The complete 2026 product guide to ChainAware.ai – covering every tool in the suite: Fraud Detector, Rug Pull Detector V3, AML Monitoring Agent, Wallet Auditor, Wallet Rank, Credit Score, Token Rank, and Behavioral User Analytics. Powered by 20M+ wallet profiles across 8 blockchains. Start here if you’re new to ChainAware.
MiCA is live and most DeFi protocols are being quoted $100K-$500K+/year for compliance tools built for banks. This guide explains what MiCA actually requires from DeFi protocols, what it doesn’t, and how ChainAware delivers 70-75% MiCA coverage at 1% of the cost of Chainalysis – pay-per-use, no annual contract.
Crypto fraud hit $158B in illicit volume in 2025. Rule-based detection systems produce 30-70% false positive rates and are bypassed by fraudsters within days. This 2026 guide covers how machine learning replaces static rules for crypto security – and how ChainAware’s ML models achieve 98% fraud prediction accuracy across 20M+ wallets on 8 blockchains.
DeFi lending is stuck at overcollateralization because protocols can’t assess borrower creditworthiness. This complete guide covers how ChainAware Credit Score – built on Wallet Auditor, Fraud Detector, and Cash Flow Analysis – enables undercollateralized lending, DAO treasury credit, and smarter user acquisition in 2026.
How to use ChainAware as a Web3 business – four products covering growth, analytics, compliance, and AI agent integration. Covers Growth Agents for 1:1 wallet outreach, Behavioral Analytics via GTM pixel, Transaction Monitoring and AML, and Prediction MCP for any LLM. All built on 20M+ wallet profiles across 8 blockchains.
SmartCredit.io achieved 8x engagement and 2x primary conversions in 6 months using ChainAware Web3 Growth Agents and Behavioral Analytics. This case study covers the challenge (low connect-to-transact conversion, no user insight), the solution (behavioral segmentation + personalized wallet messaging), and the full results.
Every wallet’s transaction record is a complete financial decision log – the richest behavioral dataset in marketing history. This guide covers how behavioral user segmentation works in Web3, why on-chain data outperforms cookies and surveys, and how DApp teams use ChainAware’s 20M+ wallet profiles to target, convert, and retain the right users.
AML is rules-based and tracks the flow of bad funds. Transaction monitoring is AI-based and predicts future fraud from behavioral patterns. This guide – based on X Space #33 with ChainAware co-founders Martin and Tarmo – covers how to integrate both into any DApp, why you need both, and what 98% prediction accuracy actually means in practice.
Which AI can every DApp actually integrate via API continuously, with measurable accuracy? Based on X Space #32 with ChainAware co-founders Martin and Tarmo. The core framework: generative AI is a one-time tool used by humans; predictive AI runs continuously in the background – and it’s predictive AI that delivers real, measurable Web3 use cases.
X Space #31 recap: how predictive AI builds trust, accelerates growth, and improves user experience in Web3. ChainAware co-founders Martin and Tarmo cover Fraud Detector, AML Scorer, Growth Agents, Prediction MCP, and the onboarding-router – practical AI already running across 8 blockchains and 20M+ wallet profiles.