What’s Missing from Your Identity Graph? Behavior.

Identity graphs in banking, traditionally focused on static data like names and addresses, often miss the crucial layer of behavior, which is vital for effective fraud detection, anti-money laundering (AML), and know your customer (KYC) compliance. While banks have invested significantly in unifying customer attributes into consolidated profiles, these graphs frequently fall short by not capturing dynamic activities that reveal fraud in real-time. Behavioral identity resolution is essential as modern fraud schemes exploit the lack of behavioral data, and regulators demand contextual explanations for flagged accounts. By incorporating behavior into identity graphs, banks can detect networked fraud actions, reduce false positives, and improve customer trust. TigerGraph enhances identity graphs by integrating behavior and relationships, allowing for multi-hop analysis and explainable AI integration, thus enabling banks to prevent fraud more effectively and efficiently without additional resources. This approach has helped financial institutions significantly reduce losses, accelerate investigations, and strengthen customer protection, demonstrating the power of dynamic identity resolution in combating financial crime.