Bridgewatch
Cross-Chain Compliance Intelligence
About
The problem it solves **The Gap**: $15.8B reached sanctioned entities last year. Current tools catch known bad actors — but when a fresh wallet bridges funds from China's digital yuan system, Chainalysis sees nothing unusual. The origin is invisible. **Our Solution**: First compliance tool that detects behavior, not just addresses. We apply NLP to transaction calldata — like detecting an accent even when you've never heard the speaker before. How it works: Layer 1: On-chain OFAC screening (Merkle proofs, trustless) Layer 2: ML flags suspicious patterns — institutional timing, gas behavior, origin obfuscation **Why now**: GENIUS Act (July 2025) mandates stablecoin compliance. $312B market needs this. Use of AI tools and agents Core ML Pipeline: NLP Feature Extraction — Analyzes transaction calldata patterns using text analysis techniques adapted for blockchain data Explainability Layer — Shows which features drove each risk decision Development: Cursor and Claude assisted with code implementation and debugging throughout the hackathon.
How it's made
Challenges we ran into **1. False Positive Calibration** The bug: First live test against Ethereum mainnet — 19 out of 20 transactions flagged as suspicious. What happened: Unknown function selectors were scoring +15 points automatically. The Across Protocol relayer function executeRelayerRefundLeaf wasn't in our whitelist, so every legitimate relayer transaction got flagged. The fix: Realized this is actually correct behavior for a compliance system — flag unknown patterns, build whitelist over time. Added Across selectors to known list. Understood the difference between "false positive" and "conservative by design." **2. The Cold Start Problem** The challenge: ML needs training data. But there's no public dataset of "CBDC-origin transactions" — that's literally the gap we're filling. The solution: Generated synthetic transactions mimicking mBridge patterns based on public documentation. Combined with real bridge traffic labeled as "normal." Not perfect, but enough to prove the concept works.