Synthetic identity fraud is one of the most damaging financial crimes today — in fact, per incident it can be up to 10 times more costly than third-party identity fraud. The “profit” per synthetic fraud opportunity is simply much higher — think benefits fraud, P2P fraud scams, or romance swindling. It’s also pervasive — Socure estimates that synthetics make up 1-3% of open accounts at U.S. financial institutions.
As consumers continue growing their digital footprints across devices, channels, and geographies, differentiating malicious synthetic behavior from that of good consumers is harder than ever. Synthetic identity patterns are complex and ever evolving. Traditional rules-based systems and third-party fraud solutions often fall short, because singular investigations within a customer’s user base won’t glean the multiplex relationship connections and fraud patterns that could be found through a consortium approach. Without the benefits of a network of known good and bad outcomes to stop synthetic fraud at the front door, your fraud investigation team has a limited ability to identify tricky elements of synthetic identity, exposing your business and customers to unnecessary risk.
So, what’s the answer?
Introducing Sigma Synthetic Fraud Model v4
Bringing together predictive machine learning algorithms and fraud investigators where needed, the v4 enhancements include:
Enhanced Fraud Capture and Precision
Sigma Synthetic Fraud now boasts a remarkable 71% synthetic identity fraud capture for users classified in the top 3% of the riskiest category. This heightened efficacy empowers financial institutions to identify and neutralize threats with high accuracy.
“Proof of Life” Features
Drawing from diverse data sources including property records, driver’s licenses, and educational data, proof of life data adds a new dimension of accuracy so organizations can confidently verify younger and immigrant demographics. Without these types of proof of life data sources, these segments of the population may otherwise appear to be synthetic fraudsters. Sigma Synthetic Fraud can recognize more people in these demographics as the good users they are, so organizations can approve more applicants.
Anomaly Detection in Social Security Number Metadata
Recognizing that fraudsters constantly evolve their tactics, Sigma Synthetic Fraud introduces features to detect anomalies in Social Security number (SSN) metadata. By analyzing SSN issue dates and variations in SSN in relation to credit age, this model effectively identifies deviations that indicate potential fraudulent activity.
Embedded Link Analysis
Link analysis reveals how Socure’s tens of thousands of identity elements including entity’s name, address, email address, phone number, SSN, DOB, IP address, and device are connected to each other. If a bad actor creates accounts using different names and SSNs but uses the same email address, phone number, or physical address, link analysis will quickly identify these linked fraudulent accounts.
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 detects suspicious tumbling techniques that are commonly used to commit synthetic fraud, so customers can block the bad actors behind them.
Email, Phone, and Address RiskScores boost product performance by identifying the riskiness of individual email, phone, and address PII elements and correlating the likelihood that a particular element is associated with the applicant or user.
End-to-End Identity Fraud Solution
Socure’s Sigma Fraud Suite including add-on device and behavioral analytics is the industry’s most accurate identity fraud detection solution that solves vastly different third-party and synthetic identity fraud challenges utilizing comprehensive network feedback, velocity intelligence, link analysis, entity resolution, and state-of-the-art machine learning.
Preemptive Protection Against Synthetic Threats
By harmonizing advanced technology, human insights, and an evolving understanding of fraudster tactics, Sigma Synthetic means that organizations can preemptively detect synthetic threats before damage occurs, confidently verify real identities, and foster consumer trust. With synthetic identity fraud growing in scale and sophistication, Socure’s multilayered approach offers a critical line of defense.
Learn more about Sigma Synthetic here.
Yarne Hermann is a Senior Product Manager for Socure's Sigma fraud suite. He began his career in software engineering with Socure, starting with the Sigma Device product, Socure's internal velocity feature engine, and later moved to Sigma Identity and Sigma Synthetic. Yarne holds a Master's in Computer Science from Columbia University.
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