Projects wrapped in phrases like “AI agents,” “quantitative investment,” and “Web3.0” are drawing closer scrutiny from regulators. The article says many of these offerings use high-return promises to lure participants, then rely on invitation codes, downline expansion, and rebate systems tied to recruitment, deposits, and rank levels. Crypto’s decentralized and harder-to-trace nature can make those schemes more concealed and easier to spread.
As of early August 2026, Hong Kong’s “Fun Coffee” virtual currency investment scam had received 225 reports involving HK$94 million, according to the article. It describes that case as one example of a broader stream of new fraud models.
How AI can identify a “sham project”
The piece describes AI as an around-the-clock online scout that can examine a project before launch. Instead of relying only on manual review of white papers, websites, and team disclosures, AI systems can process large volumes of public information for initial risk screening.
White paper and marketing review
According to the article, AI can flag repeated template language, weak technical originality, contradictions in technical descriptions, roadmaps that do not appear executable, and promotional claims such as “100% capital protection,” “guaranteed profits,” or “100x growth.” It also says AI can detect obvious signs of AI-generated material, including hollow wording and a lack of concrete technical implementation.
In June 2026, Shenzhen’s financial regulator issued a risk warning saying criminals were using labels such as “AI agents,” “AI quantitative investment,” and “Web3.0” as covers for illegal fundraising. The article says automated analysis of white papers and promotional materials can shorten the time needed to spot shared traits across these projects.
Social media risk analysis
Fraudulent projects often build momentum on social platforms before launch, the article says, using influencer endorsements, chat groups, sponsored posts, and manufactured hype. AI can monitor X, formerly Twitter, as well as Telegram and Discord for bot-driven traffic, synchronized promotion across multiple accounts, sudden follower buying, and FOMO-driven messaging designed to rush users into decisions.
The article says many Rug Pull projects create the appearance of a booming community before going live. Those anomalies, it argues, can be detected early through AI-based monitoring.
Cross-checking fundraising claims
Claims of backing from well-known investors are another common packaging tactic, according to the article. AI can cross-check whether an investment firm actually exists and whether it did invest in the project, whether named partners have publicly announced cooperation, whether team members are real and match their LinkedIn profiles, and whether the project’s GitHub shows sustained development or only empty repositories.
As one example, the piece says that if a project claims it was “backed by Binance,” AI can compare that statement with public information to test whether the claim is real.
How on-chain modeling can detect recruitment-based fraud
The article’s basic point is simple: white papers can be written to impress and social activity can be inflated, but fund flows are harder to fake. Because blockchain records are public, AI can use transaction data to build fund-flow models and keep watch for abnormal activity.
Pyramid-style capital structures
AI systems can trigger alerts when one core address keeps receiving funds, lower-tier addresses keep expanding, income depends heavily on money from new participants rather than operating revenue, and flows show a layered distribution structure. The article says this pattern closely resembles traditional recruitment-based pyramid schemes, and transaction-path analysis can expose those traits early in a project’s life.
Tracking rapid fund aggregation
The article describes a common path in scam cases: thousands of retail addresses send funds that are collected into at least several core wallets, then moved across chains to other public blockchains, and eventually sent to exchanges for cash-out. AI can automatically map those movements and connect scattered, cross-chain transactions into a full funding trail.
In the “Fun Coffee” case exposed in early August 2026, victims were first introduced by friends or relatives to download an app and were then induced to put in money, the article says. Hong Kong lawmaker Johnny Ng said the case involved a “Ponzi scheme” and a layered person-to-person recruitment method that used the concealment and tracing difficulty of virtual currencies. The article says AI-based on-chain modeling can clearly show how funds move upward from lower-level participants and make the pyramid structure visible.
Abnormal rebate models
The article says AI can also combine smart contract code analysis with on-chain transaction data to identify characteristics associated with online pyramid selling. These include promises of fixed daily returns unrelated to real business activity, returns that only rise and never fall, no visible market-risk hedging mechanism, multi-level commission structures beyond normal referral rewards, and referral payouts far above industry averages.
Risky address linkage
By combining historical blacklist databases, AI can automatically identify addresses already tagged as fraudulent, wallets linked to stolen funds, addresses associated with mixers such as Tornado Cash, wallets on international sanctions lists, and addresses tied to past scam projects. The article says that can help exchanges and regulators block risks earlier.
What AI can do in crypto compliance work
The article says AI is not meant to replace supervision. Its role is to shift oversight from broad manual searching to more targeted identification.
Building risk profiles for virtual assets
AI can combine project background, on-chain transactions, community behavior, KYC information, and address labels to create a fuller risk profile and provide data support for regulatory decisions, according to the article.
Spotting signs of illegal fundraising
The piece says AI can monitor high-yield marketing and exaggerated promises, multi-level commission structures, public fundraising without approval, and the rapid concentration of large volumes of capital. Once anomalies appear, the system can trigger automated alerts.
In February 2026, the People’s Bank of China and seven other departments issued the Notice on Further Preventing and Handling Risks Related to Virtual Currency (Yinfa [2026] No. 42), which made clear that business activities related to virtual currency are illegal financial activities. The article says AI systems can cross-check those regulatory signals against on-chain data to lock onto higher-risk projects more accurately.
Real-time alerts and faster case handling
When paired with on-chain monitoring systems, AI can be used for alerts on large fund movements, automatic warnings on suspicious addresses, real-time monitoring of hacking incidents, on-chain tracing of stolen assets, and automatic identification of high-risk projects. The article says this can move oversight away from a model centered on post-incident investigation and closer to early warning.
On the enforcement side, traditional investigations often require extensive manual work to analyze addresses and reconstruct fund flows. The article says AI can automate address clustering, fund-flow analysis, wallet linkage discovery, and transaction path reconstruction, helping investigators identify key evidence faster and shorten the time needed to handle cases.
Where AI regulation reaches its limit
The article also draws a line around what AI can and cannot do. It says AI may see through code and data patterns, but it cannot directly answer the core question of who is controlling a scheme behind the scenes.
Judgment depends on data quality and rules
AI outputs rely on data quality, model training, risk rules, and label accuracy. If the inputs are biased, the article says, normal projects may be wrongly flagged while well-disguised scams may still slip through.
Decentralization is not the same as illegality
Many DeFi protocols, decentralized autonomous organizations, and open-source projects are decentralized by design. Anonymous wallets are not automatically malicious, and large on-chain transactions are not automatically illegal, the article says. It argues that AI should not treat anonymous wallets, decentralized protocols, or large transfers as direct proof of wrongdoing without examining fund sources, transaction patterns, and behavioral traits.
Human review still matters
The article says a more reasonable future model would combine AI-based risk discovery, professional human judgment, and on-chain evidence analysis. In its framing, AI is a tool rather than the final answer: it can indicate that something looks wrong, but people still need to determine what the problem is.
The article’s closing view
The piece concludes that AI is becoming an important technical engine for blockchain security and compliance, spanning sham-project detection, on-chain fund-flow analysis, monitoring of recruitment-based fraud, and stolen-asset tracing. It says those tools are helping regulators, security firms, and project teams build more intelligent and efficient risk-control systems.
At the same time, the article says effective security governance still depends on coordination among AI, big data, on-chain analysis, professional security teams, and regulatory mechanisms. It adds that the security team at LingShi Technology continues to follow the convergence of AI and blockchain security and can provide services including on-chain fund tracing, smart contract security audits, risky address identification, anti-money laundering analysis, threat intelligence monitoring, and blockchain security training for government agencies, regulators, exchanges, and Web3 projects.