AI
Our AI moves faster than the attacker
Incode runs its own agentic attacker against its defenses, so every new attack becomes a shipped model the same day.
Agentic Fraud
Incode AI stops agentic fraud faster than threats evolve
Agentic fraud is fraud run by autonomous AI agents, and Incode's own models learn from every attack to stop it across onboarding, login, and recovery.
The challenge
AI didn't make the fraudsters smarter. It made them infinite
Agentic fraud is already inside onboarding and login flows, running at machine pace against defenses tuned for people.
Rules and vendor models update in weeks, the agent iterates in minutes, and every miss carries a dollar figure.
60%+
of fraud attempts expected to be AI-assisted by the end of 2026
Incode
+55%
growth in injection attacks in a year, where a forged camera feed is fed straight into the verification flow
Incode
33 sec
between onboarding attempts in one documented fraud operation
Incode
How it works
The defense is ready when the attack appears
Incode has built and retrained every model in-house for 10 years, so updates ship on its own schedule.
Learn more: DeepsightLiveness detectionDocument VerificationTrustGraph
How we fight it
Agentic fraud is our training data
Incode verifies the identity behind every account, turns every failed attack into training data, and retrains its models every day.
Data
We operate where fraud is hardest
Incode is built where fraud is most sophisticated, so 7.1 billion trust checks can surface a new vector early.
Network
An attack on one trains the defense for all
More than 700 enterprises contribute signals, and TrustGraph turns a catch at one company into detection for the rest.
Use cases
Cover every gate an agent can probe
Agentic attacks target any flow that decides who gets in, across fintech, banking, crypto, and payments.
Onboarding & Identity Verification
Stop synthetic identities and forged documents at account opening, the gate agents probe first.
ExploreDocument Verification
Authenticate the government ID at any gate, including the AI-generated documents agents produce at scale.
ExploreAccount Takeover
Block recovery-flow attacks by verifying the human rather than the credential an agent already holds.
ExploreAuthentication
Prove a real human is present behind every returning login, without adding friction.
ExploreDeepfake Defense
Catch the synthetic faces and injected camera feeds agents generate at scale.
ExploreEmployee Onboarding (KYE)
Screen for fake candidates and help-desk impersonation as agent-driven attacks reach workforce access.
ExploreAgentic Identity (KYA)
Tell a customer's agent from an attacker's: bind every AI agent to a verified human and audit its actions.
ExploreMethods
The identity stack behind the defense
Every check runs on the same in-house stack, from capture to fraud decision, so every layer learns from every attack.
Know Your Human (KYH) proves a real person is present, and Know Your Agent (KYA) binds every agent to one.
Incode's agentic fraud prevention runs inside existing onboarding, login, and recovery flows through platform integrations.
Global enterprises rely on the Incode platform
Incode operates where fraud is hardest, serving the clients attackers hit first
99.99%
of fraud caught by Deepsight
7.1B+
trust checks run on the Incode platform
400M
identity profiles informing every model
700+
enterprises contributing fraud signals
What customers say
“How do you expect every single business to keep up with deepfakes, synthetic identities, and romance scams on its own? It is impossible. That is why we chose Incode, which has the strongest solution in the market today.”
Navjeet Grewal · Managing Director, Verification and Digital Credentials, Interac
What is agentic fraud?
Agentic fraud is a class of identity fraud in which an autonomous AI agent, rather than a human operator, executes the full attack workflow: it finds targets, generates fake documents or media, submits them, observes how the system responds, and adapts and retries at scale without human involvement. Its defining feature is this adaptive feedback loop.
How does Incode detect and stop agentic fraud?
Incode runs every attempt through in-house liveness, Deepsight deepfake detection, document forensics, and face match, retrains its models every day, and shares each catch across more than 700 enterprises through TrustGraph.
Does Incode's agentic fraud prevention require changing existing onboarding or login flows?
No: Incode's agentic fraud prevention runs inside existing onboarding, login, and recovery flows through platform integrations, and deploys in SaaS, private cloud, or bare metal.
What does the Incode network add against agentic fraud?
More than 700 enterprises contribute fraud signals, and TrustGraph turns an attack caught at one company into detection for the rest, so a synthetic identity burned at one company can be caught at the next.
How do Know Your Human (KYH) and Know Your Agent (KYA) work together against agentic fraud?
Know Your Human (KYH) proves a real, live person is present at each gate, and Know Your Agent (KYA) binds every AI agent to that verified person, so an autonomous attacker must pass both.
How does Incode prove its agentic fraud prevention works?
Incode tests each layer against public benchmarks: 0% error across three iBeta PAD levels, a 0.2% deepfake false-accept rate against 13.7% for the next best system, top-tier NIST results since 2018, and zero pentesting bypasses.
Where does the Incode platform run?
The Incode platform deploys in SaaS, private cloud, or bare metal, runs at more than 99.99% reliability, and supports enterprise-level security and compliance, so regulated environments can keep verification inside their own infrastructure.
How does Incode retrain its models against agentic fraud?
Incode retrains from as few as 10 samples: a new attack pattern becomes training data the moment it appears, the update ships the same day, and Incode's own agentic attacker tests the defense continuously.
What's next
See the numbers behind the defense
Request a demo and walk through the benchmarks, the pentesting record, and the daily retrain loop.