Incognia Unveils Advanced Scam Detection Tool Using Location Insights

Incognia has introduced a revolutionary fraud prevention system that aims to thwart payment scams before they occur by analyzing historical location data and the physical interactions between payment senders and recipients.

Understanding the New Fraud Prevention System

The company’s Real-Time Scam Prevention product integrates its unique Physical Social Graph technology with behavior analysis, device intelligence, and mule-account detection to evaluate transactions as they happen. This innovative approach seeks to fill a significant gap in traditional fraud prevention efforts, which often fail to detect scams where a legitimate account holder is involved in the transaction.

André Ferraz, Incognia's co-founder and CEO, emphasizes, “While banks have made strides in confirming the legitimacy of the person making a payment, scams often involve the genuine customer transferring money to a scammer.” The focus shifts to the nature of the relationship between the sender and receiver, introducing a critical context for scam detection.

Targeting Authorized Push Payment (APP) Fraud

Ferraz elaborates that APP fraud presents one of the greatest challenges to the financial sector. “This type of fraud is especially tricky as the true account holder is the one making the payment, often believing they’re sending money to someone trustworthy,” he stated.

Examples include romance scams, where one partner deceives another, impersonation scams involving alleged bank representatives, and requests for money from individuals posing as family members online. According to the Federal Trade Commission (FTC), consumers reported a staggering loss of $3.5 billion to impersonation scams in 2025, nearly threefold that of 2020.

The Technology Behind Incognia's Solution

At the heart of this system is Incognia's patented Trusted Locations technology, designed to discern users’ physical behaviors and establish individual identities based on location patterns. The system can pinpoint device locations with remarkable accuracy—within nine feet, a level of precision far superior to standard GPS. This capability extends even to distinguishing between floors within a building.

Furthermore, Incognia's Physical Social Graph leverages this location intelligence to form a network based on the real-world relationships of users. Ferraz describes the formation of connections through overlapping Wi-Fi signals and Bluetooth devices, signaling in-person interactions. The strength of these connections is assessed, highlighting relationships with family, coworkers, and acquaintances differently.

Why Traditional Banks Struggle

When a bank reviews an APP fraud transaction, everything seems legitimate—the right person, trusted device, and potentially recognizable location are all accounted for, leading to the approval of the payment.

Ferraz notes that the industry has faced immense difficulties in tackling this issue, with some vendors pointing fingers at social media platforms and telecommunications providers. Regulatory measures have emerged in places like the UK, where banks must reimburse scam victims equally.

Typically, banks assess various signals, including device intelligence and behavioral biometrics. However, Ferraz points out that they often focus only on the sender, lacking robust analysis of both parties involved in the transaction. Fraudsters frequently create new accounts rapidly, slipping through conventional verification processes.

Data Insights and the Effectiveness of the Solution

Incognia conducted an analysis of 230 million transactions over a month across three banks, identifying connections between users and their legitimacy. Strikingly, they found that 80% of genuine payments were made between users with first-degree relationships, indicating a real-world connection. Alarmingly, over 65% of scams occurred between individuals who lacked even a third-degree relationship.

The Physical Social Graph technology was able to flag around 70% of suspicious transactions within this dataset, underscoring the importance of historical relationships in evaluating scam risks, even when other verification signals may appear legitimate. Ferraz asserts, “We believe we have effectively addressed the challenge of scams.”

Real-Time Transaction Risk Assessment

When initiating a payment through a platform like Zelle, the customer’s banking app communicates with Incognia’s API to assess potential risks associated with the transaction.

This system uses a combination of technologies, including mule-account detection and location anomalies, to evaluate payment risks efficiently. If Incognia suspects a recipient may be operating a mule account—defined as having inconsistent device behavior or location history—it alerts banks to heightened risks.

Collaboration in Fraud Prevention

As fraud networks continue to evolve, the importance of collaboration among fraud prevention vendors and financial institutions has grown. Though vendors may not currently share insights widely, there’s a hopeful trend among banks to collaborate through third-party providers to tackle these issues robustly.

Incognia emphasizes the significance of anonymizing data to protect user identities while still enabling effective fraud prevention. By utilizing anonymized identifiers instead of personal information, banks can share valuable insights without privacy concerns.

Conclusion

With the rise of payment scams and associated fraud risks, Incognia's innovative use of location intelligence presents a promising solution. By understanding the real-world connections between individuals, this approach allows banks to stay ahead of fraudsters and protect their customers more effectively.