Fintech with artificial intelligence, big data, and blockchain /

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Bibliographic Details
Imprint:	Singapore, Singapore : Springer, [2021]
Description:	1 online resource (vi, 304 pages) : illustrations (some color)
Language:	English
Series:	Blockchain technologies Blockchain technologies (Series)
Subject:	Finance -- Technological innovations. Artificial intelligence. Big data. Blockchains (Databases) Artificial intelligence. Big data. Blockchains (Databases) Finance -- Technological innovations. Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12612209

Hidden Bibliographic Details
Other authors / contributors:	Choi, Paul Moon Sub, 1976- editor. Huang, Seth H., editor.
ISBN:	9789813361379 9813361379 9813361360 9789813361362
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references. Description based on online resource; title from digital title page (viewed on May 6, 2021).
Summary:	This book introduces readers to recent advancements in financial technologies. The contents cover some of the state-of-the-art fields in financial technology, practice, and research associated with artificial intelligence, big data, and blockchain all of which are transforming the nature of how products and services are designed and delivered, making less adaptable institutions fast become obsolete. The book provides the fundamental framework, research insights, and empirical evidence in the efficacy of these new technologies, employing practical and academic approaches to help professionals and academics reach innovative solutions and grow competitive strengths.
Other form:	Print version: Choi, Paul Moon Sub Fintech with Artificial Intelligence, Big Data, and Blockchain Singapore : Springer Singapore Pte. Limited,c2021 9789813361362
Standard no.:	10.1007/978-981-33-6137-9

Table of Contents:

1. Blockchain, Cryptocurrency, and Artificial Intelligence in Finance
2. Alternative Data, Big Data, and Applications to Finance
3. Application of Big Data with Financial Technology in Financial Services
4. Using Machine Learning to Predict the Defaults of Credit Card Clients
5. Intelligence and Advanced Time Series Classification: Residual Attention Net for Cross-Domain Modeling
6. Generating Synthetic Sequential Data for Enhanced Model Training Through Attention: A Generative Adversarial Net Framework.

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