Built on Canton Network

Privacy-Preserving Fraud Detection for Financial Institutions

Reduce false positives by 30% and detect fraud in real-time through collaborative intelligence—without sharing customer data.

AML Prediction Network Dashboard
$180B
Annual Industry Compliance Cost
95%
Current False Positive Rate
30%
Target Reduction
Real-time
Detection Speed

The Challenge

Financial institutions face a critical dilemma: they cannot share customer data to catch fraudsters, yet siloed systems allow criminals to cycle through banks undetected.

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Data Privacy Constraints

Bank Secrecy Act and GDPR prevent sharing customer information, even to prevent fraud across institutions.

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Repeat Offender Blindspot

Fraudsters exploit siloed systems, moving from institution to institution without detection.

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Delayed Response

Traditional AML systems require days or weeks to identify patterns. Damage occurs before detection.

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Operational Inefficiency

95% false positive rates consume compliance resources reviewing legitimate transactions.

Our Solution

Collaborative fraud detection using prediction markets on Canton Network—share intelligence, not customer data.

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Pattern Sharing, Not Data Sharing

Share fraud patterns and behavioral signatures without exposing PII. Canton's selective disclosure ensures regulatory compliance.

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Prediction Market Intelligence

Institutions stake predictions on transaction risk. Weighted scores aggregate network intelligence for accurate, real-time decisions.

Built-in Compliance

BSA Section 314(b) compliant framework with immutable audit trails and automated SAR filing capabilities.

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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

1

Pattern Detection

Institution detects suspicious activity and submits anonymized pattern to network.

2

Market Formation

Prediction market opens. Participating institutions assess and stake on fraud probability.

3

Risk Aggregation

Weighted risk score calculated. Automated actions triggered based on configurable thresholds.

4

Continuous Learning

Outcomes verified. Accurate predictors rewarded. Network intelligence improves.

Regulatory Compliance

Designed for compliance from the ground up

BSA Section 314(b) Pre-approved information sharing framework
GDPR Compliant No personal data leaves institution
Immutable Audit Trail Complete decision rationale for regulators
Auto-SAR Filing Threshold-based automated reporting
Regulator Observer Nodes Real-time supervision capability
KYC Utility Ready Optional shared verification with consent

See the Platform in Action

Schedule a demonstration to see how AML Prediction Network can reduce your false positive rate while improving detection accuracy.

Request Demo →