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The more data you have at hand, the better you will be to assess risk. At the same time, much of the necessary data is restricted - whether due to regulations or the sensitive nature of the data.
Decentriq helps you unlock and share these critical data assets with stakeholders through data clean rooms, completely within GDPR and other regulatory objectives.
Confidentially match insurance claims data to find potential duplicate claims, surfacing any "double dipping" fraud schemes.
Decentriq provides maximal flexibility and accuracy for matching logic from hard match to probabilistic matches.
Combine data across different jurisdictions and entities for risk and compliance reporting, all within regulatory guardrails.
Participating entities can submit data through the clean room and run aggregated statistics while complying with the regulation.
Easily share data with insurance aggregators to drive efficient and high converting customer acquisition initiatives.
With access to insurance aggregator data, insurers can identify underserved population and better inform their product strategy.
"Decentriq enables a whole new perspective on how we can innovate with data that wasn't possible before."
“Our partnership with Decentriq provides an innovative example of how businesses can better embed data protection into their application design and build cross-organizational data ecosystems."
"Decentriq’s confidential analytics platform offers the possibility to work together with other financial partners in a fast and easy way."
Swiss Re is one of the leading and largest re-insurance firms in the world. As a data-driven, risk knowledge company, Swiss Re constantly pursues initiatives to test new data driven technologies.
Watch on-demand for a webinar co-hosted between Swiss Re, Intel and Decentriq on how insurance institutions can unlock the value of private data.
You heard the term mutiple times already, but what exactly are data clean rooms? How do choose the right one for your organization?
Learn how data ecosystems allows companies to enhance the sophistication of their data-driven decision making and machine learning models.