Remove Big data Remove Data science Remove Insurance Remove Security
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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.

Insurance 107
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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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The Role of Master Data Management in Enabling Omnichannel Connected Customer Experiences

Reltio

Now Tech: Customer Data Management Solutions, Q1 2020, by Noel Yuhanna, Forrester Research, Inc., Our innovative customers span different industries like life sciences, financial services and insurance, healthcare, CPG, apparel, retail, travel and hospitality and high tech. Jan 8, 2020. But, they all have one thing in common.

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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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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. An AI system, especially one with such an impact on healthcare, must be fair, explainable, secure and transparent.

Risk 52
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Generative AI use cases for the enterprise

IBM Big Data Hub

Key considerations: Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing. Teamwork: Assemble a team with expertise in AI, data science and your industry. Data: High-quality, relevant data is the fuel that powers generative AI success.

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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.