Members submit approved data views
Each institution contributes only the fields and time windows it has agreed to use for the approved purpose.
Secure collaboration infrastructure
Work together on private data.
Detect signals no member can see alone across AML, fraud, credit risk, cyber risk, and benchmarking, while each institution keeps sensitive records, models, and internal context protected.
The collaboration gap
Institutions need shared signals, without shared data.
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Signals cross boundaries
Risk patterns move across institutions, products, and jurisdictions.
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Each member sees a fragment
Important context is split across organizations that cannot simply pool data.
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Collaboration needs protection
Members can learn more together only when records, models, and internal context stay protected.
Where collaboration matters
An example: AML
Joint analysis runs under agreed rules
Approved AML analytics run in a protected environment where participants can verify what is being run and which rules apply.
Only approved results leave the platform
Outputs can be limited, logged, and reviewed so the process stays aligned with the members' agreed purpose.
Example shared signals
Start with the signals that become stronger across members.
- Repeated entity behavior across members
- Unusual transaction pattern overlap
- Shared device, account, or identity signals
- Counterparty and exposure concentration
- Cyber indicators seen across institutions
- Aggregated operating and risk benchmarks
Beyond AML
One network for institutional collaboration.
Fraud intelligence
Identify repeat actors, staged claims, and coordinated behavior across member boundaries.
Credit risk signals
Compare exposure, repayment, and concentration patterns without exposing member-level portfolios.
Cyber risk collaboration
Share signals about attack infrastructure without disclosing internal incidents or response posture.
Regulated benchmarking
Compute aggregated operating or risk metrics with controls that avoid member-level exposure.
Private and verifiable
Trust the process without trusting each other.
Or us.
Sensitive data is used only for approved analysis. Participants can verify what will run before data is processed, keep raw records and models protected, limit what results are released, and review evidence of what happened after each run.
Private inputs
Raw datasets, models, and internal investigations are not pooled or exposed to other members.
Verifiable process
Participants can confirm the approved analysis and rules before sensitive data is used.
Controlled outputs
Results are limited to the insight level members agreed to, reducing the risk of exposing member-specific information.
Audit trail
Each run creates evidence that risk, security, legal, and compliance teams can inspect later.