Banks aspire to understand their customers more completely - through analytics of transactions and trading activities - to make sure that they are delivering the right services. However this data is distributed across various silos - devices, departments. Connecting and exploring these sources is not easy, due to concerns about privacy and safety


Fluid’s Confidential Data Exploration tools enable data-science teams to connect and explore data across sources in a confidential manner - remotely/on the cloud. This empowers them to learn from data faster, while respecting and preserving complete privacy.


  • More business - Faster data-product launches

  • Future-proofing safety - Prevent loss of effort and brand-equity

Co-operative risk & fraud management


Banks are increasingly using data and AI to profile risk and prevent fraud. In a real-world scenario, data-science teams need to check suspicious patterns across various data-sets, departments and sometimes, across competing companies. This co-operation can enable them to mitigate fraud closest to its source. The challenge - risking exposure of such sensitive data and IP on the cloud when the computation happens.


Fluid’s Confidential Deployment tools secure the deployment in a confidential computing sandbox - thereby enabling operators to group their intelligence without exposing any data or computation.


  • Better models : more accurate intelligence about fraud

  • Future-proofing safety : Reduced liability of data exposure internally and externally

Use cases of FLUID

Our solutions empowers data driven teams across industries to unlock valuable insights from sensitive data while preserving data and building trust.

Take a step towards safer AI

Are you managing data and AI on the cloud? Is your team trying to understand data privacy and protection better?