Incognia launches real-time scam prevention powered by patented location intelligence
New solution combines real-world relationship intelligence with multiple risk signals to detect scams before money moves.
Incognia, the leader in cross-device risk intelligence, announced the launch of Real-Time Scam Prevention, an AI-powered solution that helps financial institutions detect and prevent payment scams, including authorized-payment fraud and social-engineering schemes, by combining Physical Social Graph intelligence with additional risk signals.
Authorized-payment scams present a growing challenge for financial institutions because the customer making the payment may be using a legitimate account, device and credentials. According to the FTC, consumers reported losing $3.5 billion to impersonation scams in 2025, nearly triple the amount reported in 2020. While traditional fraud controls focus on verifying the person initiating a transaction, they may miss signs that a legitimate customer is being manipulated into sending money to a scammer.
Incognia addresses this gap with its Physical Social Graph, powered by patented Trusted Locations technology. Using persistent, apartment-level indoor location intelligence, it reveals real-world connections between payment senders and recipients that conventional transaction data may miss, helping identify suspicious relationships and coordinated fraud 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 findings demonstrated how historical real-world relationships can provide valuable evidence of scam risk, even when conventional fraud signals appear 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 where users regularly spend time. Rather than relying on a snapshot of where a customer is when a transaction occurs, Real-time Scam Detection compares historical location patterns between the sender and recipient to create Incognia’s Physical Social Graph that may indicate additional risk.
Incognia’s AI model combines Physical Social Graph intelligence with mule-account detection, active-call detection, remote-access-tool detection, device integrity and location-spoofing detection to assess payment risk in real time. Banks use these assessments alongside their own data and policies to determine the appropriate action.
The solution also connects payment activity and real-world relationships across participating financial institutions, helping reveal suspicious networks and coordinated activity that individual banks may not be able to identify on their own.
“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.”
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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.
