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12 Use Cases of AI and Machine Learning In Finance

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There's no doubt that the finance industry is undergoing a transformational change. The recent years have seen a rapid acceleration in the pace of disruptive technologies such as AI and Machine Learning in Finance due to improved software and hardware. The finance sector, specifically, has seen a steep rise in the use cases of machine learning applications to advance better outcomes for both consumers and businesses. Until recently, only the hedge funds were the primary users of AI and ML in Finance, but the last few years have seen the applications of ML spreading to various other areas, including banks, fintech, regulators, and insurance firms, to name a few. Right from speeding up the underwriting process, portfolio composition and optimization, model validation, Robo-advising, market impact analysis, to offering alternative credit reporting methods, the different use cases of AI and Machine Learning In Finance are having a significant impact on this sector.


Top 50 Use Cases of Artificial Intelligence in Diverse Sectors

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The digital sphere is raining technologies. The influence of artificial intelligence is taking center stage with every possible improvement. Technology is changing almost all industries including banking and finance, healthcare, automobile, telecommunication, manufacturing, defense and military, entertainment and media, education, etc. The sub-domains of Artificial Intelligence such as machine learning, natural language processing, data analytics, and image analytics are also rolling out profitable use cases in diverse sectors. Besides, artificial intelligence is serving the business purpose by leveraging end-to-end automation processes. Therefore, Analytics Insight has listed the top 50 business use cases of artificial intelligence in diverse sectors. Predictive analytics is a gift to healthcare. Sometimes, we come across patients who say they underwent an unnecessary surgery due to a lack of predictions on what was coming. Fortunately, artificial intelligence is changing the fate of such burdensome risks and avoidable surgeries.


51 Artificial Intelligence (AI) Predictions For 2018

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It is somewhat safe to predict that AI will continue to be at the top of the hype cycle in 2018. But the following 51 predictions also envision it becoming more practical and useful, automating some jobs and augmenting many others, combining machine learning and big data for fresh insights, with chatbots proliferating in the enterprise.


Is Machine Learning Inevitable for Data Analytics?

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One of the most watched developments in data analytics and business technology these days is that of machine learning. Becoming something of a business-critical technology, machine learning makes computing processes more efficient, cost-effective, and reliable and may ultimately accelerate every aspect of business decision-making. Machine learning has applications in most industries, where it presents a great opportunity to improve upon existing processes. Yet many organizations are slow on the uptake. Recent surveys report that fewer than 25% of businesses have adopted any significant level of machine learning automation; yet it is currently behind some of the most game-changing advancements at Google, PayPal, Netflix, and other industry giants.


What is the Difference Between AI and Machine Learning in Healthcare?

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Artificial intelligence and machine learning have permeated many aspects of modern life, from entertainment to supply chain logistics, financial services, retail, and beyond. The healthcare industry is no exception. Spending on artificial intelligence in healthcare is projected to increase at a rate of 48% annually through 2023. This surge in spending makes sense, given the widespread digitization of healthcare-related data and information. What is not so clear in discussions about the future of healthcare is the difference between artificial intelligence (AI) and machine learning (ML) and their specific roles in the industry.