Incode Launches TrustGraph Fraud-Signal Sharing Network

Incode Technologies has launched TrustGraph, a biometric-anchored trust network designed to combat the rise of AI-driven "agentic fraud." By using peer-to-peer cryptography to link fraud signals across institutions without pooling sensitive customer data, the platform addresses a critical vulnerability where siloed defenses allow fraudsters to target multiple financial institutions simultaneously using synthetic identities.

What was announced

TrustGraph is a privacy-preserving network built for financial institutions, marketplaces, and AI companies to detect repeat and agentic fraud. The system operates on patented peer-to-peer cryptographic technology and Secure Multi-Party Computation (SMPC), which allows participating institutions to match fraud and trust signals without ever exchanging or pooling raw customer records. This architecture ensures that customer data remains with the original institution while still providing a real-time fraud signal during onboarding, login, or high-risk transactions.

The launch comes as Incode projects that agentic fraud—where AI agents automate the creation of synthetic identities and document forgery—will account for 60% of all fraud attempts by the end of 2026. In early access testing, TrustGraph reportedly surfaced up to 53% more fraud than existing models alone. By anchoring signals to verified biometric identities, the network can link seemingly unrelated applications that share device details, document fragments, or near-identical addresses, effectively exposing fraud rings that operate across different platforms.

The technology is the result of over a decade of research by Incode and Identiq, a cryptographic identity validation pioneer that Incode acquired in June 2026. Participating institutions, even direct competitors, can benefit from these shared signals without seeing the customer records of other members. The cryptographic architecture is specifically designed to protect the identities of both the institutions requesting signals and those providing them.

The companies involved

Incode Technologies is a global provider of AI-powered identity verification and fraud prevention solutions. The company specializes in creating secure digital identity platforms that utilize biometrics to streamline consumer experiences. In June 2026, Incode strengthened its position in the privacy-preserving technology space by acquiring Identiq. Identiq was founded by experts in security and identity and was known for its work in cryptographic peer-to-peer identity validation, which now forms the backbone of the TrustGraph network.

Interac, a prominent Canadian financial services brand, is an early adopter of the technology. The organization plays a central role in the Canadian payments and digital ID landscape. Navjeet Grewal, who serves as Managing Director of Verification and Digital Credentials at Interac, has noted that the scale of modern threats like deepfakes makes it impossible for individual businesses to defend themselves in isolation. The collaboration between these entities highlights a shift toward decentralized trust models where the focus is on verifying the legitimacy of a user through cross-network signals rather than relying solely on internal data silos.

What FF News has reported before

FF News has tracked Incode’s development across several key partnerships and product milestones. In 2023, we covered how the Mama Money and Incode Partnership Revolutionizes Customer Verification for Cross-Border Payments brought biometric security to the remittance industry. We also reported on the company's expansion into broader fintech infrastructure through the Incode Announces Strategic Partnership with Sardine in 2022. Our earlier reporting includes the 2019 announcement where INCODE DELIVERS SEAMLESS AND SECURE CONSUMER EXPERIENCES WITH NEXT-GENERATION DIGITAL IDENTITY PLATFORM first detailed the company's vision for a secure, end-to-end identity stack.

What this means

The introduction of a decentralized trust network represents a direct challenge to the traditional "siloed" approach to fraud prevention. For the fintech sector, the primary hurdle has always been the tension between the need for shared intelligence and the strict requirements of data privacy regulations. By proving that cryptographic matching can occur without data exposure, this model may force a re-evaluation of how trust is established in digital ecosystems. It places significant pressure on legacy fraud detection systems that rely on static data, as they are increasingly outmatched by the speed and volume of AI-generated synthetic identities. The success of this model will ultimately depend on network effects; the more institutions join, the harder it becomes for automated fraud agents to find a foothold.