Stop tracing stolen funds. Start preventing the exploit.
Legacy blockchain analytics are built for the past — tracing where stolen assets went after your protocol has been drained. Fraudalysis is built for the future — flagging rug pulls, money laundering, and drainer activity before capital moves.
The problem with reactive crypto forensics
For years, the standard approach to Web3 security has been reactive. When an exploit occurs: a protocol is drained, forensic analysts are hired, funds are traced across mixers and exchanges, and law enforcement requests are filed — hoping to freeze assets that may already be laundered.
The result: lost capital, damaged reputation, and long investigation cycles with low recovery rates. Reactive security is no longer viable in high-speed Web3 environments.
The Fraudalysis approach: continuous, automated surveillance
Fraudalysis shifts the paradigm from post-mortem forensics to live, automated prevention. The platform continuously monitors blockchain state, transactions, and smart contract behaviour, detecting risk signatures at the block level rather than waiting for a hack to happen.
How we outpace the status quo
| Capability | Legacy Forensic Tools | Fraudalysis Platform |
|---|---|---|
| Detection speed | Hours to weeks after the exploit. | Real-time — as blocks are proposed. |
| Alert type | Static alerts from historical blacklists. | Dynamic behavioural alerts from ML models. |
| Threat target | Known, already-published illicit addresses. | Unseen threat configurations — new rug pulls and drainers. |
| Integration | Manual research dashboards. | APIs and webhooks for direct integration. |
The pillars of the Fraudalysis advantage
Zero-day rug pull detection
Machine-learning models analyse smart contract deployment parameters and bytecode, flagging proxy manipulation capabilities, malicious minting privileges, and hidden blacklist-owner functions before a rug pull can be executed on buyers.
Behavioural wash trading detection
Blacklists can't catch wash trading because scammers rotate addresses. Fraudalysis uses clustering and graph algorithms to detect circular transaction patterns, self-funding wallets, and artificial volume inflation across DEXs and NFT marketplaces.
Mempool analysis
Scanning pending transactions across major EVM chains lets the platform alert protocol security gates to incoming frontrunning, flash loan, and sandwich attack patterns before they are written to the ledger.
Real-world use cases
DeFi protocols
Suspicious flash-loan funding sequences are flagged so automated circuit breakers can pause interactions for high-risk addresses before an exploit is triggered.
Wallets and gateways
A user about to connect to a freshly deployed drainer contract sees a high-severity alert powered by the real-time risk API — before the signature prompt.
Centralised exchanges
Layered transfer patterns are identified and flagged for a compliance verification hold before scam deposits settle.
Elevate your platform security
Don't wait for a security incident to expose your platform's vulnerabilities. Get in touch to see real-time, automated on-chain intelligence in action.