Incognia, a leader in cross-device risk intelligence, has introduced Real-Time Scam Prevention, an AI-powered solution designed to help financial institutions identify and prevent payment scams, including authorized-payment fraud and social-engineering attacks. The solution combines Physical Social Graph intelligence with multiple risk signals to detect suspicious transactions before funds are transferred.
Authorized-payment scams are an increasing concern for financial institutions because victims often initiate transactions using legitimate accounts, devices and credentials. According to the FTC, consumers reported losing $3.5 billion to impersonation scams in 2025, nearly triple the amount reported in 2020.
Traditional fraud prevention systems typically focus on verifying the identity of the person initiating a payment. However, these controls may fail to detect situations in which a legitimate customer has been manipulated into transferring money to a fraudster.
Incognia aims to address this challenge through its Physical Social Graph, which is powered by patented Trusted Locations technology. Using persistent, apartment-level indoor location intelligence, the technology identifies real-world connections between payment senders and recipients that conventional transaction data may not reveal. These insights help financial institutions identify suspicious relationships and potentially coordinated fraudulent activity.
In an analysis of transactions from a major financial institution, Incognia found that its Physical Social Graph technology alone identified approximately 70% of scam transactions in the analyzed dataset. The results highlight how historical real-world relationships can provide meaningful evidence of potential scam activity, even when conventional fraud indicators suggest that a transaction is legitimate.
“Banks have gotten very good at determining whether the person making a payment is legitimate, but with a scam, the legitimate customer is often the one making the payment,” said André Ferraz, co-founder and CEO of Incognia. “The question becomes – who are they sending money to, and does that relationship make sense? That is the context Incognia is bringing into scam detection.”
Incognia’s patented Trusted Locations technology establishes persistent location patterns based on the places where users regularly spend time. Instead of relying solely on a customer’s location at the moment a transaction occurs, Real-Time Scam Prevention compares historical location patterns between senders and recipients to build Incognia’s Physical Social Graph, identifying relationships that may provide additional context for assessing payment risk.
The solution’s AI model combines Physical Social Graph intelligence with several additional risk signals, including mule-account detection, active-call detection, remote-access-tool detection, device integrity and location-spoofing detection. By evaluating these signals in real time, the system provides financial institutions with a more comprehensive assessment of potential payment scams. Banks can incorporate these assessments into their existing data and policies to determine the appropriate response to a transaction.
The solution also links payment activity and real-world relationships across participating financial institutions. This cross-institutional perspective can help uncover suspicious networks and coordinated fraud patterns that individual banks may be unable to detect using their own transaction data alone.
“Two people can live in the same building and have no real-world relationship, while two people living in completely different cities may know each other well,” said Alan Gomes, CTO of Incognia. “That’s why distance alone isn’t enough. Our apartment-level indoor location intelligence identifies patterns and connections over time, giving banks context that IP addresses or GPS coordinates alone cannot provide.”
For more information, visit Incognia.com.
About Incognia
Incognia provides cross-device risk intelligence for companies that rely on trusted digital interactions. Its technology blends location behavior, device integrity and behavioral analysis to create a risk signal that cannot be faked at scale and shows whether a user is trusted or risky. With real-time assessments and actionable data, Incognia helps organizations stop repeat and large-scale fraud while reducing friction for legitimate users.
Media Contact: Grace Rigas, Incognia@983group.com
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