5 major behaviors criminals use to hide money in crypto in 2025 1️⃣ Layering & Fund Dispersion Stolen assets are rapidly split across multiple wallets, then pushed through complex chains of transactions. The goal: bury the trail & make it nearly impossible to trace back to the source. 2️⃣ Exploiting DeFi Services Decentralized Exchanges (DEXs), lending markets & cross-chain bridges are prime tools. 🔄 Swap tokens (e.g. USDT → DAI) 🌉 Move funds across blockchains (ETH → BTC) Why? No KYC. No gatekeepers. No traditional oversight. 3️⃣ High-Frequency, Low-Balance Transfers Instead of big moves, criminals go fast-in / fast-out: ⚡️ Zero-out accounts that immediately push funds onward 💸 Countless small-volume transfers instead of large ones This “churn” helps avoid freezes & detection. 4️⃣ Fake Tokens & Counterfeit IDOs Scammers create fake projects or liquidity pools to cycle dirty funds. 🎭 Masquerade as ordinary speculators 💰 Manipulate token prices → sell → convert into “clean” assets Result: illicit gains disguised as investment profits. 5️⃣ Mixing Services Mixers like Tornado Cash pool many users’ crypto together, then redistribute. The outcome? A broken transaction trail. Add ZK tech & privacy protocols → tracing becomes nearly impossible. 💡 These methods highlight the cat-and-mouse game between launderers & regulators. As Web3 evolves, so do the laundering tactics—and so must detection & AML/CFT strategies on OpenAML and OpenKYT.

While banks are still working on scaling agentic AI, crypto is already setting the pace. TRM Labs, Chainalysis, and Elliptic use it to detect illicit blockchain activity, while Binance cut false positives by 60% with AI-driven systems. Initiatives like OpenAML aim to make MCP investigations faster and cheaper

Introducing OpenAML: DTCC AI Hackathon Winner, now part of FINOS (Linux Foundation) We’re proud to announce OpenAML — an open-source AI-powered framework for detecting suspicious activity on public blockchains. Built for transparency, modularity, and real-world compliance, it took home the top prize at the DTCC AI Hackathon and is now part of FINOS, the open finance initiative under the Linux Foundation. Why OpenAML? Traditional AML tools in crypto are black-boxed, expensive, and often behind the curve. OpenAML is designed to be: •Open •Interoperable •Fully auditable All while using AI and graph analytics to trace and score transactional behavior across EVM-based chains. 🔍 What it detects: We don’t just follow tokens — we analyze how money moves. Key detection capabilities include: •🧠 Behavioral pattern recognition (e.g. layering, smurfing, circular flows) •🌑 ZKP transfer inference (e.g. Tornado Cash, Aztec) through indirect heuristics •🌪️ Mixer usage detection and flow correlation •🕸️ Graph analysis for wallet clustering, proxy routing, and obfuscation •🅿️ “Parking” behavior: idle wallets before large withdrawals •💸 Withdrawal funnels to CEXs, OTC desks, or cross-chain bridges ⚙️ Under the hood: •Core logic in Python Rust •EVM trace ingestion from open RPCs and node plugins •ML models (GNNs, tree ensembles) for scoring risk •Modular alert engine (dashboards, APIs, or integrations) 🔐 Coming next: OpenKYT We’re building OpenKYT, a hosted service built on top of OpenAML with extended features to fully meet KYT and due diligence requirements from regulators — including MiCA, the Genius Act, and other emerging AML frameworks globally. 🌍 OpenAML is now part of FINOS (Linux Foundation) — making it the first fully open-source blockchain AML tool backed by the open finance community. 🚀 Join us, contribute, and help shape the future of transparent crypto compliance #OpenAML #OpenKYT #CryptoCompliance #BlockchainAnalytics #AI #ZKP #MiCA #FINOS #LinuxFoundation #DTCC #EVM #DeFi #OpenSourceFinance