Traditional anti-fraud systems miss the subtle signs of a compromised account. We baseline normal behavior for every user, then surface deviations the moment they happen so your team can act fast.
Mule accounts are hard to spot because they start as legitimate customers. We track how every account behaves over time — and surface the ones that no longer match their own history.
We detect when accounts share the same operator, when groups coordinate across accounts, and when behavioral patterns reveal organized activity. We can map the full shape of a fraud ring from a single confirmed case.
When someone is being coerced, their behavior changes. We read those signals in real time, distinguishing a willing user from a coerced one, before an irreversible transaction goes through.
AI agents can now mimic human behavior convincingly enough to fool traditional detection. We separate legitimate automation from malicious agents by modeling the behavioral signatures that distinguish authorized activity from exploitation.
Define your own protection against the fraud topologies that affect your platform.
Our proprietary foundation model learns what every user on your platform looks like from how they physically interact with your app — mouse movement, keystroke timing, scroll patterns, touch behavior.
No fraud labels. No enrollment period. Identity, intent, and state of mind — all from behavior alone.
incandor