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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. The risk of privacy leakage from interaction with AI technologies is a major source of consumer concern and mistrust.

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Perplexing Impacts of AI on The Future Insurance Claims

Smart Data Collective

We previously talked about the benefits of data analytics in the insurance industry. billion from the insurance industry. However, major advances in AI have arguably affected the insurance industry even more. They interact with AI features on their phone or when using a service, so their expectations are ever-increasing.

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Machine Learning Transforms Life Insurance Beyond the Actuarial Process

Smart Data Collective

The insurance industry is among those that has found new opportunities to take advantage of machine learning technology. Life insurance companies in particular are discovering the wondrous opportunities that AI provides, since this sector faces some unique challenges relative to other insurance offerings.

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Insurance Dashboard Design: KPIs, Analytics & Examples

FineReport

Insurance companies provide risk management in the form of insurance contracts. Industry-specific, comprehensive, and reliable data management and presentation have become an issue of increasing concern in the insurance industry. The insurance dashboard is one of the most commonly used data display methods.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

I am the Chief Practice Officer for Insurance, Healthcare, and Hi-Tech verticals at Fractal. The Insurance practice is currently engaged with several top 10 P&C insurers in the US, across the Insurance value chain through AI, Engineering, Design & Behavioural Sciences programs.

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The Sprint towards Digital Healthcare

Cloudera

As healthcare providers and insurers /payers worked through mass amounts of new data, our health insurance practice was there to help. One of our insurer customers in Africa collected and analyzed data on our platform to quickly focus on their members that were at a higher risk of serious illness from a COVID infection.

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Conversational AI use cases for enterprises

IBM Big Data Hub

The emergence of NLG has dramatically improved the quality of automated customer service tools, making interactions more pleasant for users, and reducing reliance on human agents for routine inquiries. These technologies enable systems to interact, learn from interactions, adapt and become more efficient. billion by 2030.