John Wiley & Sons Artificial Intelligence for Asset Management and Investment Cover Make AI technology the backbone of your organization to compete in the Fintech era The rise of arti.. Product #: 978-1-119-60182-1 Regular price: $42.90 $42.90 Auf Lager

Artificial Intelligence for Asset Management and Investment

A Strategic Perspective

Naqvi, Al

Wiley Finance Editions


1. Auflage April 2021
320 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-60182-1
John Wiley & Sons

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Make AI technology the backbone of your organization to compete in the Fintech era

The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond.

No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you'll be able to build an asset management firm from the ground up--or revolutionize your existing firm--using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren't integrating AI in the strategic DNA of your firm, you're at risk of being left behind.
* See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework
* Learn how to build AI into your organization to remain competitive in the world of Fintech
* Go beyond siloed AI implementations to reap even greater benefits
* Understand and overcome the governance and leadership challenges inherent in AI strategy

Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations.

Preface xv

Acknowledgments xxi

Chapter 1: AI in Investment Management 1

What about AI Suppliers? 5

Listening without Judging 6

The Four Stages of AI in Investments 9

The Core Model of AIAI 14

Your Journey through This Book 16

How to Read and Apply this Book? 16

References 17

Chapter 2: AI and Business Strategy 19

Why Strategy? The Red Button 19

AI--a Revolution of its Own 21

Intelligence as a Competitive Advantage 22

Intelligence as a Competitive Advantage and Various Strategy Schools 23

The Intelligence School 25

Intelligence and Actions 26

Actions 27

Automation 28

Intelligence Action Chain and Sequence 28

Enterprise Software 29

Data 29

Competitive Advantage 30

Business Capabilities 31

Chapter 3: Design 35

Who Is Responsible for Design? 36

Introduction to Design 36

AI as a Competitive Advantage 38

The Ten Elements of Design 40

1. Design Your Business Model 41

2. Set Goals for the Entire Firm 44

3. Specify Objectives for Automation and Intelligence 45

4. Design Work Task Frames Based on Human-Computer Interaction 45

5. Perform a DTC (Do, Think, Create) Analysis 46

6. Create a SADAL Framework 47

7. Deploy a Feedback System and Define Performance Measures 49

8. Determine the Business Case or Value 49

9. Analyze Risks 50

10. Develop a Governance Plan 50

Some Additional Ideas about Designing Intellectualization 50

Summary of the Design Process 51

References 52

Chapter 4: Data 53

Who Is Responsible for the Data Capability? 53

Data and Machine Learning 55

Raw Data 55

Structured vs. Unstructured Data 56

Data Used in Investments 57

Data Management Function for the AI Era 58

Step 1: Data Needs Assessment (DNA) 59

Step 2: Perform Strategic Data Planning 59

Step 3: Know the Sensors and Sources (Identify Gaps) 61

Step 4: Procure and Understand the Supply Base 61

Step 5: Understand the Data Type (Signals) 62

Step 6: Organize Data for Usability 62

Step 7: Architect Data 63

Step 8: Ensure Data Quality 63

Step 9: Data Storage and Warehousing 63

Step 10: Excel in Data Security and Privacy 63

Step 11: Implement Data for AI 64

Step 12: Provide Investment Specialization 65

About Legacy Data Management 66

References 67

Chapter 5: Model Development 69

Who Is Responsible? 69

High-Level Process 70

Models 73

The Power of Patterns 74

Techniques of Learning 75

What Is Machine Learning? 76

Scientific Process on Steroids 79

The Learning Machines 79

Algorithms 80

Supervised Learning 82

Supervised: Classification 85

Classification: Random Forest 86

Classification: Using Mathematical Functions 87

Classification: Simple Linear Classifier 88

Supervised: Support Vector Machine 91

Classification: Naive Bayes 94

Classification: Bayesian Belief Networks 95

Classification: k-Nearest Neighbor 95

Supervised: Regression 96

Supervised: Multidimensional Regression 99

Unsupervised Learning 100

Neural Networks 103

Reinforcement Learning 106

References 107

Chapter 6: Evaluation 109

Who Performs the Evaluation? 109

Problems 111

Making the Model Work 111

Overfitting and Underfitting 113

Scale and Machine Learning 113

New Methods 114

Bias and Variance 115

