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Fintech with Artificial Intelligence, Big Data, and Blockchain / edited by Paul Moon Sub Choi, Seth H. Huang.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Blockchain Technologies, 2661-8346
- Language:
- English
- Subjects (All):
- Cooperating objects (Computer systems).
- Social sciences-Mathematics.
- Blockchains (Databases).
- Internet of things.
- Big data.
- Computational intelligence.
- Cyber-Physical Systems.
- Mathematics in Business, Economics and Finance.
- Blockchain.
- Internet of Things.
- Big Data.
- Computational Intelligence.
- Local Subjects:
- Cyber-Physical Systems.
- Mathematics in Business, Economics and Finance.
- Blockchain.
- Internet of Things.
- Big Data.
- Computational Intelligence.
- Physical Description:
- 1 online resource (VI, 304 pages) : 66 illustrations, 49 illustrations in color.
- Edition:
- 1st ed. 2021.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2021.
- System Details:
- text file PDF
- 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.
- 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. Artificial 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-981-33-6137-9
- 9789813361379
- Access Restriction:
- Restricted for use by site license.
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