SEON, the AI Command Centre for fraud prevention and anti-money laundering compliance, has expanded its Signal Intelligence capability, lifting its proprietary, directly sourced data foundation from more than 900 signals to over 1,100.
The upgrade adds fresh coverage across address verification, session behaviour tracking, and phone and carrier data, on top of deeper digital footprint and device-level signals.
The aim is to help risk and compliance teams spot inconsistencies in a customer’s identity and trace the shared infrastructure that links fraud rings together, all without adding friction to the customer journey.
Generative AI has removed much of the effort that fraud once required. Where criminals previously had to gather evidence, complete forms and run accounts individually, convincing profiles and matching device histories can now be produced in minutes with little more than a prompt.
The Financial Action Task Force has noted that a smartphone alone is enough to create a persuasive deepfake in roughly the time it takes to open a social media account, while ACAMS research shows 75% of anti-financial crime professionals have named GenAI misuse their leading emerging concern for three years running. Criminals still choose who to target and which identity to fake, but AI tools now do the manufacturing at speed.
Individually, fraudulent signals rarely raise suspicion. An email clears validation, a device looks untouched, an address checks out. It is only once these elements are cross-referenced that fabricated details tend to surface. SEON’s broadened signal set gives teams more independent evidence to draw on across identity history, connected infrastructure and live behaviour, feeding that context into decisions made by rules engines, human analysts and AI agents alike.
Within the expanded Digital Footprint checks, SEON now traces where an email address or phone number has surfaced across a wider range of platforms over time, including AI developer tools, job-hunting sites, property portals and dating apps, while Phone Intelligence brings in SIM-swap and porting records so teams can judge whether a number has a genuine history rather than appearing from nowhere.
Address Intelligence converts inconsistent address entries into a usable risk indicator, validating and standardising addresses across more than 240 countries and attaching fixed identifiers to specific properties, which flags cases where separate accounts cycle through unit numbers or formatting tweaks tied to one location.
Device Intelligence has also been broadened to pick up AI-agent activity, compromised iOS handsets, mismatched Android eSIMs and discrepancies between a device’s network and its country when a VPN is disguising the real IP address.
Session Monitoring follows customer activity from onboarding through login, account recovery, checkout and payment, allowing teams to identify automation, remote access tools, background activity or live calls mid-session and step in before it develops into a takeover.
Every new signal sits inside SEON’s AI Command Centre, feeding rules, alerts, manual reviews and network investigations, and is also accessible to outside AI tools via SEON’s Model Context Protocol server, letting investigators’ own agents reason from the same underlying evidence as human analysts.
SEON co-founder and CEO Tamas Kadar said, “AI has made a believable identity cheap to produce. What fraudsters cannot easily do at scale is build a consistent history for every account without reusing infrastructure. That is where our signal foundation makes the difference. The more dimensions a fraud team can check simultaneously, the harder it is to hide an identity that does not add up.”
For fraud and risk leaders navigating an environment where synthetic identities are cheaper and faster to produce than ever, staying ahead demands more than reactive checks. It requires continuous, strategic visibility into the signals that expose what automation alone cannot fabricate, giving decision-makers the early insight to act before exposure turns into loss.
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