"Fraud Investigators: The Secret Component to Help Machine Learning Models Detect Synthetic Fraud

As the world moves toward even greater levels of digital commerce, and fraudsters evolve from merely impersonating other people to fabricating fake (aka “synthetic”) identities—a particularly dangerous type of identity fraud—automated detection is the only way to keep up with scaled attacks.

Human-in-the-loop vastly improves the performance and transparency of machine learning (ML) datasets for automated models to detect synthetic identity fraud. Fraud investigators uncover patterns and apply domain knowledge to fraud labels to train the ML platform. This means synthetic identities are detected with far greater precision, which dramatically reduces false positives and manual reviews.

Hear from a front-line fraud investigator why synthetic identity fraud is so complex and difficult to detect, and learn about the behaviors and patterns that set it apart. Take part in an interactive opportunity to test your skills at choosing between synthetic and legitimate identity profiles.