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The insurance industry has always relied on data to calculate risk and come up with personalized ratings. Today, the sector is undergoing a profound digital transformation thanks to technologies such as machine learning.  Insurers are using machine learning to increase their operational efficiency, boost customer service, and even detect fraud. Here are 6 ways machine learning is transforming the insurance industry.
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A well-organized supply chain has always been a powerful source of competitive advantage. It's even more so in today's interconnected economy. With the development of Artificial Intelligence (AI) solutions, logistics departments can solve complex optimization problems, cutting forecasting errors by half and reducing losses in sales caused by product unavailability by up to 65%, according to a recent paper by McKinsey Global Institute.
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In healthcare, getting the diagnosis right – and getting it in time – is key to any successful treatment. It’s a matter of utmost importance, as revealed in the research on postmortem examinations: diagnostic errors are responsible for about 10% of patient deaths in total. The study also suggests that these errors contribute to 6-17% of all adverse events in hospitals.
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The life of a sales rep? Lead generation, profiling and scoring - sales representatives spend the majority of their time on repetitive and time-consuming tasks. Time, in a way, wasted. Instead, they should be focusing on building genuine relationships with prospects and cutting deals.
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Skin cancer is the most common form of malignancy that affects human populations all over the world. In the US, more people are diagnosed with skin cancer each year than with all other cancer types combined. The mortality rates from melanoma skin cancer have risen by 156% in the UK.
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AI and the big data revolution have already made a huge impact in sectors such as retail, healthcare, education, and autonomous driving. Now, manufacturing is experiencing rapid transformation thanks to a combination of AI and advanced technologies like the Internet of Things.
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Machine learning is one of the hottest topics in the world of technology. It makes sense then that so many companies are analysing whether they can benefit from it. Should you decide to enhance your business with ML, the question of finding a suitable team will arise immediately. Which is better: building an in-house machine learning team or outsourcing your needs to an industry specialist? You will need to think about many aspects before making a commitment to either option. Read on to see our take on whether you should go for an in-house or an outsourced ML team.
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Chatbots, from smart home assistants to automatic bank assistants, have become a very common way to quickly respond to users’ questions and needs. Their main benefit is that they help businesses scale effectively and support customer service with no harm to the user experience. Implementing a chatbot into your customer communication strategy can save you time and money. They have some limitations too - most of which result from the fact that they simply are not human. Let’s look at DialogFlow - a great solution for all your chatbot needs.
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It’s no secret that big data and advanced analytics are changing the face of the business world. Insights gained from big data are revolutionising foundational business processes such as sales and marketing, supply chain management, and R&D; giving companies the information they need to increase revenues, reduce costs, and improve customer experience.
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The telecommunications industry is riding high on the waves of the tech revolution and digital transformation. Their business is doing so well that, unlike in many other sectors, Artificial Intelligence (AI) and Machine Learning (ML) is more of a solution to their problems than a challenge. Telecoms are not far behind such big tech companies as Google, Amazon, or Microsoft and could use the 5G transition as an opportunity to cut the distance.
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