Prevent Synthetic Identity Risk with Multi-Layered Controls
Synthetic fraudsters are exploiting security gaps at a whirlwind pace. No sector or industry is safe.
Rely on the holistic protection delivered with Socure’s multi-layered controls to block harmful Synthetic identities.
The Socure Difference
Utilizing human-in-the-loop machine learning models and trained with consortium data across multiple industries, Sigma Synthetic Fraud pinpoints synthetic fraud patterns to precisely identify risk—within milliseconds.
Cut Financial Losses
Leverage the power of human-in-the-loop ML to prevent losses
Improve the User Experience
Reduce false positives and boost user satisfaction with quick and reliable approvals
Protect Your Ecosystem
Stop bad actors from exploiting accounts to commit financial crimes and harm other consumers
Open More Good Accounts
Drive expandable growth by auto-approving more good customers faster
Lower Operational Costs
Depend on precise ML detection to reduce manual reviews and operational expenses
Prevent Reputational Damage
Avoid enforcement action and fake account prosecution
Human-in-the-Loop Machine Learning
Sigma Synthetic Fraud combines the knowledge and experience of expert fraud investigators with the computational and statistical capabilities of advanced ML to mitigate rapidly evolving and complex synthetic patterns.
A Model that Thinks Like a Fraudster
Sigma Synthetic Fraud ingests clean labels—based on fabricated and manipulated definitions and actual synthetic fraud incidents. So, the model is trained to think like a fraudster and applies this smarter intelligence to detect evolving synthetic threats.
Fabricated and Manipulated Definitions
The Sigma Synthetic Fraud model applies the Federal Reserve’s definition of synthetic identity fraud which focuses on “manipulated” and “ fabricated” types to support customers in determining the optimal follow-on treatment strategy without adding friction to the onboarding process.
Diverse, Dynamic Data Sources
Credit header, credit inquiry, and hundreds of other public records data sources combine with velocity data, representing diverse segments and industries, to power real-time risk detection that identifies anomalous synthetic patterns.
Comprehensive Consortium Data
Network consortium data constantly improves model performance and mitigation of emerging threats across various industries and segments.
Sigma Synthetic Fraud Score
The model returns a predictive fraud score that gauges risk calculated on thousands of synthetic-specific features, linkages, correlation, and patterns of identity elements across Socure’s network.
Explainable ML translates to an ability to explain why an identity scores a particular way, with simple-to-understand Reason Codes that are delivered with each score to provide insight into the decisioning.