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How Data Science Experience improves accuracy for the insurance industry

IBM Big Data Hub

In this Q&A, IBM financial services solution architect Irina Saburova discusses an insurance use case with IBM Data Science Marketing Lead Rosie Pongracz.

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Private UK health data donated for medical research shared with insurance companies

The Guardian Data Protection

Observer investigation reveals UK Biobank opened its biomedical database to insurance firms despite pledge it would not do so Sensitive health information donated for medical research by half a million UK citizens has been shared with insurance companies despite a pledge that it would not be. Continue reading.

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The risks and limitations of AI in insurance

IBM Big Data Hub

In my previous post , I described the different capabilities of both discriminative and generative AI, and sketched a world of opportunities where AI changes the way that insurers and insured would interact. Usage risk—inaccuracy The performance of an AI system heavily depends on the data from which it learns.

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Ping An casts a long digital shadow over Asia's insurers

Information Management Resources

The insurer's strategy has been to incubate tech businesses and then spin them off, while incorporating the technology in its main operations.

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Don’t Follow the Money; Follow the Customer!

Bill Schmarzo - Dell EMC

If you complete the full Fluvastatin prescription, then we’ll reduce your monthly healthcare insurance payment by 5%.”. Check out “ The New Normal: Big Data Business Model Disintermediation and Disruption ” for more details on business model disruption and customer disintermediation. Figure 4: Optimizing the Customer Lifecycle.

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The Third Modern Data Management Summit: Making Data Work!

Reltio

This year the event offered three tracks – Modern Data Management, Personalize Customer 360 , and Healthcare & Life Sciences. This year’s theme was “ Organize Master Data. Keynote sessions included: .

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Trustworthy AI helps provide equitable preventative care for diabetics

IBM Big Data Hub

And with good reason: it’s a lot of data to process effectively, and not all AI systems are created with the proper ethical guardrails in place. Organizations need to be able to trust their data science outcomes. Here’s how that North American healthcare company achieved its goals using data fabric.

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