Incode Trust Graph is a fraud intelligence platform that operates through a collective defense network to detect and prevent identity fraud across organizations. The system analyzes risk signals from biometrics, documents, behavioral patterns, and device data to identify fraudulent activity. The platform detects known fraudster identities by recognizing when an identity, device, or document has been previously confirmed as fraudulent across the network. It identifies first-party fraud where legitimate users engage in fraudulent activities such as repeated chargebacks or account misuse. The system flags unusual activity patterns including velocity spikes and inconsistent logins that deviate from normal user behavior. Trust Graph tracks frequency and repeat attempts by monitoring the reuse of credentials, devices, or tactics across multiple fraud attempts. Organizations can contribute their internal fraud signals to strengthen the network while receiving real-time insights into threats flagged by other participants. The platform provides continuous monitoring that reassesses sessions over time as new risk signals emerge, even after initial onboarding. Organizations can define customizable risk rules tailored to their specific risk sensitivity, industry requirements, and compliance needs. All risk signals are anonymized to protect personally identifiable information while maintaining detection effectiveness. The system delivers relationship-based scoring across the trusted network to enable cross-organization fraud detection without exposing sensitive user data.