Duality empowers organizations in regulated industries and the public sector to collaborate on sensitive data in a privacy-compliant manner.

Selected use cases

Eliminate fraud in the trade finance data ecosystem by enabling multiple lenders to be alerted when a funding request has already been financed – without revealing the identity of the entity making the request.

AML

Securely collaborate across institutions, business lines, and borders to speed processes, cut false positives, reduce operational costs, mitigate risk, and prevent financial crimes.
Securely collaborate across institutions, business lines, and borders to better detect, prevent, and investigate fraud.
Accelerate multi-center Real World Evidence (RWE) studies while ensuring privacy, confidentiality & regulatory compliance.
Accelerate multi-center Real World Evidence (RWE) studies while ensuring privacy, confidentiality & regulatory compliance.
Accelerate multi-center Real World Evidence (RWE) studies while ensuring privacy, confidentiality & regulatory compliance.
Accelerate multi-center Real World Evidence (RWE) studies while ensuring privacy, confidentiality & regulatory compliance.
Collaborate with teams, customers, vendors, & partners across borders by jointly analyzing sensitive data -- while also meeting data privacy & data residency requirements.

Cloud
Migration

Securely migrate your AI workloads to the cloud: your own, your partner’s, or any third-party cloud.

Industries

Financial Services

Healthcare

Insurance

Government

We connect innovators
Join one of these privacy-protected collaboration projects

List Sharing and
Comparison with IBM
Payments Center Canada

Collaborate with IBM Payments Center Canada customers to prevent fraud and validate identities during onboarding, loan origination, and more

Learn more

Experience secure collaborative computing today.

Maximize the value of sensitive, regulated, or confidential data.

Experience secure collaborative computing today.
Maximize the value of sensitive, regulated, or confidential data.