Fintech with Artificial Intelligence, Big Data, and Blockchain

1. Auflage 2021
ISBN: 978-981-336-139-3
Verlag: Springer Nature Singapore

Buch, Englisch, 304 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 476 g

Reihe: Blockchain Technologies

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Huang, Seth H.

Choi, Paul Moon Sub

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Angewandte Informatik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

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. Arti?cial Intelligence and Advanced Time Series Classi?cation: Residual Attention Net for Cross-Domain Modeling.- 6. Generating Synthetic Sequential Data for Enhanced Model Training Through Attention: A Generative Adversarial Net Framework.

Über Autor(innen)

Dr. Paul Moon Sub Choi: Dr. Choi has served on the business faculty of Ewha (Associate Dean and Associate Professor of Finance), Cornell (Fulbright Visiting Scholar), and the State University of New York at Binghamton (lecturer). He earned a Ph.D. with a financial economics concentration and an A.M. in statistics from Cornell and Harvard Universities, respectively. He was an undergraduate economics major at Yonsei University before joining Deutsche Bank in equity research on the Korean technology industry. He has published numerous peer-reviewed research articles in leading journals and presented papers in some of the most prestigious conferences in finance and economics. His recent research areas include distributed ledger technology (blockchain), artificial intelligence, financial technology, etc. He is an advisor at various technology-based start-ups, including OrganicSmart, Lozi, Squared-S Artificial Intelligence, etc.

Dr. Seth H. Huang: Dr. Huang has over ten yearsof artificial intelligence (A.I.) research and financial management experiences developing large-scale fintech applications for hedging, global sentiment analysis, and risk management. Most recently, he was Director of A.I. Applications Research Center in Guangdong, China. He occasionally served on the business faculty at the State University of New York-Korea (Adjunct Professor of Finance), the Hong Kong University of Science and Technology (Adjunct Associate Professor of Finance), Ewha Womans University (Assistant Professor of Finance), and Soochow University (lecturer) designing and teaching fintech, A.I., and finance courses. He has vast cross-industry application expertise with 6 patents on A.I. predictive systems and is the founder of a quantitative trading operation, Aris Intelligence Corporation, based in New York. He earned his Ph.D. with a financial economics concentration from Cornell University.