Finance
Banks, fintechs and payment networks need to share signals to fight fraud and manage risk — but can rarely share the underlying customer data itself. PryvX applies federated learning, secure multi-party computation and confidential computing so financial institutions can collaborate on fraud detection, risk scoring and compliance, with no raw data ever leaving its source.
Cross-Institution Fraud Detection
Correlate fraud signals across banks and payment networks without any institution exposing its customer data to another.
Joint Risk Scoring
Compute shared risk models across institutions using secure multi-party computation — no raw financial records change hands.
Compliance-Ready by Design
GDPR, DORA and financial-sector regulation are enforced structurally, since sensitive data never leaves its origin.
How It Works
From separate signals to one shared verdict, in four steps
Contribute Signals
Each institution connects its own fraud/risk signals — without centralizing the underlying data.
Compute Jointly
Federated learning or secure multi-party computation runs the joint model across all contributors at once.
Score the Risk
A shared risk or fraud score is produced from the joint computation.
Return the Verdict
Only the verdict returns to each institution — never another institution's raw customer data.
Where It's Used
Anywhere financial institutions need to collaborate without sharing customer data
Payment Fraud Prevention
Detect coordinated fraud rings across card networks and payment processors without sharing cardholder data.
Credit Risk & Underwriting
Pool risk signals across lenders for better underwriting decisions without pooling customer records.
Regulatory Compliance & Reporting
Produce compliance-ready aggregate reporting across institutions without any raw data leaving its source.