Applications and Trends in Fintech I: Governance, AI, and Blockchain Design Thinking (Global Fintech Institute - World Scientific Series on Fintech Book 4)

"This book is the first part of Applications and Trends in Fintech, which serves as a comprehensive guide to the advanced topics in fintech, including the deep learning and natural language processing algorithms, blockchain design thinking, token economics, cybersecurity, cloud computing and quantum computing, compliance and risk management, and global fintech trends. Readers will gain knowledge about the applications of fintech in finance and its latest developments as well as trends. This fourth volume covers the foundation of fintech, which is ethics and governance, and advanced topics in two of the most important technologies, artificial intelligence and blockchain. Together with the second part in applications and trends (fifth volume), these two books will deepen readers' understanding of the fintech fundamentals covered in previous volumes through various applications and analysis of impacts and trends"-- Provided by publisher Contents Preface About the Editors Part A: Ethics and Governance Chapter 1 Ethical Framework and Principles 1.1 Introduction 1.2 The Ethical Framework 1.2.1 Introduction 1.3 Ethical Principles — CFtP Code of Ethics 1.3.1 Introduction 1.4 Professional Practice — CFtP Standard of Practice 1.4.1 Introduction References/Further Readings 1.5 Sample Questions Solutions Chapter 2 Governance and Regulation 2.1 Introduction 2.2 Governance and Regulatory Framework 2.3 Governance and Regulatory Principles 2.4 Areas of Governance and Regulation 2.4.1 Technology 2.4.2 Artificial Intelligence 2.4.3 Blockchain 2.4.4 Cybersecurity 2.4.5 Data Protection/Privacy 2.4.6 Finance 2.4.7 Anti-Money Laundering/Combating the Financing of Terrorism (AML/CFT) 2.4.8 Corporate Governance 2.4.9 Environmental, Social and Governance (ESG) 2.4.10 Sustainability and Green Finance References/Further Readings 2.5 Sample Questions Solutions Part B: Artificial Intelligence, Machine Learning, and Deep Learning in Finance Chapter 3 Machine Learning 3.1 Bias−Variance Tradeoffs 3.1.1 Generalization 3.1.2 Complexity (Flexibility) 3.1.3 Bias−Variance Tradeoff 3.2 Subset Selection and Shrinkage Methods 3.2.1 Subset Selection 3.2.2 The Best Subset Selection 3.2.3 The Evaluation Criteria 3.2.4 Shrinkage Methods 3.2.5 Ridge Regression 3.2.6 Least Absolute Shrinkage and Selection Operator 3.3 Principal Component Regression 3.3.1 Principal Component Analysis 3.3.2 Principal Component Regression 3.4 Advanced ML Models in Classification 3.4.1 Linear Discriminant Analysis 3.4.2 Quadratic Discriminant Analysis 3.4.3 Comparison of Different Classification Methods 3.5 Cross-Validation 3.5.1 The Validation Set Approach 3.5.2 Leave-One-Out Cross-Validation 3.5.3 K-Fold Cross-Validation References/Further Readings 3.6 Sample Questions Solutions Chapter 4 Deep Learning 4.1 Artificial Neural Networks (ANN)/Multilayer Perceptron (MLP) 4.1.1 What is Deep Learning? 4.2 Convolutional Neural Network (CNN) 4.2.1 What CNN Can Do? 4.2.2 The Working Mechanism of CNN 4.3 Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) 4.3.1 Recurrent Neural Network (RNN) 4.4 Generative Adversarial Networks (GAN) 4.4.1 The Generator 4.4.2 The Discriminator 4.5 Deep Learning Applications in Finance References/Further Readings 4.6 Sample Questions Solutions Chapter 5 Natural Language Processing (NLP) 5.1 Natural Language Processing (NLP) 5.1.1 What is NLP? 5.1.2 NLP Applications 5.1.3 NLP Techniques 5.1.4 Major Challenges of NLP References/Further Readings 5.2 Sample Questions Solutions Part C: Blockchain Programming and Design Thinking Chapter 6 Blockchain and Digital Currency Advanced 6.1 Blockchain and DLT Advanced 6.1.1 Cryptography 6.1.2 Consensus 6.2 Cryptocurrency Advanced 6.2.1 Bitcoin Script Language 6.3 Ethereum and Smart Contracts 6.3.1 Introduction 6.4 New Forms of Digital Currency 6.4.1 Central Bank Digital Currency 6.4.2 Stablecoins 6.4.3 Summary 6.5 Privacy and Security 6.5.1 Security of Blockchain Networks 6.5.2 Ensuring Privacy of Users on Blockchain Networks 6.6 Scalability 6.6.1 Authentication and Non-Repudiation 6.6.2 Scalability and Performance 6.6.3 Types of Fault 6.6.4 Synchrony 6.7 Types of Blockchains 6.7.1 Introduction References/Further Readings 6.8 Sample Questions Solutions Chapter 7 Token Economics, Blockchain Ecosystem 7.1 Token Economy and Valuation 7.1.1 Token Economy 7.1.2 Tokens and Design Thinking 7.1.3 Good and Bad Examples 7.1.4 Token Valuation 7.1.5 Future Considerations 7.2 Blockchain Design Thinking 7.2.1 Blockchain Architecture and Design 7.2.2 Planning Your Blockchain 7.2.3 Choose Your Technology 7.2.4 Designing Token Economics 7.2.5 Putting it Together 7.2.6 Token Design and Game Theory References/Further Readings 7.3 Sample Questions Solutions

