Moving Toward Identity Intelligence in Fraud Detection

Fraud Management & Cybercrime , Fraud Risk Management , ID Fraud Moving Toward Identity Intelligence in Fraud Detection Point Predictive's Frank McKenna on Detecting Hidden Signals in Synthetic IDs Suparna Goswami (gsuparna) • April 17, 2026     Credit Eligible Get Permission Frank McKenna, co-founder and chief fraud strategist, Point Predictive Fraud detection is moving beyond verification toward identity intelligence. Frank McKenna, co-founder and chief fraud strategist at Point Predictive, said synthetic identities leave subtle signals such as thin profiles and behavioral traits that demand deeper analysis. See Also: Building a Secure Agentic AI Framework in Financial Services Detecting these fake identities - often generated with fabricated information rather than using stolen identities - requires analyzing signals tied to behavior and digital presence rather than relying on static data points, he said. "They tend to have a very thin public profile. They won't have Facebook, Instagram, or social media accounts. These gaps create patterns that distinguish synthetic identities from real individuals," McKenna said. Indicators such as limited digital footprints, address clustering and inconsistent credit profiles help expose fraud attempts. Banks and other lenders are expanding detection strategies to include behavioral biometrics and data relationships across applications. In this video interview with ISMG, McKenna also discussed: Key signals that expose synthetic identities; The limits of traditional verification models; The role of behavioral and data-driven analysis in fraud detection. McKenna has advised more than 200 banks, lenders and finance companies worldwide to help them achieve reductions in fraud. He provides fraud management tips through his daily blog, FrankonFraud.com.