Artificial Intelligence and Big Data for Financial Risk Management

Intelligent Applications
1. Auflage 2022
ISBN: 978-0-367-70056-0
Verlag: Routledge

Buch, Englisch, 246 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 540 g

Reihe: Banking, Money and International Finance

This book presents a collection of high-quality contributions on the state-of-the-art in Artificial Intelligence and Big Data analysis as it relates to financial risk management applications. It brings together, in one place, the latest thinking on an emerging topic and includes principles, reviews, examples, and research directions. The book presents numerous specific use-cases throughout, showing practical applications of the concepts discussed. It looks at technologies such as eye movement analysis, data mining or mobile apps and examines how these technologies are applied by financial institutions, and how this affects both the institutions and the market. This work introduces students and aspiring practitioners to the subject of risk management in a structured manner. It is primarily aimed at researchers and students in finance and intelligent big data applications, such as intelligent information systems, smart economics and finance applications, and the internet of things in a marketing environment.

Zielgruppe

Postgraduate and Undergraduate

Autoren/Hrsg.

Metawa, Noura

Hassan, M. Kabir

Metawa, Saad

Fachgebiete

• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Wirtschaftswissenschaften Betriebswirtschaft Management Risikomanagement
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein

Inhaltsverzeichnis

1: Grey Model as a tool in dynamic portfolio selection: simple applications 2: Predicting Financial Statement Fraud Using Artificial Neural Networks 3: Bank Network Credit Model and Risk Management System Based on Big Data Technology 4: Deep Learning in Detecting Financial Statement Fraud: An Application of Deep Neural Network (Dnn) 5: Predicting Stock Return Risk and Volatility Using Neural Network: The case of the Egyptian Stock Exchange 6: Operation Analysis of Financial Sharing Center Based On Big Data Sharing Technology: Taking SF Express as an Example 7: Optimization Algorithms for Multiple-Asset Portfolios with Machine Learning Techniques: Theoretical Foundations of Optimum and Coherent Economic Capital Structures 8: Random Forest and Grey methodology in dynamic portfolio selection 9: The Role of Blockchain in Financial Applications: Architecture, Benefit, and Challenges 10: Using Computer Block Chain Technology to Analyze the Development Trend of China's Modern Financial Industry 11: Financial Efficiency Differentiation Based on Data Quantitative Analysis under Big Data Technology 12: Optimization Algorithms for Multiple-Asset Portfolios with Machine Learning Techniques: Practical Applications with Forecasting of Optimum and Coherent Economic Capital Structures 13: An Overview of Neural Network in Financial Risk Management

Über Autor(innen)

Noura Metawa is Assistant Professor of Finance at the Faculty of Commerce, Mansoura University, Egypt, and at the College of Business Administration, University of Sharjah, Sharjah, UAE.

M. Kabir Hassan is Professor of Finance in the Department of Economics and Finance at the University of New Orleans, Louisiana, USA.

Saad Metawa is Professor of Finance at the Faculty of Commerce, Mansoura University, Dakahliya, Egypt.