This book is the first part of Applications and Trends in Fintech, which serves as a comprehensive guide to the advanced topics in fintech, including the deep learning and natural language processing algorithms, blockchain design thinking, token economics, cybersecurity, cloud computing and quantum computing, compliance and risk management, and global fintech trends. Readers will gain knowledge about the applications of fintech in finance and its latest developments as well as trends.

This fourth volume covers the foundation of fintech, which is ethics and governance, and advanced topics in two of the most important technologies, artificial intelligence and blockchain. Together with the second part in applications and trends (fifth volume), these two books will deepen readers' understanding of the fintech fundamentals covered in previous volumes through various applications and analysis of impacts and trends.

Contents:

• Ethics and Governance:
  • Ethical Framework and Principles
  • Governance and Regulation
• Artificial Intelligence, Machine Learning, and Deep Learning in Finance:
  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
• Blockchain Programming and Design Thinking:
  • Blockchain and Digital Currency Advanced
  • Token Economics, Blockchain Ecosystem, and Design Thinking

Readership: Professionals, researchers, and advanced undergraduate and graduate students in the field of financial technology, data science, finance, financial innovation, statistics, and technology.

Key Features:

• Comprehensive guide to fintech applications and future trends
• Introduction to the advanced topics related to ethics, governance, artificial intelligence, machine learning, deep learning, blockchain, token economics, and cryptocurrency investment
• Easy to understand
• Useful handbook with learning outcomes, main terms, lessons learnt and sample questions provided

This book is the first part of Applications and Trends in Fintech, which serves as a comprehensive guide to the advanced topics in fintech, including the deep learning and natural language processing algorithms, blockchain design thinking, token economics, cybersecurity, cloud computing and quantum computing, compliance and risk management, and global fintech trends. Readers will gain knowledge about the applications of fintech in finance and its latest developments as well as trends.This fourth volume covers the foundation of fintech, which is ethics and governance, and advanced topics in two of the most important technologies, artificial intelligence and blockchain. Together with the second part in applications and trends (fifth volume), these two books will deepen readers' understanding of the fintech fundamentals covered in previous volumes through various applications and analysis of impacts and trends. Bundle set: Global Fintech Institute-Chartered Fintech Professional Set I