According to the 2026 Crypto Crime Report released by Chainalysis, crypto scams generated an estimated $17 billion in illicit profits in 2025. Even more alarming, impersonation-based scams surged by 1,400% year over year, while AI-generated scam techniques overtook traditional cyberattacks to become the primary method of theft.
This marks a critical shift in crypto crime: attackers are no longer focused on breaking systems—they are manipulating people and transaction behavior itself.
The Nature of AI Scams Has Changed: From System Exploitation to Behavioral Manipulation
Compared with early phishing websites, private key theft, or smart contract exploits, AI-driven scams exhibit three defining characteristics:
- Extreme Realism AI-generated voices, videos, and social accounts convincingly impersonate project teams, customer support, acquaintances, or institutions, making surface-level verification ineffective.
- Compliant Identities, Abnormal Behavior Victims often use real identities and fully KYC-compliant accounts, initiating transfers voluntarily.
- Highly Structured Fund Flows Stolen assets are rapidly split, aggregated, bridged across chains, or converted into stablecoins, forming recognizable—but not immediately obvious—risk patterns.
- This exposes a hard truth: KYC alone cannot stop users from transferring funds after being deceived.
The Core Challenge for Regulators and Platforms: Identity Compliance Does Not Equal Transaction Safety
In AI scam scenarios, most transactions appear “legitimate” on the surface:
- The sender is a verified user
- The assets seem to come from lawful sources
- Smart contract calls and transfers follow on-chain rules
Yet in outcome, these transactions represent systematic value extraction at scale.
Effective risk control therefore no longer hinges on who the user is, but on a more critical question:Does this transaction make sense from a behavioral perspective?
This is precisely where KYT (Know Your Transaction) becomes essential.
KYT’s Role in the Age of AI-Driven Fraud
Unlike traditional controls, KYT focuses on transaction behavior itself, enabling proactive risk detection in an environment where AI scams are rapidly evolving.
Our KYT product is designed to address modern scam dynamics through the following capabilities:
- Abnormal Transfer Detection Identifies sudden large transfers, unusual transaction frequencies, and fund movements that sharply deviate from historical user behavior, flagging irrational or coerced actions.
- Scam Fund Path Modeling Builds behavioral models for common scam flows, accurately identifying high-risk patterns such as rapid splitting, aggregation, cross-chain hopping, and stablecoin parking.
- High-Risk Address and Behavior Network Analysis Goes beyond labeling single blacklisted addresses to detect entire scam-related address networks and fund-flow structures, preventing risk propagation.
- Real-Time Alerts and Control Integration Triggers alerts during transaction execution and supports integration with delayed settlement, manual review, and transaction limits to reduce actual losses.
From “Defending Against Hackers” to “Defending Against Deception”: A Shift in Security Priorities
The $17 billion figure does not represent a failure of cryptography or infrastructure—it reflects the cost of insufficient understanding of transaction behavior. In an era where AI can convincingly fake identities, narratives, and trust, the one element that remains difficult to fake is the internal consistency of fund behavior.
Future crypto security will not be defined by system complexity, but by who can identify abnormal behavior earlier and interrupt risk paths faster.
Conclusion: In the AI Scam Era, KYT Is No Longer Optional—It Is Foundational
The Chainalysis report does not describe a future threat; it documents a present reality. In an ecosystem where scams are increasingly “legitimate-looking” and behavior is highly deceptive, KYT is evolving from a compliance tool into a core security infrastructure for crypto.
Whether losses actually occur will increasingly depend on whether abnormal transaction behavior is recognized before the next scam transaction is completed.