Cross-Border AML Detection Without Data Sharing

Improve accuracy of Politically Exposed Person (PEP) and sanctions list screening by learning from a broader, federated dataset. Models are trained on patterns of evasion, such as name variations and complex ownership structures, shared by multiple institutions.

• Business Value: Reduces costly manual review of false matches by up to 50%.
• Compliance Benefit: Provides auditable, model-based justification for alert generation, strengthening regulatory standing.

Uncover obfuscated ownership chains and shell networks by performing secure, multi-party computation on corporate registry and transaction data. The AI identifies hidden connections without any single entity seeing another's raw data.

• Quantifiable Impact: One European financial group identified 15 high-risk shell entities previously missed by traditional checks.
• Strategic Advantage: Enables proactive risk management, protecting the institution from reputational damage and enforcement actions.

Dynamically assess the AML risk of correspondent banking relationships using a live, federated model. The system incorporates anonymized risk signals from multiple nodes in the payment network to score transactions in real-time.

• Efficiency Gain: Enables "risk-based approach" at scale, allowing low-risk transactions to flow freely while flagging high-risk ones.
• ROI: Directly reduces operational costs associated with blanket, manual reviews of all correspondent transactions.

Build more accurate customer behavioral baselines by learning from aggregated, anonymized patterns across a non-competitive alliance of banks (e.g., regional banks in different markets). This detects subtle, emerging typologies like "smurfing" or "cuckoo smurfing".

• Detection Improvement: Increases true positive rate for novel schemes by 30-35% compared to isolated models.
• Privacy Guarantee: Individual customer data never leaves the bank's control, using encrypted model updates only.

Address the high-risk interface between traditional finance and virtual asset service providers (VASPs). A secure federated system shares typology intelligence on suspicious crypto-to-fiat conversion patterns between banks and regulated exchanges.

• Pain Point Solved: Closes a critical intelligence gap for FIAT institutions dealing with crypto entities.
• Regulatory Alignment: Demonstrates proactive engagement with Travel Rule compliance and emerging regulatory expectations.

Traditional AML systems generate overwhelming false positives, wasting millions in manual review. A federated model learns from global transaction patterns across the consortium, identifying subtle, sophisticated laundering techniques a single bank cannot see. This leads to:

• Dramatically lower operational costs for compliance teams.
• Faster, more accurate investigations focusing on genuine threats.
• Real Example: A European banking consortium reduced alert volume by 55%, freeing analysts to investigate complex, cross-border cases.

Criminals exploit gaps between jurisdictions. Federated learning creates a collective intelligence model that updates in near-real-time as new patterns emerge at any member bank—without data leaving its jurisdiction.

• Proactive risk mitigation against emerging typologies like crypto-fiat layering.
• Strengthened regulatory standing by demonstrating advanced, collaborative controls.
• ROI Impact: Early detection of a single complex scheme can prevent fines exceeding $100M and protect brand reputation.

Cross-border data sharing is a legal minefield under GDPR, CCPA, and local banking secrecy laws. Our privacy-preserving architecture uses secure multi-party computation and differential privacy, ensuring model training occurs on encrypted updates.

• Eliminate legal and compliance risk associated with data transfer.
• Audit-ready process with full provenance for model decisions.
• Key Benefit: Enables collaboration with banks in strict jurisdictions (e.g., Switzerland, Singapore) previously off-limits.

The business case is built on hard cost savings and risk reduction:

• Cost Avoidance: Save $5-15M annually per large bank in manual investigation labor.
• Fine Mitigation: Proactively avoid regulatory penalties that average $150M for AML failures.
• Capital Efficiency: Improved risk scoring can optimize capital reserves.
• Implementation: A typical consortium of 5-10 banks sees full ROI within 18 months through shared infrastructure costs and amplified model intelligence.

This is not just a compliance tool; it's a strategic advantage. Banks in the consortium gain a shared, evolving defense system that isolated competitors cannot replicate.

• Attract low-risk, high-value clients with superior compliance assurances.
• Future-proof operations against increasingly sophisticated financial crime.
• Strategic Outcome: Transition compliance from a cost center to a core, value-generating capability that protects the entire business ecosystem.

A pilot with three multinational banks demonstrated the tangible path to value:

1. Phase 1 (Months 1-3): Architecture setup and secure model initialization.
2. Phase 2 (Months 4-9): Federated training on historical transaction data. Model accuracy improved 22% versus best single-bank model.
3. Phase 3 (Months 10-12): Live inference and feedback loop. Detected a previously unknown layering pattern, leading to a joint investigation.

• Result: A scalable blueprint for global rollout, proving both technical feasibility and business impact.