From Shared Ledgers to Shared Judgment
A privacy-preserving coordination layer that turns isolated institutional judgments into shared probabilistic awareness — without sharing customer data.
The Problem
Financial crime detection fails not because institutions lack analytics, but because each institution observes only a partial transaction graph. Risk becomes visible only when signals are combined — yet combination is exactly what regulation prohibits.
Regulatory Boundaries
Privacy regulations prevent sharing customer information across institutions, even to prevent fraud.
Coordination Failure
Fraudsters exploit siloed systems, cycling through institutions without detection. Each bank decides alone with incomplete information.
Delayed Response
Traditional AML systems require days or weeks to identify patterns. Damage occurs before detection.
Operational Inefficiency
95% false positive rates consume compliance resources reviewing legitimate transactions.
The Solution
Probabilistic risk signaling — a coordination primitive where institutions share calibrated confidence, not data.
Confidence Signals, Not Data
Institutions submit structured belief commitments without exposing transaction data or customer information. Canton's selective disclosure preserves privacy.
Weighted Risk Aggregation
Signals are weighted by reputation and aggregated into a shared risk score. Collective inference emerges without any party learning another's private reasoning.
Regulator Observer Mode
Read-only audit trail for regulators. Complete decision rationale with immutable records and automated SAR filing capabilities.
Network Effects
Detection accuracy improves with each participating institution. Collective intelligence compounds over time.
How It Works
Four-step process from detection to network learning
Signal Submission
Institution detects suspicious activity and submits a confidence signal to the network.
Belief Collection
Participating institutions independently assess risk and submit weighted belief commitments.
Risk Aggregation
Canton aggregates signals with privacy boundaries. Shared risk score emerges — no data disclosed.
Continuous Learning
Outcomes verified. Accurate signalers gain reputation weight. Network intelligence improves.
Privacy Architecture
Designed for institutional trust from the ground up
AML Prediction Network
From Shared Ledgers to Shared Judgment