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Artificial Intelligence in Insurance - Thematic Research

Artificial Intelligence in Insurance - Thematic Research

Summary

Machine learning is an artificial intelligence (AI) technology which allows machines to learn by using algorithms to interpret data from connected ‘things’ to predict outcomes and learn from successes and failures.

There are many other AI technologies – from image recognition to natural language processing, gesture control, context awareness and predictive APIs – but machine learning is where most of the investment community’s funding has flowed in recent years. It is also the technology most likely to allow machines to ultimately surpass the intelligence levels of humans.

Many companies, like Alphabet, have already become ‘AI-first’ companies, with machine learning at their core. At the same time, many ML techniques are getting commoditized by being open sourced and pre-packaged into developer toolkits that anyone can use.

This report focuses on Artificial Intelligence in Insurance.

Scope

This report focuses on artificial intelligence in insurance.
-It discusses the importance and benefits of AI technologies in the insurance industry.
-It identifies the key technology and insurance leaders in this technology theme.

Reasons To Buy

The report focuses on understanding the impact of AI technology on the insurance industry.
-It highlights the key technology leaders in each of the ten key AI technologies.
-It identifies the key trends we expect to see in the AI sector over the next two to three years.
-It analyses the AI value chain across four key segments – gathering raw data, managing this data, using the data to allow AI systems to learn from it, and creating AI based applications and use cases.
-It provides an industry analysis, classifying it in terms of the speed of adoption – high adopters, mid adopters, and low adopters.
-It includes insurance case studies and recommendations for IT vendors to discuss the impact of AI technology on the industry.
-The report provides a technology briefing, to understand the history of machine learning and how deep learning works.