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. ---------------------------------------------------------------------------------------- Note: this post has been updated, to reflect the progress of new technologies and business moves by companies mentioned in the article. ----------------------------------------------------------------------------------------
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How Machine Learning and Hyperspectral Imaging Contribute to Sustainable Agriculture and Environmental Protection
Michał Marcinkiewicz
Sustainable agriculture is a simple and pragmatic idea that there does not have to be a trade-off between agricultural productivity and environmental protection. The conventional approach, however, has been to view agricultural yields and environmental protection as some sort of a zero-sum game in which the latter was constantly out-prioritised by the former. As a result, we have only managed to exacerbate the already adverse environmental impact of agriculture in terms of groundwater depletion, water pollution from fertilizer runoffs, biodiversity loss, soil erosion, and high rates of greenhouse gas emissions.
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Fraud Detection with Machine Learning: How Banks and Financial Institutions Leverage AI
Michał Dyzma
The financial services sector is on the eve of a major transformation, and the driving force behind it is AI. Innovative applications for AI have already been found across areas such as credit scoring, regulatory compliance, customer experience, and portfolio management. Thanks to rapid advancements in technology, tasks that once took employees hours to complete manually, can now be done in a matter of seconds.
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Audio classification is one of the most common and most explored tasks in the field of audio processing. It’s the foundation of many apps that enable users to automatically identify artists, instruments, or simply recognize someone’s voice. How does the algorithm work in practice? We had a chance to implement audio processing with machine learning on iOS and Android mobile devices. See how our journey went.
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Increasing the Accuracy of the Machine Learning Model in the CarLens Mobile App
Anna-Mariia Shkarlinska
25,000 analyzed photos, 10 more weeks, 6 new Machine Learning models, and even more hard work of development teams, topped with AR engines and Machine Learning technology. That's our recipe for improving our Machine Learning model! Welcome to the second part of our journey with CarLens, where we are going through all the methods that we used for improving our Machine Learning model. How did the model’s performance change? Read on to see our results with detailed charts. In case you missed the first one, make sure you read this article too.
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Have you ever wondered what steps you should take first to dive deep into the Machine Learning world? You can consider various scenarios – including academia classes, online tutorials, and reading books. All of these approaches have their special benefits and may help you become an AI Engineer. On the other hand, it is often said that the learning process is at its best when done hands-on.
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I have recently stumbled upon an article reporting that Facebook is using Artificial Intelligence to fight “revenge porn” - a phenomenon of sharing intimate photos on social networks without people’s permission. This is one of the downsides of the development of new technologies and social networks. This great TED talk from one of the victims explains why it is a serious problem that needs strong measures.
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Machine Learning Automation – What Kind of Tasks Can Be Improved and Automated in the Near Future
Mateusz Hapon
Machine learning brings a wide range of new possibilities to nearly all industries. A number of tasks that can be automated or improved with machine learning-based algorithms is growing day to day. Let’s dive deeper and try to establish factors that enable a specific task to be improved with ML, so we can understand better how your business and processes can leverage artificial intelligence.
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AI-driven UX design is one of the hottest trends for 2019. That said, product designers are not among the jobs and specializations expected to be replaced by robots soon. After all, coming up with the right product idea and executing it is a complex process that combines creativity and advanced analytics.
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AI and Machine Learning in Fintech. Five Areas Which Artificial Intelligence Will Change For Good [Updated]
Timothy Clayton
AI and machine learning have moved from the fringes to the very centre of the technology debate. But which industry has the huge datasets, along with the capital and staffing resources, to make the most of AI? As recently reported by both PwC and Gartner, it is the financial sector.
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