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Data privacy examples

IBM Big Data Hub

These are just some examples of how organizations support data privacy , the principle that people should have control of their personal data, including who can see it, who can collect it, and how it can be used. For example, they can decline to have their data shared with third parties while allowing the app to generate personalized offers.

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Examples of IBM assisting insurance companies in implementing generative AI-based solutions  

IBM Big Data Hub

IBM can help insurance companies insert generative AI into their business processes IBM is one of a few companies globally that can bring together the range of capabilities needed to completely transform the way insurance is marketed, sold, underwritten, serviced and paid for.

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Climate change examples

IBM Big Data Hub

Here are a few examples: Sea ice loss and melting ice sheets: Declining levels of Arctic sea ice threaten the habitats of species such as polar bears and walruses. Poor air quality from wildfire smoke can exacerbate asthma and heart disease, for example, while heat waves can cause heat exhaustion.

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Why Cyber Insurance is Essential in 2022

IT Governance

Organisations must always look for cost-effective ways to address the cyber security risks they face. With more than 1,200 publicly disclosed data breaches last year , and organisations spending almost £3 million on average responding to security incidents , effective risk management is a top priority. The benefits of cyber insurance.

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How Much Cyber Liability Insurance Do You Need?

IT Governance

Cyber liability insurance helps organisations cover the financial costs of a data breach. Without insurance, organisations spend £3.6 million on average recovering from security incidents. By purchasing cyber liability insurance, organisations gain the resources they need at a fraction of the cost.

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Is Artificial Intelligence relevant to insurance?

IBM Big Data Hub

In this first of two posts, I investigate the anatomy of artificial intelligence and its impact on insurance. Soon after, AI’s capabilities extended to Speech and Natural Language processing, such as with IBM Watson, and for Image Recognition, which is now ubiquitously used for unlocking phones and other biometric security.

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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. One example of this is facial recognition being used for the illegal tracking of people’s movement.