, often indicates ill intent. Sigma Synthetic Fraud v4 can detect tumbling techniques that are commonly used to commit synthetic fraud, so customers can block the bad actors behind them.

—Consortium data including feedback: Bringing together a network (Socure Risk Insights Network) of more than 1,900 of the world’s largest organizations that span diverse industries and government agencies allows Socure to identify multiple identity elements across the consortium and continually optimize machine learning algorithms to drive the highest accuracy in the market. Bolstered by more than 150 million rows of outcomes in the past year, Socure’s database now totals two billion known good and bad identities.

—Human-in-the-loop machine learning: Socure fraud investigators can provide clean, corrected, and properly classified fraud labels for unlabeled or mislabeled raw data. The labeled data, based on actual synthetic incidents and patterns, becomes training data. Thus, the model is trained to think like a fraudster and applies this intelligence to become smarter at detecting evolving synthetic threats. This unique machine-human intelligence can be used to identify synthetic identities at onboarding, and account changes, and uncover “sleepers” hiding within portfolios.

—Real-time fraud attack detection: Socure’s velocity engine can track how often someone’s personal information is used in applications, as well as how often that information is linked to other data across the Socure Risk Insights Network. Analyzing all of this data on a large scale can help predict fraud attacks before they happen.

—Embedded link analysis: Link analysis searches tens of thousands of correlations between an entity’s name, address, email address, phone number, SSN, DOB, IP address, and device intelligence to track fictitious identities across the Socure Risk Insights Network.

“For example, suppose a bad actor creates accounts using different names or SSNs but uses the same email address, phone number, or physical address. In that case, link analysis will quickly identify these linked fraudulent accounts,” the company said.

The largest enterprises and government agencies stop synthetic identity fraud with Socure’s multi-layered, best-in-class approach which correlates PII, events, and behaviors across businesses and locations using real-time and historical data, velocity intelligence, entity resolution, and link analysis,” the company went on to say.

For more information about Sigma Synthetic Fraud v4, visit Socure’s website.