Three institutions.
Three pilot slots.
One question answered.
Can cross-institutional AML detection work without sharing a single byte of customer data? We have built the platform. Now we need three banks to prove it under regulatory supervision.
The Opportunity
The EU Anti-Money Laundering Regulation takes effect on 10 July 2027. For the first time, Article 75 creates a legal framework for cross-institutional AML information sharing. Every bank in Europe is evaluating how to comply.
ZQUAS has built the only platform that enables cross-institutional AML detection without sharing any customer data. Using Multi-Party Computation, banks jointly identify cross-bank laundering patterns while each institution's data remains entirely within its own infrastructure. No data extraction. No central database. No GDPR conflict.
The platform is built, tested, and stress-tested. 29 compliance policies across 9 regulatory domains. GPU-accelerated. Real-time. Cryptographically attestable. The Founding Partner Programme is how three institutions join first.
What Founding Partners Receive
The Pilot
The pilot validates one capability: cross-institutional AML detection using MPC federation, with real compliance policies, against realistic data, under regulatory observation.
Scope
| Dimension | Detail |
|---|---|
| Entities | Minimum 5,000 per institution, up to 500,000. Synthetic data with planted typologies, or representative data from the bank's existing monitoring system. |
| Policies | The full 29-policy compliance framework, adapted to each institution's regulatory perimeter if needed. |
| Federation | Direct peer-to-peer MPC rounds between all Founding Partners. End-to-end encrypted (AES-256-GCM), authenticated (Ed25519), with cryptographic attestation on every verdict. |
| Detection targets | Four planted laundering typologies: trade-based money laundering, wire stripping, shell company layering, and funnel account structuring. Plus a legitimate control population. Measured outputs: detection rate, false positive rate, federation time. |
Timeline: 12 weeks from signature to results
Legal agreements signed. ZQUAS installation deployed. Data feed configured. Integration engineer assigned on-site or remote.
Single-institution policy evaluation. Verify 29 policies produce correct results against the bank's entity data. Calibrate thresholds to the bank's risk appetite.
Cross-institutional MPC rounds between Founding Partners. Bilateral detection. Measure results. Resolve any integration issues.
Compile detection results. Benchmark report. Case study draft. Regulatory sandbox submission prepared.
Joint presentation to regulator. Demonstrate detection. Discuss GDPR position on MPC protocol messages. Obtain regulatory feedback.
What the bank needs to provide
| Requirement | Detail |
|---|---|
| Entity risk data | Entity identifier plus risk score per entity. Synthetic or representative. No raw transaction data required. |
| Infrastructure | One server or VM for the ZQUAS installation. Minimum: 16 GB RAM, 4 cores. GPU optional (CPU fallback available). One outbound TCP connection to peer banks. |
| People | One IT contact for deployment. One compliance contact for policy calibration and results review. Part-time commitment, not full-time. |
| Regulatory relationship | Willingness to participate in a joint sandbox submission to the relevant regulator (DNB, FCA, or national FSA). |
| Legal | Standard NDA plus pilot agreement. ZQUAS provides templates. Typical legal review: 2 to 4 weeks. No procurement RFP required. |
Why Now
AMLR applies directly from 10 July 2027. Unlike previous directives, there is no national transposition period. The regulation is directly applicable in all EU member states from that date. AMLA begins direct supervision of high-risk cross-border institutions in 2028.
Banks that can demonstrate cross-institutional detection capability before July 2027 are ahead of the compliance curve. Banks that cannot will be responding to regulatory requests after the deadline.
Cross-institutional detection is a network. It becomes more valuable with each institution that joins. The first three banks to deploy MPC federation establish the network. Every subsequent bank that joins benefits from the detection capability the founders built. Founding Partners are not just early customers. They are the initial participants in a network that will ultimately span the entire banking sector.
The window in which Founding Partners can shape the platform, the policies, and the market is measured in months, not years.
About ZQUAS
ZQUAS is a GPU-native governance and financial crime compliance platform. The core engine executes deterministic compliance policies in real time on GPU, producing cryptographically attestable decisions for each verdict. Each decision produces a cryptographic proof bundle: a record of the policy that fired, the inputs it read, and the verdict it produced. A regulator can verify this independently, years after the fact.
The federation layer enables privacy-preserving cross-institutional detection using Multi-Party Computation: ECDH-PSI for entity matching, Yao's Garbled Circuits for risk comparison, and Oblivious Transfer for secure key exchange. Banks detect cross-bank laundering patterns without any institution accessing any other institution's data.
Key metrics (measured, not projected)
Leadership
Founded by Danny de Gier. 18+ years of financial crime compliance across Deutsche Bank, HSBC, RBS, and ABN AMRO. ICA Postgraduate Diploma in Financial Crime (University of Manchester). Former CRO at Vivid Money. Direct regulatory experience across FCA, DNB, BaFin, and ECB supervised institutions.
Next Steps
Three slots. When they are committed, the programme closes.
Ready to discuss?
Contact us directly. No sales process. A conversation with the founder.
[email protected]