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    Big Data and Artificial Intelligence in Digital Finance

    Increasing Personalization and Trust in Digital Finance using Big Data and AI

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    Contributor(s)
    Soldatos, John (editor)
    Kyriazis, Dimosthenis (editor)
    Language
    English
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    Abstract
    This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance.
    URI
    https://library.oapen.org/handle/20.500.12657/54429
    Keywords
    Artificial Intelligence and Big Data; Digital Finance; FinTech; Blockchain; RegTech
    DOI
    10.1007/978-3-030-94590-9
    ISBN
    9783030945909, 9783030945909
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2022
    Imprint
    Springer International Publishing
    Pages
    363
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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