Identity fraud management refers to the use of strategic processes, technologies, and governance frameworks to detect and mitigate identity-related fraud. These processes include continuous monitoring, robust incident response protocols, post-incident root-cause analysis, and adaptive controls that often leverage AI and machine learning so that they can evolve with emerging threats, ensuring long-term resilience against fraud attempts and ongoing regulatory compliance.
Use case/ examples of identity fraud management
Activity monitoring: Creating a centralized fraud monitoring system to detect anomalies and unexpected behavior across digital banking platforms, using AI and machine learning to allow these analytics tools to learn emergent fraud patterns and improve over time. These centralized platforms should also be able to coordinate fraud alerts, case investigation, and fraud mitigation in a unified way, supporting efforts both within a financial institution and in the data they share with other institutions.
Risk scoring: Integrating automated, AI-powered risk scoring into account onboarding flows to better assess identity legitimacy in real-time, streamlining the onboarding process for most customers while ensuring higher risk accounts are flagged for manual review.
