One thing you have to understand as a blockchain developer: in the future you will face a lot of attacks in the decentralized ecosystem—smart contracts, DeFi products, exchanges, everything. In this video I show you how I built an AI-powered Web3 fraud detection tool that scans any EVM wallet address and gives you a risk scorecard. The goal is simple: identify suspicious transactions, abnormal behavior, and wallets that look like they’re trying to drain funds—then you can prevent those wallets from doing transactions inside your app. I walk you through the full product demo first: paste a wallet address, select a network (I test Ethereum and Polygon), click analyze, and the app pulls transaction history, balance, contract interactions, token transfers, gas usage, unique wallets interacted with, and graphs like spikes in activity and value distribution. After that, I explain the architecture: a Next.js frontend that calls an API route, which communicates with my Python AI service (ML model + feature extraction) and also stores data in Supabase so you can train further later. Finally, I show the exact setup—getting free API keys (RPC provider + Supabase), running the SQL schema, starting the Python backend, running the Next.js frontend, and validating results against a block explorer.