Backtesting 116

Backtesting Protocol 119

References 121

Chapter 7: Deployment 123

Reference Architecture 127

The Reference Architecture and Hardware 130

References 131

Chapter 8: Performance 133

Who Is Responsible for Performance? 134

What Are the Work Processes of Performance? 134

Business Performance 136

Technological Performance 138

References 141

Chapter 9: A New Beginning 143

Building an Investment Management Firm Around Artificial Intelligence? 144

The Fallacy of Going Digital 145

Why Build Your Firm Around AI? 148

You Must Rely on Your Own Capabilities 149

What Is Asset Science? 150

A Healthy Cycle 154

The Tool Set 155

This Is Not Just Automation 156

References 157

Chapter 10: Customer Experience Science 159

Customer Experience 159

Value, Strength, and Duration of Relationship 160

Understanding Customers: Empathy for CX 161

Steps to Become an Empathetic Asset Management Firm 162

Know Your Empmeter 162

Expand Empathy Awareness and Understanding 163

Incorporate into Products and Services 163

What Is Automated Empathy and Compassion (AEC)? 163

Incorporating AEC Marketing 165

References 168

Chapter 11: Marketing Science 171

Who Undertakes This Responsibility? 171

How to Apply AI for Marketing 172

Begin with Assessment 172

Know Your Data 174

The AI Plan for Asset Management Marketing 176

Perform Strategic Planning 176

Manage Product Portfolio with AI 179

Transform Your Communications 180

Build Relationships 181

Execute with Excellence 181

References 182

Chapter 12: Land that Institutional Investor with AI 183

Who Is Responsible for IRMS Automation? 183

Is IRMS Your CRM System? 184

Know Thyself: Automated Self-Discovery 184

Automated Asset Class Analysis 185

Automated Institutional Analysis 185

Automated Structure and Terms Analysis 186

Automated Fee Analysis 186

Automated Communications 186

Unleash the Power of Knowing 188

Chapter 13: Sales Science 189

What Is Sales Science? 189

Who Is Responsible for Implementing Sales Science? 190

Are You Driving This in Sales? 190

How to Build Your AI-Based Sales System 193

References 195

Chapter 14: Investment: Managing the Returns Loop 197

Who Is Responsible for Investment Management? 197

How to Approach Building the New-Era Investment Function? 198

The Core Tool Set 204

What Will Be the Function of Your Investment Lab? 206

Make the Decisions 206

A New World 207

The (Unnecessary) Debate 208

More Behaviors 208

Research and Investment Strategy 209

Portfolio 210

Performance 210

References 210

Chapter 15: Regulatory Compliance and Operations 213

Who Is Responsible? 213

Regulatory Compliance 213

Why Intelligent Automation? 214

Have You Scoped Out What to Do? 215

How to Do It? 215

How to Use Technology for GIPS Implementation? 217

Back and Middle Office 219

Chapter 16: Supply Chain Science 221

Who Is Responsible for Supply Chain Science? 221

How to Think about Supply Chains 222

References 225

Chapter 17: Corporate Social Responsibility 227

CSR Woes: Can Processes Explain Them? 227

What Are the Criticisms of CSR? 228

Measurement Issues 228

Behavioral and Role Issues 230

Strategic and Organizational Issues 230

How to Apply AI in CSR? 231

CSR Must Not Be Forgotten 232

ESG Investment 232

How Can AI Help? 234

You Must Avoid These Mistakes 236

Summary Steps 236

References 237

Chapter 18: AI Organization and Project Management 241

The New Asset Management Organization 241

Why a CAIO/COO Role? 243

What Is Changing? 244

How to Get There? 244

Issues of the New Organization 246

Change Management 248

Managing AI Projects 249

References 250

Chapter 19: Governance and Ethics 251

Corporate Governance with AI 251

Governance of AI 257

Framing the Ethical Problems from a Pragmatic Viewpoint 261

Some Obvious Ethical Issues 262

Humans and AI 262

Ethics Charter 263

References 264

Chapter 20: Adaptation and Emergence 267

The Revolution Is Real 268

Complex Adaptive Systems 270

Our Coronavirus Meltdown Prediction 271

Index 273
AL NAQVI is the CEO of the American Institute of Artificial Intelligence, where he designs and develops machine learning based finance products, teaches classes on applied AI, deep learning, and cognitive transformation, and leads the company strategy. He studies the application of deep learning to financial engineering, investment, and asset management. He is also the author of Artificial Intelligence for Audit, Forensic Accounting, and Valuation (Wiley).

A. Naqvi, American Institute of Artificial Intelligence